Mental Health Informatics: Enabling a Learning Mental Healthcare System

Preface
Acknowledgments
Contents
Chapter 1: Precision Medicine and a Learning Health System for Mental Health
	1.1 Introduction
	1.2 The Need for Precision Mental Healthcare
		1.2.1 Informatics: A Brief Preview
	1.3 The Path to the Learning Health System
		1.3.1 The Traditional Model for the Discovery and Application of Knowledge in Healthcare
		1.3.2 Translational Science
			1.3.2.1 Limitations of Translational Research
		1.3.3 The Learning Health System Paradigm
			1.3.3.1 Limitations of the Learning Health System Paradigm
		1.3.4 Foundational Requirements of a Learning Health System in Mental Health
		1.3.5 Learning Health System Models: The Role of Informatics
	1.4 Precision Medicine in Mental Health
		1.4.1 The Role of Informatics in Precision Medicine
		1.4.2 A Learning Heath System for Precision Mental Health
	1.5 Summary and Conclusions
	References
Chapter 2: What Is Informatics?
	2.1 History and Role in Biomedicine and Health
	2.2 From Data to Knowledge (D2K)
		2.2.1 Knowledge Discovery Process
		2.2.2 Data and Databases
		2.2.3 Natural Language Processing and Text Mining
		2.2.4 Data Mining and Machine Learning
		2.2.5 Standards and Interoperability
	2.3 From Knowledge to Performance (K2P)
		2.3.1 Clinical Decision Support
		2.3.2 Software and Knowledge Engineering
		2.3.3 Human Factors Engineering
	2.4 From Performance to Data (P2D)
		2.4.1 Evaluation Models
		2.4.2 Quantitative and Qualitative Methods
	2.5 Summary
	References
Chapter 3: The Mental Health System: Definitions and Diagnoses
	3.1 Introduction
	3.2 Defining Mental Health and Mental Illness
		3.2.1 The Concept of Mental Health
		3.2.2 Health and Disease
		3.2.3 Definitions of Mental Health
		3.2.4 Mental Health and Somatic Health
	3.3 The Concept of Mental Illness
		3.3.1 The Continuums of Mental Health and Illness
	3.4 Theories of Psychopathology
		3.4.1 Biological Theories of Psychopathology
		3.4.2 Psychological Theories of Psychopathology
		3.4.3 Social Theories of Psychopathology
		3.4.4 The Biopsychosocial Theory of Psychopathology
	3.5 Defining Mental Disorders
		3.5.1 Diagnostic Classification Systems Used in Mental Healthcare: DSM-5 and ICD-11
		3.5.2 Mental Health Conditions
	3.6 Conclusions
	References
Chapter 4: The Mental Healthcare System: Organization and Structure
	4.1 Introduction
	4.2 Mental Healthcare Professionals
		4.2.1 Types of Mental Healthcare Professionals
	4.3 Mental Healthcare Settings
		4.3.1 Inpatient Settings
		4.3.2 Outpatient Settings
	4.4 Disparities in the Mental Health Workforce
	4.5 Mental Healthcare Payment Models
		4.5.1 Privately-Funded Insurances
		4.5.2 Publicly-Funded Insurances
	4.6 Summary
	References
Chapter 5: The Mental Health System: Access, Diagnosis, Treatment, and Monitoring
	5.1 Introduction
	5.2 Access to Mental Healthcare
		5.2.1 Pathways to Care: Primary Care
		5.2.2 Alternate Pathways to Care
		5.2.3 Delays in Care
	5.3 Mental Health Assessment and Diagnosis
		5.3.1 The Assessment of Illness
		5.3.2 Diagnosis and Case Conceptualization
	5.4 Mental Health Treatment
		5.4.1 The Treatment Setting
		5.4.2 Selecting the Right Treatment
