Artificial Intelligence in Behavioral and Mental Health Care (eBook)

David D. Luxton (Herausgeber)

eBook Download: EPUB

2015 | 1. Auflage
308 Seiten
Elsevier Science (Verlag)
9780128007921 (ISBN)

Infos

Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. - Summarizes AI advances for use in mental health practice - Includes advances in AI based decision-making and consultation - Describes AI applications for assessment and treatment - Details AI advances in robots for clinical settings - Provides empirical data on clinical efficacy - Explores practical issues of use in clinical settings

Inhalt

Front Cover 1
Artificial Intelligence in Behavioral and Mental Health Care 4
Copyright Page 5
Contents 6
List of Contributors 10
About the Editor 12
Preface 14
1 An Introduction to Artificial Intelligence in Behavioral and Mental Health Care 16
Introduction and Overview 16
Key Concepts and Technologies 17
What Is AI? 17
Machine Learning and Artificial Neural Networks 18
Natural Language Processing 20
Machine Perception and Sensing 22
Affective Computing 22
Virtual and Augmented Reality 23
Cloud Computing and Wireless Technologies 24
Robotics 24
BCIs and Implants 25
Supercomputing and Brain Simulation 26
The Turing Test 28
Technological Barriers 29
Benefits of AI for Behavioral and Mental Health Care 30
Intelligent Machines Are Better at Some Things 30
Improved Self-Care and Access to Care 30
Integration and Customization of Care 31
Economic Benefits 31
Additional Considerations 32
Conclusion 34
References 36
Additional Resources 40
2 Expert Systems in Mental Health Care: AI Applications in Decision-Making and Consultation 42
Introduction 42
The History – Expert Systems and Clinical Artificial Intelligence in Health Care 43
The Present – Dynamical Approaches to Clinical AI and Expert Systems 47
Temporal Modeling Overview 47
Real-World Clinical Applications – Predicting in a Dynamic World 47
Multi-Agent Models for Personalized Medicine 51
Technology-Enhanced Clinicians 52
Summary of Dynamical Approaches for Clinical AI 53
The Future 54
Cognitive Computing in Health Care 54
The Intersection Between Other Emerging Technologies and Clinical Artificial Intelligence 56
Ethics and Challenges 60
Conclusion 61
References 62
3 Autonomous Virtual Human Agents for Healthcare Information Support and Clinical Interviewing 68
Introduction 68
The Rationale and Brief History of the Clinical Use of VHs 70
Use Cases: SimCoach and SimSensei 74
SimCoach: A VH Agent to Support Healthcare Information Access 74
SimSensei: A VH Interviewing Agent for Detection and Computational Analysis of Psychological Signals 79
Nonverbal Behavior and Clinical Conditions 82
Comparative Evaluation Across Interviews: Face-To-Face, WoZ, and Automatic Interaction with the SimSensei VH Agent 85
Conclusions 89
References 90
4 Virtual Affective Agents and Therapeutic Games 96
Introduction 96
Brief History of Virtual Affective Agents and Serious Games 99
Virtual Affective Agents 99
Serious Games 102
State of the Art 103
Nonverbal Interaction: Emotion Recognition, Emotion Expression, and Agent Embodiment 104
Emotion Recognition and Expression 104
Embodiment 106
Believability, Affective Realism, and Emotional Intelligence 107
Personalization and Adaptation Capabilities 108
Putting It All Together: Agent Architectures 109
Nonplaying Characters and Player Avatars 112
Affective and Affect-Adaptive Gaming 113
Overview of Recent Applications in Health Care 113
Pediatric Pain Management – “Free Dive” 115
OCD in Children – “Ricky and the Spider” 116
PTSD in War Veterans – “Virtual Iraq” 116
Social and Emotion Regulation Skills for Children on the Autism Spectrum – “Secret Agent Society” 117
Applicable Ethical and Privacy Considerations 119
Affective Privacy 119
Emotion Induction 120
Virtual Relationships 121
Future Prospects 122
Proliferation 123
Formal Evaluations 123
Improved Understanding of Suitable Applications and Contexts for Agents and Games 123
Agents: Empathy and Personality 124
Improved User State Recognition, Affective User Modeling and Personalization 124
Natural Language Understanding 125
New Types of Relationships and Improved Understanding of Relationships 125
Conclusions 126
References 126
5 Automated Mental State Detection for Mental Health Care 132
Introduction 132
Theoretical and Technical Foundation 134
Example Systems 137
Affective States 138
Basic Emotions 138
Nonbasic Emotions 139
Affect Dimensions 140
Attentional Lapses (Mind Wandering) 141
Pain 143
Depression 143
Stress 144
Concluding Remarks 146
Acknowledgments 147
References 147
6 Intelligent Mobile, Wearable, and Ambient Technologies for Behavioral Health Care 152
