Developing an early warning system for congestive heart failure during a Bayesian reasoning network

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Developing an Early Warning System for Congestive Heart

Failure Using a Bayesian Reasoning Network

by

Joseph C. C. Su

Submitted to the Department of Mechanical Engineering

in partial fulfillment of the requirements for the degree of

Master of Science in Mechanical Engineering

at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

September 2001

@

Massachusetts Institute of Technology 2001. All rights reserved.

A uthor ...

i/-I

.. . . ... ...

Department of MechanicalEngineering

August 23, 2001

Certified by...

-Kent Larson

Principal Research Scientist, MIT Department of Architecture

Thesis Supervisor

R ead by ...

... .

Stephen Intille

Research Scientist, MIT Department f Architecture

Irhesis Reader

... . . . . .

Sanjay E. Sarma

Assistant Professor of Mechanical Engineering

Thesis Reader

Accepted by ...

Chairman, Departmental Committee on Graduat

tudents

BARKER

MASSACHUSETTS INSTITUTE

OF TECHNOLOGY

DEC 1 0 2001

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Developing an Early Warning System for Congestive Heart Failure Using

a Bayesian Reasoning Network

by Joseph C. C. Su

Submitted to the Department of Mechanical Engineering on August 23, 2001, in partial fulfillment of the

requirements for the degree of

Master of Science in Mechanical Engineering

Abstract

We propose a framework for the development of a home-based early warning system for congestive heart failure (CHF). The system contains a diagnostic Bayesian reasoning net-work that uses probabilistic reasoning and evidence to arrive at a judgement. The netnet-work combines both simulated biometric data (daily weight and blood pressure readings) and actual position of the user to dynamically select context-specific health questions. These questions are presented to the user via a wireless personal digital assistant (PDA). Answers to questions and biometric data are used by a Bayesian network to dynamically calculate a probability that the user is at risk for CHF. We argue that current biometric sensing tech-nology alone is inadequate to accurately establish a CHF risk factor; a Bayesian network that incorporates both biometic information and answers to context-specific questions may be a more accurate predictor.

Thesis Supervisor: Kent Larson

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List of Figures

3-1 Differences and Similarities Between an Expert Diagnostic and Early

Warn-ing Systems in the CHF Disease Domain . . . . 13

3-2 A Control Paradigm for an Early Warning System . . . . 14

4-1 Bayesian reasoning network for CHF . . . . 21

4-2 Causation Diagram for a Diagnostic Network . . . . 21

4-3 Causation Diagram for CHF . . . . 22

5-1 Flow Diagram of a Querying Process . . . . 24

5-2 Setting Observation in a CHF Network . . . . 28

6-1 Question Displayed on a Palm Pilot Vx . . . . 34

6-2 Display of Both Contextual and Biometric Data . . . . 37

6-3 Dynamic Highlighting of Q-Orthopnea Question Variable in a CHF Network 38 6-4 Display of Both Contextual and Biometric Data as A Slider Bar Moves . . 39

8-1 A Feedback Closed-Loop Design for an EWS . . . . 42

B-1 Two-Node Bayesian Network . . . . 50

C-1 Question Form at . . . . 52

D-i Bayesian Reasoning Network for CHF Using JavaBayes Package . . . . 55

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List of Tables

B.1 Discrete probabilities for CHF-HTN Variables ... 50

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Chapter 1 Introduction

United States faces a considerable challenge in providing healthcare for its people in the coming years. A stream of expensive medical innovations and procedures have exacerbated the dilemma of providing high-quality care at a reasonable cost. While innovative diagnostic tools and treatments have improved healthcare by providing less invasive procedures and promising more effective outcomes, healthcare spending due to the number of patients using the technology is on the rise [49]. Experts predict that by 2025, 5.3% of the gross domestic product will be spent in Medicare, compared with 2.7% in 1998 [47, 46]. Per capita spending on healthcare reached $3,000 in 2000, making United States the number one nation in healthcare spending [45]. In nursing home and home care costs alone, the

U.S. government spent $115 billion in 1997 [48]. This figure will continue to rise due largely

to hospitalization of elderly in the final decade of life.

Vigorous efforts exist to reduce the escalating costs of care. Cutbacks in both the number of patient treatments and length of clinic visits, allowed by managed care organizations (HMO's), result in patients being discharged prematurely in the treatment cycle. Home healthcare providers (HHP's) are attempting to provide more affordable home care services

[1]. In 1995 alone, there were more than 17,500 HHPs that delivered services to seven million patients. Providing medical education to patients and instructing them on how to self-administer certain medical procedures is common practice among HHP's and HMO's. Many cost-saving mechanisms have also been proposed, such as liberalization of Medicare's treatment benefits to include preventative home care [1].

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na-tional health programs have proposed a slew of clinical treatment plans for individuals with various diseases, to counteract cost and enhance quality of life [23, 2]. These plans include encouraging patients to take control of their health by pursuing more rigorous health ex-ercises, eating healthier diet, carefully self-monitoring changes in their physiological states, enrolling in commercial disease-management programs, etc. One program with 29 patients reduced the average hospital readmission rate from 1.5% to 0.13% per year, while the actual emergency room visits from 17% to 3% a year [56]. In another program with 238 patients, there was an observed 69% decrease in hospital visits, saving each patient by as much as

$8,000 healthcare cost a year [52].