		5.4.3 Psychotherapy and Social Interventions
		5.4.4 Pharmacotherapy
		5.4.5 Neuromodulation and Surgical Interventions
	5.5 Treatment Monitoring
		5.5.1 Patient Reported Outcome Measures
		5.5.2 Side Effect Monitoring
	5.6 Conclusion
	References
Chapter 6: Mental Health Informatics
	6.1 Mental Health Informatics as an Informatics Subdiscipline
	6.2 Contrasting Mental Health Informatics with Related Disciplines
		6.2.1 How Mental Health Informatics Differs from Mainstream Biomedical and Health Informatics
			6.2.1.1 Differences in the Phenomena of Interest
			6.2.1.2 Differences in the Knowledge Acquisition Cycle
			6.2.1.3 How Mental Health Informatics Differs from Other Informatics Work in Mental Health
		6.2.2 Mental, Behavioral, and Social Phenomena in Mainstream Health Informatics
	6.3 Mental Health Informatics: Bridging the Biological, Behavioral, and Social Sciences
		6.3.1 Mainstream Health Informatics Has Not Fully Embraced Social and Behavioral Phenomena
		6.3.2 Epistemological Differences Between the Behavioral and Biological Sciences
		6.3.3 A Primary Epistemological Challenge for Informaticians: The Relationship Between the Mind and Brain
		6.3.4 Epistemological Differences within the Behavioral and Social Sciences: A Multiplicity of Theories of ‘Mind’ and Behavior
		6.3.5 Points of Intersection Between the Biological, Behavioral, and Social Sciences
	6.4 How Mental Health Informatics Extends Informatics
	6.5 Summary
	References
Chapter 7: Technologies for the Computable Representation and Sharing of Data and Knowledge in Mental Health
	7.1 Introduction
	7.2 Technologies for Representing Data, Information, and Knowledge
		7.2.1 The Terminology Used to Describe “Terminology”
		7.2.2 Concept Representation
		7.2.3 Controlled Vocabularies
		7.2.4 Classifications
		7.2.5 Terminologies
		7.2.6 Information Models
		7.2.7 Knowledge Representation
	7.3 What Is a Standard?
		7.3.1 Content Standards
		7.3.2 Syntax Standards
		7.3.3 Semantic Standards
			7.3.3.1 SNOMED CT
			7.3.3.2 LOINC
	7.4 Interoperability Standards
		7.4.1 HL7 Messages
		7.4.2 Consolidated Clinical Document Architecture (C-CDA)
		7.4.3 Fast Health Interoperability Resources (FHIR)
	7.5 Repositories of Standards
		7.5.1 FAIRSharing
		7.5.2 Interoperability Standards Advisory (ISA)
	7.6 Addressing Gaps in Standards to Accommodate Mental Health
		7.6.1 Standards for Concept and Knowledge Representation in Mental Health
		7.6.2 Minimum Clinical Data Sets
		7.6.3 Quality of Terminologies Relative to Mental Health
	7.7 Conclusions and Recommendations
	References
Chapter 8: Use of Medical Imaging to Advance Mental Health Care: Contributions from Neuroimaging Informatics
	8.1 Introduction
	8.2 Capturing Meaningful Neuroscientific Anatomic and Physiologic Data
	8.3 Radiology Workflow: From Order to Storage
	8.4 Data and Standards
	8.5 Image-Derived Features for Mental Health
		8.5.1 Magnetic Resonance Imaging
		8.5.2 Nuclear Medicine Imaging
		8.5.3 Neurophysiology Workflows
		8.5.4 Neuroimaging Informatics
	8.6 Challenges and Opportunities
	References
Chapter 9: Informatics Technologies for the Acquisition of Psychological, Behavioral, Interpersonal, Social and Environmental Data