Introduction 152
Overview of Intelligent Mobile Health 153
Intelligent Capabilities for Mobile Health 154
Mobile Sensors 155
Example Applications of Multi-Modal Sensor Technology in Smart Mobile Health 157
Speech Recognition and NLP 158
Examples of Speech Recognition Technologies in Health Care 159
Virtual Humans on Mobile Platforms 160
Examples of Virtual Human Health Interventions on Mobile Platforms 162
Augmented Reality on Mobile Devices 163
Example AR Applications in Behavioral Health Care 163
Summary 164
Overview of AmI 165
Example of AmI for Behavioral Health Applications 165
The Internet of Things 167
Design Recommendations 167
Mobile Health Design Considerations 168
AmI Design Considerations 169
Privacy, Data Security, and Ethics Considerations 170
Conclusion 171
References 172
7 Artificial Intelligence and Human Behavior Modeling and Simulation for Mental Health Conditions 178
Introduction 178
Background 178
Why ABMS and Challenges 181
History of ABMS in Medicine/Mental Health Care 181
Synergies with Other Industries 182
Sociological Inputs into Multi-Tiered ABMS 182
Antonovksy’s Salutogenesis Model 182
Theory of Reasoned Action 183
Social Factor Impact on Readmission or Mortality 183
A Toolbox for Multi-Layer Modeling of Social Systems 184
Agent Mind–Body Level: The PMFserv Architecture 184
Agent Motives 185
Agent State Properties 185
Organizational Level: StateSim 187
Societal Level: StateSim 189
Data and Privacy Constraints for ABMS in Mental Health Modeling Applications 190
National Data Sources 191
National Survey on Drug Use and Health (NSDUH) 191
Local Data Sources 191
Penn Data Warehouse 191
US Census and GIS Data 192
Example Application 192
Agent/Individual Modeling 193
Organization Modeling 193
Population Modeling 194
Future Prospects 195
Conclusion 196
Acknowledgments 196
References 197
8 Robotics Technology in Mental Health Care 200
Introduction 200
Background 201
Human–Robot Interaction 201
Robot Morphology 201
Robot Capabilities 203
Robot Autonomy 203
Recent Applications 204
Autism Spectrum Disorders 204
Activity Engagement and Physical Exercise 205
Dementia and Age-Related Cognitive Decline 206
Companion Robots to Improve Psychosocial Outcomes 207
Clinician Training for Interacting with People with Disabilities 207
Diagnosing and Studying Schizophrenia 209
Design Issues 209
Potential Barriers to Provider Technology Adoption 209
Cultural Barriers to Technology Adoption 210
A Need for Evidence-Based Robotics Use in Mental Health Care 211
Ethical Issues 212
The Prime Directive 212
Specific Principles 213
Human Dignity Considerations 213
Design Considerations 213
Legal Considerations 213
Social Considerations 213
Conclusion 214
Acknowledgment 215
References 215
9 Public Health Surveillance: Predictive Analytics and Big Data 220
Introduction 220
The Current State of Informatics 221
Overview of Recent Applications 221
Data Workflow 222
The Durkheim Project 224
Background 225
Related Work 226
Overview 226
Results 230
Implications 230
Larger Cohorts (Current Work) 233
The Durkheim Project Architecture 233
Impact 235
Applicable Ethical Considerations 237
Consent 238
Privacy 238
Transparency 238
Discussion 239
Future Prospects in the Topic Area 239
Next-Generation Inference 239
Approaches in Deep Learning 241
Limitations of Deep Learning 241
Latest Extensions of Deep Learning 241
Technical Steps, Challenges, and Risks 242
Deep Learning, a Discussion 242
Conclusion 243
References 243
10 Artificial Intelligence in Public Health Surveillance and Research 246
Introduction 246
Living in a Dark Tunnel of Pain: Automatic Screening for Depression 248
Neurodegenerative Diseases and the e-Health Challenge 254
Mass Shooters and the Challenge of Screening for Harmful Offenders 257
Vectorial Semantics and Personality Analysis 259
Analyzing the Texts of the Mass Shooters 261
Preprocessing of the Text and Analysis 262
Analysis and Results 264
The Screening Procedure 264
Conclusion 266
References 267
11 Ethical Issues and Artificial Intelligence Technologies in Behavioral and Mental Health Care 270
Introduction 270
Overview of Ethics Codes and Ethical Behavior in Health Care 271
Background 271
Technology-Associated Ethics Codes and Guidelines 273
Particular Ethics Challenges 276
Therapeutic Relationships and Emotional Reactions 276
Competence of Intelligent Machines 278
Patient Safety 280
Respect of Privacy and Trust 281
Deception and Appearance 283
Responsibility 283
Design and Testing Recommendations 285
Ethical (Moral) Turing Test 285
GenEth: A General Ethical Dilemma Analyzer 286
What Can Ethical Machines Teach Us? 288
Conclusion 289
References 290
Glossary 292
Index 296
Back Cover 309