Another trend in healthcare is a shift of patient care and responsibility out of the hos-pital into home. One trend in healthcare is increasing reliance on the use of computerized equipment, and application of technological advances and medical innovations for both diag-nosis and treatment [44, 49]. We see more and more prevention-based disease-management programs come into play in the new economy era laced with increasing healthcare expen-diture. Some providers employ live operators and automated mechanism to talk to and collect health information from their patients via telephone-linked care (TLC) programs, and other providers employ disease-management programs that involve the use of biomet-ric devices to non-invasively acquire patients' vital signs and monitor them telemetbiomet-rically

[51, 52, 50]. Many cardiac patients are having their vital signs monitored from home as disease-management programs are becoming increasingly available [51, 52]. These programs try to optimize outcomes through supportive technology, such as home-care automation, that links patients to providers. Studies have indicated the efficacy of home monitoring for heart-related diseases to prevent crises from occurring [39, 38]. By enabling health moni-toring automation in the home, a home owner's health status can be continuously assessed and incrementally stored, so that healthcare professionals can be on a constant watch of patient's health to make opportune clinical intervention before a crisis occurs.

Preventative home healthcare is an important solution to reducing healthcare costs, as it promotes early detection of diseases through the patient's self-awareness and the use of improved home care technology that provides more continuous monitoring of people's health. However, effectively increasing a patient's self-awareness of his or her health status is not easy. Studies have shown that more than half of all Americans with chronic disease also do not follow their physician's medication and lifestyle guidance. In one study, 30.6%

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of participants did not adhere to their medication schedules [66]. In another report, it was noted that nine out of ten make mistakes taking their medication, and two-thirds fail to take any or all of their prescriptions [67]. More studies have also shown that moderate exercise per day could result in significant improvement in overall health [63, 64], yet two-thirds of people over 65 do not regularly exercise [65]. A reverse trend in the near future is: as life expectancy for general population increases due to better treatments and preventative medicine, we may still see more costly medical episodes developed over the course of a longer life span for each individual.

We propose a home-based early warning system that aims to prevent or defer the oc-currence of costly medical episodes, by allowing users to be on an active and constant alert of their health status. In the following section, we describe a medical condition known as congestive heart failure (CHF). It has become one of the leading medical conditions that, if detected sooner, could result in tremendous savings in national budget. In Sec. 1.1.1, we describe CHF-related compliance and prevention issues and their impact to improving the prognosis and quality of life.

1.1 _{Congestive Heart Failure: An Example and Overview}

CHF is a serious end-stage of cardiovascular disease, where the heart is unable to pump an

adequate supply of blood to meet the oxygen requirements of the body's organs and tissues. In year 2000 alone, it was estimated that $21 billion dollars were spent either directly or indirectly in treatments for congestive heart failure [23]. CHF is increasing in prevalence, resulting in more hospitalizations and deaths making it a major chronic condition in the United States [25]. It was reported that in 2001 when the study was issued, that there was an estimated 4,700,000 Americans who had been diagnosis congestive heart failure [23].

CHF causes fluid retention. Fluid accumulates in the heart and other parts of the body

such as the lungs and legs. Causes of CHF include cardiovascular problems such as, but not limited to, coronary artery disease (CAD) and myocardial infarction, hypertension, cardiomyopathy, heart arrhythmia, congenital heart defects, and heart valve abnormali-ties. Depending on which side of heart is failing, symptoms sometimes overlap and vary. Symptoms are dyspnea (shortness of breath), orthopnea (difficulty breathing when lying down), edema (swelling of joints, abdomen, liver, spleen, and lungs), weight gain, fatigue

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or weakness, loss of appetite, and nocturia (an increase urination at night) [44].

1.1.1 Congestive Heart Failure: Compliance and Prevention Issues

It has been observed that compliance with a CHF treatment plan and careful monitoring at each hospital visit will improve a patient's prognosis and quality of life [36, 43]. The interval of office visits for 90% of CHF patients is between 2 to 4 months [42]. During this period, there may be occasional cardiac symptoms such as breathlessness, ankle swelling, or even a small body weight gain in the patient. These symptoms may be an indication that the patient's condition is progressively deteriorating and re-hospitalization is required. Rapid deterioration occurs in the case of an acute heart failure. Subtle physical indicators, if any,

must not be neglected by the patient. Ignorance may lead to a rapid deterioration that requires immediate medical attention. It was reported that, in geriatric patients with CHF, hospital re-admission after 3 months was 30% [55]. By increasing patient's self-awareness to detect early symptoms of CHF and the monitoring frequency of patient's biometric data, the rate of re-hospitalization might be reduced. This is because

1. Subtle symptoms might be caught at an early stage of deterioration.

2. Biometric readings taken at a much shorter and frequent interval than 2-4 months might indicate gradual changes in the patient's health.

Emerging downward trends might be detected sooner. Studies have demonstrated a clinical improvement of failure patients participating in a comprehensive home-based heart-failure management program [23, 54].

1.2 Prevention-Based Home Healthcare System

Home healthcare is becoming more mainstream due to its cost-effective, proactive, and pre-ventative nature that replaces the current reactive, episodic, and crisis-driven care delivery model that takes place in a clinical setting. In light of the current healthcare trends in

CHF, this work proposes a framework for the development of a home-based early warning

system for the prevention of CHF. The system might:

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2. Eliminate gaps in care by providing continuous health monitoring during daily living

3. Supplement existing biometric sensing technology by collecting new health data

There are two types of data that can be collected from a home occupant: numerical val-ues from biometric readings and contextual information about the setting in which these readings are taken. Context could involve the physical or emotional state of the individual, where the user was at what time, how the user felt, and if the user has been exhibiting any symptoms in addition to the biometric information collected.

In this work, the system actively queries the user to tag biometric readings with con-textual information about the user's situation. This information is used by the system to dynamically establish a medical risk factor for the individual, allowing for early detection of CHF. If fully developed, this early warning system might become a vital component of proactive and cost-effective home medical care.