	9.1 Introduction
	9.2 Psychometrics: A Brief Primer
	9.3 Types of Data Relevant for Mental Health
		9.3.1 Psychological Data
			9.3.1.1 What Is Measured
			9.3.1.2 Measurement Approaches
		9.3.2 Behavioral Data
		9.3.3 Social and Interpersonal Data
		9.3.4 Environmental Data
	9.4 Informatics Technologies for Data Acquisition
	9.5 Challenges, Limitations and Future Directions
	References
Chapter 10: Data to Information: Computational Models and Analytic Methods
	10.1 Introduction
	10.2 Analytic Approaches to Computational Modeling
	10.3 Theory-Based Approaches
		10.3.1 Dynamical Systems
		10.3.2 Causal Networks
	10.4 Data-Driven Approaches
		10.4.1 The Workflow in Machine Learning
	10.5 Preprocessing
		10.5.1 Dimensionality Reduction
		10.5.2 Feature Selection Methods
		10.5.3 Feature Extraction Methods
	10.6 Machine Learning Algorithms
		10.6.1 Supervised Learning
		10.6.2 Unsupervised Learning
		10.6.3 Semi-Supervised Learning
		10.6.4 Deep Learning
	10.7 Evaluation of Model Performance
		10.7.1 Supervised Models
		10.7.2 Unsupervised Models
	10.8 Applications of Computational Models in Mental Health
	10.9 Standards for Reporting Models
	10.10 Policy, Ethical, and Safety Issues
	10.11 Conclusion
	References
Chapter 11: Bioinformatics in Mental Health: Deriving Knowledge from Molecular and Cellular Data
	11.1 Introduction
		11.1.1 Translational Bioinformatics and Biomarker Discovery
		11.1.2 How Bioinformatics and Data Science Contribute to Biomarker Discovery in Mental Health
	11.2 Types of Data in Biomarker Discovery
		11.2.1 Genomics: The Study of the DNA
			11.2.1.1 Data Processing
			11.2.1.2 Strengths and Limitations
			11.2.1.3 Examples in Mental Health
		11.2.2 Transcriptomics: The Study of the RNA
			11.2.2.1 Data Processing
			11.2.2.2 Strengths and Limitations
			11.2.2.3 Examples in Mental Health
		11.2.3 Proteomics: The Study of Proteins
			11.2.3.1 Data Processing
			11.2.3.2 Strengths and Limitations
			11.2.3.3 Examples in Mental Health
		11.2.4 Metabolomics: The Study of Metabolites
			11.2.4.1 Data Processing
			11.2.4.2 Strengths and Limitations
			11.2.4.3 Examples in Mental Health
		11.2.5 Epigenetics/Epigenomics
			11.2.5.1 Data Processing
			11.2.5.2 Strengths and Limitations
			11.2.5.3 Examples in Mental Health
		11.2.6 microRNA
			11.2.6.1 Data Processing
			11.2.6.2 Strengths and Limitations
			11.2.6.3 Examples in Mental Health
		11.2.7 DNA Copy Number
			11.2.7.1 Data Processing
			11.2.7.2 Strengths and Limitations
			11.2.7.3 Examples in Mental Health
		11.2.8 Neuro-Imaging
			11.2.8.1 Data Processing
			11.2.8.2 Strengths and Limitations
			11.2.8.3 Examples in Mental Health
		11.2.9 Emerging Data Types: Microbiome
			11.2.9.1 Data Processing
			11.2.9.2 Strengths and Limitations
			11.2.9.3 Examples in Mental Health
	11.3 Cellular Attributes in Biomarker Discovery
	11.4 Systems Biology in Mental Health
	11.5 Mental Health Vs. Medical Conditions
		11.5.1 Bioinformatics Knowledge Discovery and Application: An Example in Mental Health
	11.6 Conclusion
	References
Chapter 12: Integrative Paradigms for Knowledge Discovery in Mental Health: Overcoming the Fragmentation of Knowledge Inherent in Disparate Theoretical Paradigms