Textauszug

Chapter 2

Expert Systems in Mental Health Care

AI Applications in Decision-Making and Consultation

Casey C. Bennett1,2 and Thomas W. Doub2,    1School of Informatics and Computing, Indiana University, Bloomington, IN, USA,    2Department of Informatics, Centerstone Research Institute, Nashville, TN, USA

Artificial intelligence (AI) based tools hold potential to extend the current capabilities of clinicians, to deal with complex problems and ever-expanding information streams that stretch the limits of human ability. In contrast to previous generations of AI and expert systems, these approaches are increasingly dynamical and less computationalist – less about “rules” and more about leveraging the dynamic interplay of action and observation over time. The (treatment) choices we make change what we observe (clinically, or otherwise), which changes future choices, which affects future observations, and so forth. As humans (clinicians or otherwise), we leverage this fact every day to act “intelligently” in our environment. To best assist us, our clinical computing tools should approximate the same process. Such an approach ties to future developments across the broader healthcare space, e.g., cognitive computing, smart homes, and robotics.

Keywords

Artificial intelligence; medical decision-making; expert systems; mental health; health care; clinical decision support systems; cognition; cognitive computing; temporal modeling; dynamical systems

Introduction

Across real-world scenarios – clinical ones included – perceptions (e.g., observations) and actions (e.g., treatments) are structured in a circular fashion (Merleau-Ponty, 1945). The actions we take change what we perceive in the future, and in turn those perceptions may alter the future actions we take (isomorphic to changes in the human visual system due to movement in the world (Gibson, 1979)). There is information inherent in this dynamical process. As humans we leverage this fact every day to act “intelligently” in our environment. We think about problems in a temporally extended fashion, whether it be during treatment of a patient or making a left turn in our car. For instance, when driving a car we don’t simply decide to make a left turn and then do it. Rather, there is constant perceptual feedback (e.g., if a pedestrian suddenly appears in the crosswalk, we adjust our actions). This alters further perceptions; we may alter our turning radius to avoid said pedestrian, which results in finding a fire hydrant directly in our path. Given the probability of such a sequence (e.g., how busy the pedestrian traffic is at the crosswalk), we may choose not to turn at the intersection at all, or find an alternate route. The point is that our actions lead to certain perceptions that we use to make decisions. The same is true for clinical decision-making. We are not merely passive observers of “data” – data is a process of interaction. Should not our clinical computing tools approximate the same process? If we want tools to enhance our cognition and/or improve our decision-making, those tools need to fit the way we think about the world. In other words, they should provide a sort of cognitive scaffolding that enables people to do what they do better (Clark, 2004, 2013; Sterelny, 2007).