1.2.1 Data Collection

Continuous and Periodic Biometric Readings from the User. The home is a good place for collecting a series of biometric readings for the prevention of CHF: weight, blood pressure, oxygen saturation rate, pulse and heart rate, and glucose level. These

numerical readings indicate certain physical conditions about the individual, but the readings alone may not confirm that an individual is experiencing CHF-related phys-ical symptoms, such as exertional dyspnea (difficulty breathing upon the exertion of force), orthopnea (difficulty breathing laying down), lower leg edema (swelling), or angina pectoris (chest pain).

Continuous Contextual Information (when, where, and what) from the User. Home

environment is a place where we can find out a lot about its occupants, including the context surrounding their activities of daily living, i.e., eating habits and gait pat-terns. These contextual information contain essential yet often neglected subtleties where we may infer gradual health changes from an individual, catching latent health maladies before they become pronounced. If subtle qualitative changes in symptoms can be detected early on, we may prevent or defer the onset of rapid deterioration from occurring. If the patient's condition progresses to the level of a serious ailment, this

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contextual history can provide physician with a much richer insight into the patient's health condition.

1.3 Thesis Outline

This thesis describes the framework for a demonstration system: an adaptive home-based early warning system for CHF. The approach uses data collected from the home occupant in the context of daily living.

Chapter 2 discusses related work in the area of preventative medicine. Some of these works include disease-management programs involving telephonic health monitoring in a home setting, and diagnostic system development in a clinical setting. Chapter 3 gives an overview of the CHF system framework. Chapter 4 describes the steps taken to construct a Bayesian reasoning network for an early warning system. Chapter 5 details the system's querying mechanism, in which health questions are generated by the system and sent to the user, and responses to the questions are received from the user via a personal digital assistant

(PDA). Chapter 6 puts details from both Chap. 4 and Chap. 5 in perspective, describing

the system's decision-making process and illustrating graphically how both contextual and biometric information are used in reaching a diagnosis. Chapter 8 makes recommendations for future modifications and improvements of the system.

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Chapter 2 Related Prior Work

There has been an emergence of medical innovations that take advantage of telemetric and wireless applications in communication to monitor health or activity levels of an individual

[50, 51, 59, 60, 62]. Most of these disease management programs utilize telemetric devices to

measure the patient's bio-data and transmit the information back to a healthcare provider over the phone. Some employ live operators or an automatic transmission mechanism to phone patients and solicit health information directly from them [57, 38]. Some others even provide electronic portable systems that have messaging capabilities, sending medication reminders and health questions to the patient [52, 58]. However, few of these commercial innovations actively seek to apply artificial intelligence (AI) reasoning for medical diagnosis.

A comprehensive disease-management program or treatment plan might include patient

education, patient self-assessment, collection of patient data and access to the data, and methods of measuring treatment compliance and for making the data available. For patient data acquisition, numerous commercialized biometric sensors (such as blood pressure meter, weight scale, etc) exist for home-based health monitoring. These systems, however, do not establish the context in which the data is acquired, such as where the user was, how the user felt, or if the user had been exhibiting any symptoms in addition to the biometric variables collected. In a clinical setting, a doctor can ask the patient many questions to gather information about context. In the absence of a doctor at home, a homecare system that can ask questions and gather responses to those questions from an individual can provide context for and supplement biometric data.

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medicine.

2.0.1 Disease Management Programs

Disease management programs attempt to identify patients in need of treatment, intervene with specific programs of care, and measure outcomes. These programs focus resources on high-risk or common disorders, and have the potential to improve treatment and reduce clinical costs for patients with asthma, depression, diabetes, and congestive heart failure

[71].

Telephonic Monitoring Systems

Researchers have been conducting pioneering research in telephonic health monitoring over the years [40, 41, 57]. In a telephonic protocol, nurses or medical professionals make fre-quent calls to patients soliciting their health status and recording down the information electronically. Computerized telephonic systems have been used to monitor the health of large volume of patients [38]. Health organizations also have telephone-linked care programs

(TLC) to monitor the health of their patients [68, 69].

Telemetric Monitoring Systems with Biometric Devices

Telemetric health monitoring systems have been in use since the late 1980's [57]. Recently, these systems have been automated to collect bio-data from the user at home [41, 40]. Examples are automatic monitoring of blood pressure in patients' home with weekly reports to doctors [59, 51, 62], automatic tracking of the user's activities and even sleep patterns [61], and various systems that come equipped with biometric devices for CHF-related or other measurements [59, 51]. Still there are others that take a step further to enable interactive medicine between a portable telemetric display and the patient. One of the examples is Health Hero Network's Health Buddy, which sends patients reminders and provides them with feedback on their progress and tips for managing their disease more effectively [52]. Another example is InforMedix's Med eMonitor that comes with a medication dispensing mechanism and electronic messaging system similar to that of Health Buddy's [58]. In studies conducted with Catholic Healthcare West CHF Program and MDS Pharma Services, the results indicated that Health Buddy was a cost-saving tool that had perceived value to patients and providers [53].

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2.0.2 Medical Diagnostic Systems

An expert system represents a knowledge base of information and searches for patterns in it,

modeling how a human expert analyzes a particular situation by applying rules to the facts or comparing the current case with similar cases. Expert systems utilize different types of reasoning methods such as fuzzy-logic, neural networks, and Bayesian networks. The most common expert system is rule-based, containing a knowledge base and an inference engine (i.e., routing mechanism) which analyzes fact patterns and matches the applicable rules. Fact patterns are analyzed until either the goal succeeds or all of the rules are processed and the goal fails.