	12.1 Introduction
	12.2 Integrative Semantic Frameworks and the RDoC Initiative
	12.3 Integrative Computational Methods
		12.3.1 Factor Analysis
		12.3.2 Network Analysis
		12.3.3 Computational Psychiatry
		12.3.4 Within- and Between-Person Reasoning
	12.4 Discussion: Epistemology and the Limitations of Integrative Paradigms
	12.5 Conclusions
	References
Chapter 13: Natural Language Processing in Mental Health Research and Practice
	13.1 Introduction
	13.2 Corpus Generation
		13.2.1 Using Medical Records as a Corpus
			13.2.1.1 Collecting Medical Records
			13.2.1.2 De-Identification of Medical Records
			13.2.1.3 Annotation of Medical Records
			13.2.1.4 Publicly Available Medical Record Datasets
		13.2.2 Generating a Corpus from Social Media Data
			13.2.2.1 Collecting and Annotating Social Media Data
			13.2.2.2 Privacy with Social Media Data
		13.2.3 Other Data Sources
	13.3 Data Processing
		13.3.1 Preprocessing
		13.3.2 Featurization
			13.3.2.1 Term Vectors
			13.3.2.2 Sentence and Document Vectors
			13.3.2.3 Count-Based Features
			13.3.2.4 Rule-Based Features
			13.3.2.5 Sentiment and Psycholinguistic Features
			13.3.2.6 Sociability Features
			13.3.2.7 Temporal Features
		13.3.3 Analyzing Natural Language Data
			13.3.3.1 Rule-Based Systems
			13.3.3.2 Supervised Machine Learning Systems
			13.3.3.3 Deep Learning Systems
			13.3.3.4 Unsupervised Machine Learning
	13.4 Applications of Natural Language Processing in Mental Health
		13.4.1 Mental Illness Detection
		13.4.2 Symptom and Severity Extraction
		13.4.3 Lexicon and Ontology Construction
		13.4.4 Knowledge Discovery
		13.4.5 Other Applications
	13.5 NLP in Mental Health Practice
	13.6 Challenges, Limitations, and Ethical Considerations
		13.6.1 Challenges
		13.6.2 Ethical Considerations
	13.7 Conclusions
	References
Chapter 14: Information Visualization in Mental Health Research and Practice
	14.1 Introduction
	14.2 A Crash Course in Information Visualization
		14.2.1 Why Visualization?
		14.2.2 Visualization Tasks
		14.2.3 Building Visualizations
			14.2.3.1 Understanding User Needs and Goals
			14.2.3.2 Preparing Data
			14.2.3.3 Displaying Data
			14.2.3.4 Interacting with Data
	14.3 Mental Health Data
		14.3.1 Survey and Psychometric Instrument Data
		14.3.2 Electronic Health Record (EHR) Data
		14.3.3 Genetic Data
		14.3.4 Environmental Data
		14.3.5 Mobile Health Data
		14.3.6 Using Data and Predictive Models in Mental Health Visualization
	14.4 Current State and Outstanding Challenges
		14.4.1 Uncertainty
		14.4.2 Evaluation
	14.5 Conclusion
	References
Chapter 15: Big Data: Knowledge Discovery and Data Repositories
	15.1 What Is “Big Data”: The Big Part, the Data Part?
	15.2 Methods and Paradigms
		15.2.1 Essential Elements for Big Data Repositories
			15.2.1.1 Governance
				Technical Infrastructure
				Metadata
	15.3 Big Data and Data Repositories
		15.3.1 The Fair Guiding Principles
	15.4 Secondary Usage
		15.4.1 Biobanks
	15.5 Categories of Data and Data Repositories
		15.5.1 Refined Scientific Knowledge: Publication Databases and Specialist Databases
		15.5.2 Biological Data
		15.5.3 Behavioral Data
		15.5.4 Clinical Administrative Data Repositories
		15.5.5 Electronic Health Records
		15.5.6 Linked Multi-Modal Data Repositories: Multiple Data Sources
		15.5.7 Practical Challenges of Using Data Repositories for Mental Health Research
	15.6 Case Study: Developing a Big Data Registry/Repository
		15.6.1 Who Develops Disease-Specific Data Repositories in Mental Health and Why?
	15.7 Closing Thoughts: Opportunities and Challenges
	References
