
UNIVERSITY OF CALIFORNIA, IRVINE Health State Estimation DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Computer Science by Nitish Nag Dissertation Committee: arXiv:2003.09312v1 [cs.AI] 16 Mar 2020 Professor Ramesh C. Jain, Chair Professor Michael J. Carey Professor Gopi Meenakshisundaram 2020 © 2020 Nitish Nag DEDICATION To my inspiring advisor Ramesh Jain, exceptional parents Bishwajit and Sesha, my illuminating partner Tabya, and the shoulders of all the giants we stand upon. ii TABLE OF CONTENTS Page LIST OF FIGURES vii LIST OF TABLES xix LIST OF ALGORITHMS xxi ACKNOWLEDGMENTS xxii CURRICULUM VITAE xxiii ABSTRACT OF THE DISSERTATION xxvi 1 Introduction 1 1.1 Overview of Health and Medicine . .7 1.2 Modern Trends in Health Sciences . 12 1.2.1 Personalized Health . 13 1.2.2 Predictive Health . 14 1.2.3 Precision Health . 15 1.2.4 Participatory Health . 19 1.2.5 Preventive Health . 19 1.2.6 Healthcare Systems . 20 1.2.7 Performance and Wellness . 21 1.3 Health State Estimation is Critical for Progress . 23 1.4 Contributions . 25 1.5 Thesis Outline . 28 2 Understanding Health Computing 29 2.1 Cybernetics: Inspiration for this Work . 29 2.2 Defining Health . 33 2.2.1 Biological . 39 2.2.2 Functional . 41 2.2.3 Environmental . 44 2.2.4 Systems . 46 2.2.5 Perceptual . 47 2.2.6 Integrating a Holistic Approach . 52 iii 2.3 Design Requirements for Health State Estimation . 53 3 Literature Review 59 3.1 Health Data Acquisition . 60 3.1.1 Self Insight . 61 3.1.2 Digital Sensors . 64 3.1.3 External Data Sources . 77 3.2 State Estimation for Individual Health . 79 3.2.1 Quantified Approaches . 80 3.2.2 Classification Approaches . 81 3.2.3 Probabilistic Approaches . 84 3.2.4 Subjective Approaches . 87 3.3 Personal Human Health Models . 88 3.3.1 General Human Systems . 89 3.3.2 Sub-Population Fitting . 93 3.3.3 Static Individual Parameters . 94 3.3.4 Towards Dynamic Personal Models . 95 3.4 Artificial Intelligence and Graphs . 99 3.4.1 Graph Networks . 99 3.4.2 Causal Reasoning . 100 3.4.3 Knowledge Structures . 102 3.4.4 Systems Thinking . 106 3.4.5 Microservice Architecture . 108 3.4.6 Data-Driven Learning . 108 3.4.7 Robust Artificial Intelligence . 111 3.5 Unmet Challenges and Scope of Dissertation . 113 4 Health State Estimation Framework 115 4.1 Health State Estimation General Overview . 116 4.2 Graph Network Block Structure . 117 4.2.1 Nested Microservices . 119 4.2.2 Causal Knowledge Infusion . 120 4.2.3 Data-Driven Model Updates . 121 4.2.4 Combinatorial Generalization . 122 4.3 Semantic Laminae: Interactive Layers . 123 4.4 Utilities: The Dimensions of Health . 125 4.5 Biological Blocks: Natural Micro-Services . 128 4.6 Events: Inputs to Life . 129 4.7 Initializing the System . 134 4.8 State Update Method . 135 4.8.1 Interface Event Retrieval . 137 4.9 Individual Dynamic Model Updates . 138 4.10 Revisiting the Design Requirements . 140 iv 5 Instantiating Personal Health State Estimation 143 5.1 Experimental Overview . 144 5.2 Methods . 145 5.2.1 Longitudinal . 145 5.2.2 N=1 . 146 5.2.3 Multi-Modal Fusion . 146 5.2.4 Living-Labs Implementation in the Real World . 147 5.3 Data . 148 5.3.1 Collection Tools . 149 5.3.2 Events . 154 5.3.3 Biology and Utility Direct Measurements . 160 5.4 HSE System Initialization . 163 5.4.1 Translating User Intent . 163 5.4.2 Retrieval of Relevant Utility State Space . 164 5.4.3 Populating Biological Blocks . 167 5.5 Health State Update . 175 5.5.1 Time and Event Based HSE Updates . 175 5.5.2 Observation Based HSE Updates . 179 5.5.3 Propagating Updates . 179 5.6 Personal Model Update . 192 5.6.1 Knowledge Update . 192 5.6.2 Parallel Observations . 196 5.6.3 Cross-Modal . 199 5.7 Other Visualizations of HSE . 203 5.7.1 Multi-Person View . 204 6 Personal Health Navigation 208 6.1 General Health State Space . 211 6.2 Personal Health State Space . 212 6.3 State Transition Network . 213 6.4 Knowledge Layers and Regions of Interest . 214 6.5 Current State Estimation . 215 6.6 Goal Decomposition . 216 6.6.1 Timescales . 217 6.6.2 Recurring Goals versus Achievement Goals . 218 6.6.3 Synergistic Goals versus Competing Goals . 218 6.6.4 Identifying User Motivations for Goals . 219 6.7 Guidance . 219 6.7.1 Routing . 221 6.7.2 Actions and Cybernetic Control . 222 6.7.3 Recommendation . 227 v 7 Conclusions and Future Work 238 7.1 Technical Challenges . 239 7.1.1 Building Rich Knowledge Atlases . 239 7.1.2 Hardware . 240 7.1.3 Computing . ..
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