Medical diagnosis is an application area that utilizes reasoning in artificial intelligence (AI) [7, 9]. A medical diagnostic model is generated by acquiring evidence such as both symptoms and signs, determining a set of faults or causes associated with the evidence. Diagnosis is determining the cause of a pathological state [10]. Whenever new information is obtained, the system generates hypothesis of the patient's current condition given the model. Many medical expert systems, or diagnostic programs, employ reasoning to make prognosis and diagnosis of medical disorders, and identify an appropriate course of treatment for the patients. One such reasoning is a rule-based reasoning, with knowledge catalogued in the form of IF and THEN rules used in chains of deduction to reach a conclusion [72]. However rule-based programs suffer a serious drawback, namely, they do not embody a model of clinical reasoning or disease, leading to unfavorable interactions between rules and thus to serious degradation of program performance [72, 73, 74]. Another reasoning is fuzzy logic, where truth values become real values in the closed interval [0 ... 1]. The rules are designed to return vague values like "closer" or "very tall". This approach is used only when a system is difficult to model exactly and an inexact model is available, or when ambiguity or vagueness is common.

Numerous medical diagnostic programs have employed an AI approach called Bayesian reasoning [17, 11, 12, 19, 18, 13, 20]. Two of these examples are the Pathfinder system used for lymph-node diseases [34], and Long's Heart Failure Program, which simulates in great detail the relationships among certain physiological, etiological, gravity, and patho-physiological states affected by both disease process and therapy [26]. Long's program employs a so-called pseudo-Bayesian reasoning, which incorporates the severity of disease

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states and the temporal relations of causality to determine the mechanisms that produce the evidence, as well as to determine the primary disease causes [10]. As disease domains in-crease in complexity, more detailed data such as the level of disease manifestations, severity of disease, and types of complications, are needed in these programs. The Bayesian-based reasoning approach has becoming increasingly popular because of its adaptive variety and powerful learning component involving Bayesian network, which uses techniques of proba-bility theory to reason under conditions of uncertainty. Unlike other reasoning approaches, Bayesian networks can explain their reasoning and incorporate probabilistic data from pub-lished literature, and are useful for representing uncertain relationships where statistical data and prior knowledge are available. Furthermore, both probabilistic dependencies and constraints are made explicit in a directed acyclic graph (DAG) of a Bayesian network. Therefore, Bayesian reasoning approach is the most solid and flexible option for the system we develop.

However, one challenge associated with using Bayesian reasoning is to create a network that accurately describes the inter-dependencies of causes and effects in the domain of interest. Much effort and research are needed to build the network, by experts who judge and come up with the probabilities used in it. A Bayesian network used in any medical expert system can be optimized. The process of modifying and optimizing a network involves a lot of expertise, trial and errors, and time. It took a consortium of engineers, scientists, and physicians a total of 44 weeks to build the Bayesian network used in the Pathfinder system [34]. One of the research questions for this thesis work is how to create a Bayesian network that is reduced in complexity and optimized for CHF disease domain. Another question for this work is how to tag biometric data with contextual reliability through the use of network, so that the system can compute a dynamic risk factor for an individual living at home. These questions can have a big impact on the development of a preventative home system, which will be described in the following chapter.

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Chapter 3 System Overview

We test a first step towards developing a knowledge-based early warning system for CHF, making use of Bayesian reasoning. The system makes use of the following components:

1. A Bayesian reasoning network: The system uses a Bayesian network for diagnosis.

Diagnosis in an early warning system establishes a risk factor, predicting how likely that a person is to develop CHF.

2. There are three types of input for the system:

Simulated biometric data and symptoms relevant to CHF A simulated

med-ical history chart was made that includes daily biometric readings and symptoms indicative of a person developing CHF. The history on the chart spans a period of 3 weeks, starting with the first week on the chart when CHF symptoms are almost non-existent or mild and slowly progressive, ending with the last week when CHF starts to acutely develop.

Contextual information (when, where, what) from the user As mentioned

pre-viously, context captures where the user was, what the user was doing, and the time in which the action took place. In this work, contextual information about users are obtained from their responses to health questions that the system gen-erates. A health question is a medically informative question with the following characteristics:

* Presented in a non-intrusive way " Calming, not fear-inducing to the user

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e Non-annoying to the user

An example question from the current system is: "Do you have difficulty breath-ing even after the window is opened?". An answer of "yes" signifies that the user may be experiencing dyspnea, or breathing difficulty, even after the window is opened and more fresh air is in. When more questions are asked and hence more responses gathered, the system gradually becomes more aware of the user's health status.

Context and location/time information The system connects to a set of

track-ing sensors, which detect the user's whereabouts in a room at any given time. The location information is used to select location-specific health questions, de-pending on the user's whereabouts and known positions of objects (e.g., a desk) in the room. The time information is used to select the most appropriate, i.e., temporally relevant, location-specific questions to display on a Palm Pilot Vx.

User responses to health questions Based on the user's biometric and context

history, the system dynamically generates an appropriate set of health questions categorized by the types of locations. The location information, provided by in-house sensors, allows for the generation of the most appropriate location-specific health question at any given time. Questions are sent to the user via a personal digital assistant (PDA).

3. System output-Probability of a CHF risk factor: Using the history of the user's

biometric readings and responses to health questions, the system dynamically com-putes a CHF risk factor for the user. A risk factor is the probability (ranging from 0 to 100%) of a person developing CHF. Based on statistics, i.e., the number of CHF patients versus the total population in the U.S. in 2001, the CHF risk factor for an av-erage American is roughly 0.84%.1 For example, when a noticeable medical condition emerges that warrants an immediate medical attention-such as when the user is, say,

25 times more likely than a normal person to have developed a serious disease-the

system might notify the home occupant of the risk and encourage the occupant to seek a doctor, or notify the family or a doctor directly if appropriate.

'This is the actual number calculated in Appendix B, based on the number of CHF patients and current

U.S. population. In this paper, we have developed a Bayesian inference model that uses 0.96% as a starting

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Fig. 3-1 compares an expert diagnostic and early warning system. In the figure, P(X) signifies the probability of X given known evidence.