Chapter 16: Electronic Health Records (EHRS) and Other Clinical Information Systems in Mental Health
	16.1 Introduction
		16.1.1 Historical Perspective
		16.1.2 Federal Initiatives Related to Health IT
		16.1.3 ACOs and PCMHs
			16.1.3.1 The State Innovation Models (SIM) Initiative
		16.1.4 Overview of EHRs
			16.1.4.1 Landscape of EHRs Across Medical and Mental Health Care
			16.1.4.2 Common EHR Vendors in the Mental Health Field
			16.1.4.3 Medical EHRs with Behavioral Health Components
		16.1.5 The Proposed Value of EHRs
			16.1.5.1 Patient Safety and Quality of Care
			16.1.5.2 Improved Efficiency
			16.1.5.3 EHR Disadvantages
			16.1.5.4 Secondary Uses for EHRs
				Research Uses
				Learning Health Systems (LHS) and Quality Improvement (QI)
		16.1.6 Personal Health Records (PHRs)
			16.1.6.1 Types of PHRs
			16.1.6.2 Drawbacks of PHRs
		16.1.7 Future Directions
		16.1.8 Conclusion
	References
Chapter 17: Informatics Technologies in the Diagnosis and Treatment of Mental Health Conditions
	17.1 Introduction
	17.2 Detection and Diagnosis
		17.2.1 Consumer Facing Technologies
			17.2.1.1 Wearable Devices
			17.2.1.2 Smartphone Based Assessment
			17.2.1.3 Social Media
			17.2.1.4 Implications for Mental Health Conditions
		17.2.2 Provider Facing Technologies
			17.2.2.1 Computerized Psychometric Assessment
			17.2.2.2 Telemedicine
			17.2.2.3 Mobile Medical Devices
			17.2.2.4 Specialized Clinical Information Systems
	17.3 Prevention and Treatment
		17.3.1 Consumer and Provider Facing Technologies
			17.3.1.1 Online Support Groups
			17.3.1.2 Web Based and Mobile Applications
			17.3.1.3 Coordination and Continuity of Care
	17.4 Ongoing Issues and Challenges
		17.4.1 Contemporary Psychiatric Diagnostics
		17.4.2 Clinician Acceptance
		17.4.3 Patient Acceptance, Access and Equity
	17.5 Summary and Conclusion
	References
Chapter 18: Ethical, Legal, and Social Issues (ELSI) in Mental Health Informatics
	18.1 Introduction
	18.2 Stigma and Data Sharing
	18.3 Ethical AI in Mental Healthcare
		18.3.1 Ethical Issues at Data-Level
		18.3.2 Ethical Issues in Designing AI-Based Systems
		18.3.3 Ethical Issues in Deploying AI-Based Systems in Practice
	18.4 Mobile Health and eHealth Applications for Mental Health
		18.4.1 Passive Data Collection
		18.4.2 Telepsychiatry and Telemental Health
		18.4.3 Virtual Helpers and Providers
			18.4.3.1 Minders
			18.4.3.2 Prostheses
			18.4.3.3 Caregivers
			18.4.3.4 Providers
			18.4.3.5 Personhood and AI
	18.5 Mental Health Advocacy
		18.5.1 What Role Does Patient Advocacy Play in General?
		18.5.2 What Motivates Self-Advocacy in Mental Health?
		18.5.3 How Do Mental Health Service Users and Advocates Bring Lived Experience to Mental Health Treatment?
	18.6 Genomics and Mental Health Informatics
	18.7 Laws and Regulations
		18.7.1 Health Insurance Portability and Accountability Act of 1996 (HIPAA)
		18.7.2 HIPAA Privacy Rule
		18.7.3 HIPAA Security Rule
		18.7.4 Confidentiality of Substance Use Disorder Records
		18.7.5 21st Century Cures Act
		18.7.6 Research Regulations
		18.7.7 General Data Protection Regulation (GDPR)
		18.7.8 California Consumer Privacy Act (CCPA)
	18.8 Concluding Remarks
	18.9 Discussion Questions for Reader Consideration
	References
Chapter 19: The Future of Mental Health Informatics
	19.1 Envisioning an Ambitious Future
		19.1.1 Essential Component 1: Datasets, Data Storage, and Workflows
		19.1.2 Essential Component 2: Harmonizing and Integrating across Datasets
		19.1.3 Training
	19.2 Making a Difference Now: Informatics and a Learning Health System for Psychosis
	19.3 Conclusion
	References
Index