Medical Diagnostic System

P(severity, etiology, typ es, Early Warning: % at risk manifestations of CHF)

P(CHI)

Continuous input over time Occasional and more intensive input

Home setting by yourself Clinical setting with doctors

Biometric data, environmental context lab tests, results, signs, symptoms, etc

Figure 3-1: Differences and Similarities Between an Expert Diagnostic and Early Warning Systems in the CHF Disease Domain

An early warning system is essentially an expert diagnostic system, arriving at a clinical conclusion based on accumulating evidence about an individual. An expert diagnostic system is used in a clinical setting, incorporating a sparse set of biometric data, and clinical observations such as symptoms from the patient.

In a clinical environment, a physician gains insight into the patient's ailment by taking measurements and asking the patient a few health questions. Observable signs and symp-toms are further confirmed and validated by the patient's responses to those questions. In clinical terms, symptoms are any abnormal changes in appearance, sensation, or function experienced by a patient that indicate a disease process [44]. Signs, on the other hands, are abnormalities that indicate a disease process, such as a change in appearance, sensation, or function, that is observed by a physician when evaluating a patient [44]. Under a stressful clinical setting, the patient might deny the existence of symptoms to avoid facing the im-plications of a real problem. In other instances, a patient may exaggerate a condition to gain attention from the doctor [441.

The early warning system developed here is intended for use in a home setting. It makes a diagnosis based on a continuous and periodic flow of biometric data, and feedback from an ample amount of subtle contextual information. The system asks a home occupant questions as a doctor might do if the doctor were at home, in order to ascertain clinical information not already encoded or obvious in the patient's biometric data. Whereas weight

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or blood pressure changes might entail signs such as edema or hypertension, as observed by the system, these signs may be further confirmed by symptoms that a home occupant might have and is experiencing. The occupant's responses to dynamically changing and context-related (i.e., location-specific) health questions become symptomatic evidence entering into the system. A question might be "Do you feel winded often after standing at the kitchen counter for more than 5 minutes?". A "yes" response to this question suggests that the occupant may be experiencing dyspnea and this symptom impacts a CHF diagnosis. By and large, the system has to be efficient and acquire as much information or context about the user as possible, without causing the user to turn off the preventative monitor out of annoyance. At the same time, it has to be effective in providing more precise diagnosis at any time.

3.1 Overview of the System Processes

The proposed early warning system can be represented in a control system paradigm de-picted in Fig. 3-2, consisting of a feedback loop that illustrates the question querying mech-anism. A feedback loop exists between the user query and Bayesian network processes.

Response entered Context Location /Bayesian Us er time _Reasoning

Query

.imtre NetworkII Biometnc Data Question generated Prognosis or future occurrence of a disease

Figure 3-2: A Control Paradigm for an Early Warning System

Depending on the user's whereabouts and the user's history of responses, location-specific

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3.2 Scenario

Let's consider a scenario of an early warning system in action. An 83-year-old woman lives in a home by herself. She has had a few symptoms related to CHF, such as edema and hypertension with coronary artery diseases (CAD's). The system developed here aims to diagnose the individual in a home setting. When she enters her bedroom one night after supper, without having any information about her other physiological states, the system believes that she has CHF with a probability of 1.96%. This number might be different than that for the general population (0.86%), because the user has entered into the bedroom and might have done something to offset this number-say, she weighs herself on a bedroom scale after a full supper. While connected to the system, the scale indicates that she has a 4% increase in weight after comparing her average weight from last week. This weight gain changes how an early warning system sees her as a candidate for developing CHF. The system's view changes, however, when more new evidence arrives. The system may ask her a bedroom-specific question: "Did you experience any difficulty breathing today?". If the user's response to this question is "yes", this bit of evidence enters into the system which believes that she has a higher chance of developing CHF.

In summary, generation of context-specific health questions is based on the user's where-abouts, time, and all the other previously entered evidence, such as the user's weight and blood pressure level. The user receives these health questions via a portable electronic de-vice such as PDA. Responses to context-specific questions enter into the system (via the

PDA), becoming additional evidence allowing the system to generate the next set of

ques-tions when appropriate. An early warning system absorbs context via a question querying mechanism that involves asking questions to and receiving responses from the user. Each question is tagged with contextual reliability. Meanwhile, the system periodically receives biometric data from the user. These data become another type of evidence, enabling the system to make more precise assessment of the person's health. In this work, approaches have been taken to apply reasoning that drives the querying mechanism; we do not consider how biometric data can be obtained.

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Chapter 4 Bayesian Reasoning Network for

Congestive Heart Failure

A technique called Bayesian reasoning is often used in disease diagnosis [10]. In this work we

describe the framework for the development of a home-based early warning system, which employs Bayesian inference to predict the likelihood of having congestive heart failure (CHF) in home occupants.

4.1 Bayesian Reasoning Network: An Overview

Bayesian reasoning networks, also called belief networks, knowledge maps, or probabilistic causal networks, have become the most popular methods for describing and reasoning with probabilistic information, using a graphical model that topographically represents prob-abilistic relationships or dependencies among a set of variables [8, 15, 16]. A Bayesian network contains two major attributes as described in Appendix A: directed acyclic graph

(DAG) and conditional independence. Both of these attributes are important in the system

we have developed.

Bayesian networks offer several advantages over other reasoning-based networks. The advantages include:

1. Bayesian networks are robust at handling incomplete sets of data, offering the power

of prior knowledge, which is embedded in the causal semantics of the network to pre-dict the outcome of a process [15]. The network encodes conditional dependencies

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among the input variables, allowing for prediction of an outcome even when inputs are not completely observed. This ability is much similar to what a physician has when deciding on which drugs to prescribe to the patient. For example, a physician may want to know whether or not to prescribe drug A to improve the patient's health. To arrive at the best conclusion, the physician can determine if drug A directly con-tributes, and to what degree, to the well beings of most people with the same physical symptoms-even when no close-at-hand information about the effects of drug A to patients is available.

2. Knowledge of causal relationships in a Bayesian network can be represented in a graphical model or structure, facilitating our understanding of a problem domain'. The graphical structure consists of mathematical relationship that is made explicit and well-understood, and can be rearranged to increase computational efficiency and to weight evidence softly.

4.2 CHF Network Overview

Medical literature was used to ascertain information on probabilities and dependencies used in the network, which was created by hand. To improve the quality and accuracy of a diagnostic network, it will be necessary to consult medical experts and to continuously enhance the network.

Heart failure disease encompasses multiple disease etiologies (causes) and patterns of manifestations (effects) [10]. There is a variety of symptoms related to CHF [44]. In this work, symptom is a node in the network and is arranged in the order of causality on the

DAG. For example, both lower-leg edema and orthopnea supercede unexpected weight gain

on the DAG, because the former two contribute to fluid retention in the body resulting in the latter. It must be noted that some variables carry more weight than others in terms of their impact to heighten or lower the probability of CHF risk factor, or P(CHF). For example, an individual that has dyspnea is less likely to have CHF than another who has shown symptoms for orthopnea. This is because dyspnea can be a direct result of other precipitating factors such as arrhythmia or asthma, whereas orthopnea is more specifically a consequence of pulmonary edema which indicates a weak heart. When assigning conditional

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probabilities to the links for all variables in the network, we have to bear in mind the impact of these variables on P(CHF).

In this work, key variables and their relative importance in a CHF domain were pri-marily ascertained from a quality care assessment study for CHF [21]. In an article by Ashton et al, it was found that both dyspnea (difficulty breathing) and orthopnea (diffi-culty breathing when lying down) are one of the most common hence important symptoms of CHF, concurred by various physicians. That is, the presence of either symptom during pre-admission determines if the patient is already at-risk for CHF. The objective of the quality care assessment study was to rank variables that are most relevant, common, and specific to the evaluation of CHF patients in hospitals. The block diagram presented in

App. E is a graphical representation of inter-quartile ranking of these variables set forth in

[21].

An initial network was made consisting of some of the key variables in [21], as illustrated in Fig 4-1. Suggestions from medical doctors allowed us to make modifications to our network and assignments of conditional probabilities for each link, simplifying the network

by employing only the higher-level variables that are most relevant and important in the

domain of CHF [75, 76]. Fig. 4-1 depicts a graphical representation of the network, with variable names, their states, and prior probabilities listed.

In the network, biometric variables such as weight and blood pressure are prefixed with a B, whereas contextual variables with a Q.. The target variable, CHF, does not come with any prefix. Associated with each prefixed variable is an evidence node, which is used to weight the incoming evidence for the same variable. Each evidence node in the network has 10 states that include 0% < a < 10% to 90% < a < 100%. The use of these states and their meaning are described in Chapter 5. Each of the eight states associated with a represents severity in evidence. For example, if the evidence for Q-SkippedMed is that the patient has been skipping medications for 10% of the time since last week, the state 0% < a < 10% in EQSkippedMed will be set true to adjust the probability distribution underneath QSkippedMed. 0% < a < 10% represents the state of severity in the QSkippedMed category for that individual at that given time. When the patient has completely forgotten to take medications 95% of the time since last week, the state

90% < a < 100% in EQ-SkippedMed is set true. The effect of setting a state in an evidence

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the last case of the patient with a weekly 5% medication compliance rate (i.e., skipping medication 95% of the time), P(CHF) for this individual becomes 0.0206 as compared to

0.0196 for normal people. Chapter 5 elaborates on the mechanism of how evidence are

weighted. Note that evidence nodes associated with biometric variables are prefixed with a EB_ and contextual variables with a EQ_.

In a home environment, B_ variables are measured with biometric devices such as weight scale, pulse monitor, glucose meter, to name a few. Note that in the early warning system developed in this work, biometric data include both weight and systolic blood pressure. Contextual questions, however, are dispensed to the patient via a PDA, hence the prefix Q_ which denotes question variable.

4.3 Design Principles for CHF Network

Design Principle 1: Causation used in the ordering of variables in CHF net-work goes from predisposed signs, internal state of disease, to symptoms. In a diagnostic reasoning model, clinical signs lead to internal conditions or failure states, which leads to a plethora of inter-related observables, or symptoms. We argued that predisposi-tion, or clinical signs, relating to heart failure influences the likelihood of developing CHF, which leads to a plethora of symptoms. In this way, both signs and symptoms are made independent of each other, i.e., they are indirectly related through the target, CHF. The causal relationship for CHF is depicted in a flow diagram in Fig. 4-2.

In this way CHF is immediately dependent on either predisposition, which consists of hyper-tension, coronary artery disease, and medication compliance, and symptoms, which includes respiratory problems and fluid retention. A graphical breakdown of Fig. 4-2 is shown in Fig. 4-3.

Note that Fig. 4-3 is a diagnostic causal network that infers the presence of CHF based on percept-driven information, namely, signs or known symptoms of CHF. Thus, even though the network is ordered causally the direction of diagnostic reasoning goes from effects to causes [33].

Design Principle 2: In the network, signs and symptoms are conditionally in-dependent. To satisfy this independence assumption, we make sure that there is no

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dependent links between signs and symptoms, i.e., they are conditionally independent of each other.

Design Principle 3: In the network evidence nodes, EQ_ and EB_, might be con-ditionally dependent on the other non-evidence nodes. For example, EQAngina and QCAD are dependent on each other since both are linked to QAngina. This is plausi-ble considering that the probability of someone having angina at any given time relates to whether the individual also has a history of developing any coronary artery diseases.

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EQSmoking EQ_Drin kdng EB_Hypertension EQ_CAD

0%<a l10% 0%<as 10% 0%< P10% 0%<a 10%

10% <a 20% 10%<as 20% 10%<p s20% 10%<as 20%

20% <a &30% 20%<ms 30% 20%< Ps30% 20%<as 30%

30% < a s 40% 30% < a s 40% 30% < 40% 30% < a 40%

% < a s 0% 40% < a s 50% 40% < 50% 40% < a & 0%

60% <a60% 50%<as 60% 50%<p 60% 50%<as 60%

60% < o70% 60%<asl70% 60%<ps70% 60%<as 70%

70% <a 80% 70%<as 80% 70%<ps80% 70%<as 80%

a0%< a 90% 80%<Ms 90% 80%<p s90% 80%<as 90%

90% <a s100% 90% <as 100% 90% <p s100% 90% <a s100%

QD-inking G-CAD

Alcoholic 12.5 g Presence 7.70

NonAlcoholic 87.Absence 92.3

Q.Smoldng _{B Hypertension}

Smoker 24.6 QAngina

NonSmoker 75.4 Absent 799 Presence 3.39

Absence 96.6

EQHighChol

0% < a s 10% tHigh~hol

10% <a s 20 % HighCholest... 16.5 EQ_Angina

20% < as 30% 0% < M 10%

30% <a s 40% 10% <a s 20%

4U% < a s 50% Q_SldppedMed Congestiv Heart Failure 20% < a 30%

&0% < a 5 60% Yes 30.0 True 1.95 30% <a 40%

80% < a & 70% No 70.0 False 93.0 40% <a 50%

70% <as 80% 50% <a (60% 80% <as 90% 60% <as 70% 90% <a 100% 70% <a 80% 80% <a s 90% EQ_SldppedMed 90% < a 100% 0% < M 5 10% Q-Dyspnea QO-thopnea 10% < 2a 20% Exertional 5.73 Presence 523

20% <a s 30% NonExertional . Absence 5.2 Q _EdeOma

30% <a s 40% Presence 6.28 % < as 50% Absence 93.7 40% <a s 60% 60% <a s 70% BWeightGin 70% < a s 80% Sudden 596 a 80% < a e 90% Progressiwe 94.0 9% < as 100% 1

EQ.oyspnea EQ_Orthopnea EBWeightGain EQEdema

E%_<yspn10%0% < a s 10% 0% < ps 10% 0% < a s 10% 0% < as 10% 10% < as 20% 10% < p r 20% 10% < a r 20% 10% < a(s 20% 20% < as 30% 20% < P s 30% 20% < a s 30%

20%<a&O0% 30%<ms40% 30%<pes4% 30%<as40%

40%<540% 40% < a: 0% 40% < p .50% 40% < a 50%

5U%<a20% 50%<as60% 50%<ps0% 50%<as60%

00% < acs 70% 60% < asl 70% 60% < p s 70% 60% < as 70% 70% < as 70% 70% < a s 80% 70% < p s J0% 70% < asS 80%

70% < a. sB 0% 80% < a s90% 80% <P 90% 80% < a 90% 90% < a s 90% 90% < as 100% 90% < p s 100% 90% < a 100%

96% <a s 100%

Figure 4-1: Bayesian reasoning network for CHF

Links to internal Links to

SIGNS & failure states DISEASES observables SYMPTOMS

a D

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...

H ypertension

Predisposition

Medication

Coronary

signs

Compliance

_artery

disease

iter nal failure

state/disease

CH

F

Symptoms Respiratory Fluid

problem

retention

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Chapter 5 Question Querying Mechanism

Querying is a process by which a Bayesian reasoning network generates health questions and sends them to the user, via a personal digital assistant (PDA). Responses from the user are sent back to the network as evidence, which change the joint probability distributions in the network enabling it to generate the next set of questions. Health questions were carefully designed. These questions are ordered by the medical/variable categories they belong to, and locations of the user at the time of querying in the test environment.

5.1 Topology of a Querying Process

The querying mechanism is illustrated in a flow diagram in Fig. 5-1. As discussed in Sec. 4.2, evidence entered in the network exerts different levels of impact on the probability of CHF. In a CHF domain, the degree of impact is measured by the percentage change of P(CHF) as we vary the evidence entered. For example, setting true to Skipped Medication category increases the value of P(CHF) from 1.96% to 2.77%, whereas setting absent to QAngina results in a P(CHF) lowering to 1.7%.

The evidence nodes are attached to both Q_ and B-type variables to softly weight the incoming evidence, such that a single evidence entered will not have a significant impact on the network. Each evidence node contains 10 weighted states. Setting 0% < a <

10% in EQHighChol evidence node, for example, changes P(CHF) from 1.96% to 1.98%,

reflecting an 1.01% change in P(CHF). This minute change in the numerical distribution of probabilities is necessary and is a vital part of querying process. When the user answers a health question, the response to the question becomes evidence and subsequently enters

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Bayesian Reasoning

Network generate

Contextual Categories go to next cate gory

. t ordered by 4

digree of imp:.ct select

Context _{not found} _Selection

specific l

question display

PDA

Figure 5-1: Flow Diagram of a Querying Process

into the network. An answer to one question should not have made a significant impact to the network. The user might be entering false responses into the network for a variety of reasons. He or she might feel rather frustrated one day and exacerbated the situation by falsely responding to the system. The assumption in this work is that if enough evidence is gathered after a sufficient amount of time, accurate responses will overwhelm the user's

noise.

A window period of 7 days is used for CHF detection. This window period was confirmed

by several local medical experts in the field [75, 76]. Measurements taken during this window

period are compared with average values taken from the week prior to this period. In other words, P(CHF) for an individual is assessed at any given time based all evidence gathered this week and the week prior to this time.

In the following section, we describe the heuristic rules used by the system to arrive at a prognosis of CHF.

5.1.1 Querying Heuristics

Querying heuristic involves taking all 7-day window of evidence for a particular medical category (or variable), and determining the severity of the user's medical state pertaining

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to that particular variable category. 0% < a < 10% and EQHighChol are an example of the severity of medical state and the variable category it is in, respectively. Note that evi-dence node EQHighChol corresponds to the medical variable, QHighChol. For a particular medical variable, the heuristic rules used can be described in three steps as follows:

1. Using a window period of 7 days, the system determines a positive rate, a, or the

number of positive responses over all responses entered into the system by the user.

A positive response is the user's answering "yes" to a health question, and a negative

response is answering "no" to a health question. If no response is entered for a health question, the system inputs a "none" in the database to signify the lack of response at the time. In this system, the total number of responses is calculated by counting the total number of "yes" and "no" responses over the window period. A positive rate, a, is therefore the number of "yes" responses in last week relative to all "yes" and "no" responses.

Note that the number of "no" responses is defaulted to 5. That way, when there are no responses made in the last 7 days, a will not have a value of infinity. In addition when there is only one "yes" response and it is the only response over last week, it makes more sense that this "yes" response is softly weighted. In this case, a = 1/(1+5) = 0.17% instead of 100% if the number of "no" responses were defaulted

to 0.

2. The numerical value of a corresponds to one of the eight states, i.e., 0% < a < 5% through 90% < a < 100%, in a given evidence node. For each Q_-type variable, these eight states are listed as follows:

state 1 = 0% < a < 10% (5.1) state 2 = 10% < a < 20% state 3 = 20% < a < 30% state 4 = 30% < a < 40% state 5 = 40% < a < 50% state 6 = 50% < a < 60% state 7 = 60% < a < 70%

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state 8 = 70% < a < 80%

state 9 = 80% < a < 90% state 10 = 90% < a < 100%

3. We now define a biometric change rate, 3, which is a percentage change in the level of a biometric variable (B) over an averaged value from last week. For instance, say a person has had an average weight value of 146 lb from last week. On Tuesday this week, his weight as indicated is 1541b. The value of 3 at that point in time is (154 - 146)/154 x 100 = 5.19%. Mathematically speaking,

/ = (B - R)/B x 100 (5.2)

where R signifies an averaged B over the course of a previous window period. As time progresses, the window period also progresses resulting in dynamic changes in average biometric values and

#.

The heuristic rule used for B-type variables is the same as the one used for Q.-type variables.

4. The process of setting a state in an evidence node is termed setting observation. After the system determines the states for both a and / and sets the states to be true, the effect results in numerical changes in the joint probability distribution of the network. For example, checking 20% < a < 30% state in EQ-Smoking results in a slight change

in P(QSmoking) from 24.6% to 26%, changing P(CHF) from 1.96% to 2%.

In summary, setting observation in an evidence node sends a small impact to the network. During user querying, this amount of change to the network is desired because we want to avoid situations where responding to a single health question can result in a big change in P(CHF). In the work, the amount of impact corresponds to how many positive responses

(relative to total responses) have been entered by the user in the variable category.

5.1.2 Sensitivity of CHF to Observations

At any given time, a Bayesian network is capable of generating an impact list of all variables in the network, ranking them by the order of impact to the target. In a CHF domain, the network picks a variable that has the biggest impact to CHF, and checks into a database

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of questions to see if there are any questions that fall into the variable category. A set of variable-specific questions are then selected based on the user's location. For example, a home occupant might have developed an exertional dyspnea due to a predisposed coronary heart disease and high blood pressure in the last few years. He occasionally experiences difficulty breathing but these symptoms are mild. He gets up one day from the desk and steps into the kitchen. The system detects that the person is right by a kitchen counter and the variable QDyspnea shows up high on the impact list. There are many temporally relevant questions associated with QDyspnea but given the time is noon, a cooking-related question may be the more appropriate question to ask than ones that relate to other activities. For the category of Q.Dyspnea, location type kitchen, and the time is noon, the system then sends a QDyspnea and kitchen-specific question to him via a PDA: Do you sometimes have difficulty breathing when you cook?.

The challenges in developing suitable health questions lie in the fact that questions must

1. Be medically informative and non-intrusive

2. Be minimally intimidating and annoying to the user

3. Make sense temporally

The process of question generation goes on as the user's location is changed, i.e., location-specific and temporally-relevant questions are generated when appropriate.

Setting observation in an evidence node changes the probability distribution in the network. In particular, the change of probability for CHF is noted. This is measured in terms of the percentage difference in P(CHF) before and after the observation is made, as illustrated in Sec. 5.1. When the previous evidence is unset, P(CHF) returns to the original value in the network. We proceed to set evidence on a different evidence node and record the change in P(CHF). This process goes on until an impact list of P(CHF) changes associated with different nodes is obtained. This process is termed sensitivity test, which is used to measure the sensitivity of the target variable (CHF) as observations vary. Note that the impact list roughly corresponds to the relative clinical importance of key variables indicated in [21]; in the article, both orthopnea and dsypnea are ranked pretty high in terms of their clinical importance.

Different responses to health questions contributes to different degrees of impact to the network, resulting in a varying P(CHF) predicted. From time to time, the system gathers

Developing an early warning system for congestive heart failure during a Bayesian reasoning network