In this chapter, we describe emerging approaches for doing exactly that, i.e., temporal modeling. Such approaches are ripe for application to health care, where treatment decisions must be made over time, and where continually reevaluating ongoing treatment is critical to optimizing clinical care for individual patients. This is especially true for chronic health conditions, such as mental illness, which forms the bulk of healthcare expenditures in the United States (Orszag & Ellis, 2007). Clinicians do not just make decisions and move forward – rather they are constantly reevaluating those decisions, titrating medications, adjusting treatments, making new observations. It is a very dynamic process, both in terms of the treatment being delivered as well as the cognitive processes of the clinician and patient (e.g., how they integrate information into their decision-making over time (Patel, Kaufman, & Arocha, 2002)). This represents a major obstacle, because currently much of the focus of AI and clinical decision support systems (CDSS) in healthcare is on making a single recommendation at a single timepoint. But that is not how health care really works.

This chapter is laid out as follows. “The History – Expert Systems and Clinical Artificial Intelligence in Health Care” provides a brief history of AI, expert systems, and CDSS in physical and mental healthcare. The successes and failures of such efforts lead into “The Present – Dynamical Approaches to Clinical AI and Expert Systems,” where we discuss current research around artificial intelligence in healthcare, including dynamical approaches that explicitly incorporate time. In “The Future,” we expand upon this current work to detail future directions around how such approaches can integrate into the broader healthcare space, for example, cognitive computing, smart homes, cyborg clinicians, and robotics. We conclude with a discussion of what this all may mean for the future of health care, mental health, and clinicians and patients alike. AI is a term often loosely applied, with “intelligence” being more of a romantic notion than a precise descriptor (Brooks, 1991). But all is not lost. The aim of this chapter is to help readers understand where we have been, where we are, and where we are going in our ongoing quest to put “intelligence” into AI, for clinical applications and beyond.

The History – Expert Systems and Clinical Artificial Intelligence in Health Care

Efforts to develop AI, both within and outside of health care, have a long history. Some of the earliest successful applications of AI in health care were expert systems (Jackson, 1998; Luxton, 2014). An expert system is a computer system that is designed to emulate the decision-making capabilities and performance of human experts. Traditionally, this was done by eliciting a knowledge base of rules from experts (knowledge base), from which inference about the present state or future could be performed (inference engine) by an end-user (via a user interface), as shown in Figure 2.1. The rules often took the form of “if-then”, where the “then” component typically comprised a probability. For instance, if the patient has symptom x, then the probability of disease y is, say, 0.6. A multitude of such rules could then be used to calculate probabilistic recommendations.

Figure 2.1 Basic outline of an expert system.

One well-known early example of an expert system in health care was MYCIN, developed in the 1970s at Stanford University. The system was designed to identify bacterial infections and recommend appropriate antibiotic treatment (Shortliffe, 1976). Similar developments were also underway at the same time on the mental health side. For instance, DIAGNO was an early tool for computer-assisted psychiatric diagnosis that was developed at Columbia University in the 1960s and 1970s. It used as input 39 clinical-observation scores processed through a decision tree, resulting in a differential diagnosis. The system achieved comparable performance as human clinicians across a variety of mental disorders (Spitzer & Endicott, 1974), though it was never put to use in real-world practice.

Subsequent years saw the inclusion of expert systems into many CDSS. Decision support, as the name implies, refers to providing information to clinicians, typically at the point of decision-making (Osheroff et al., 2007). However, we should be careful to point out that not all CDSS tools are necessarily expert systems or AI – many are simply hard-coded rules that trigger alerts or messages, containing neither probabilistic rules nor inferential reasoning. Nonetheless, some CDSS tools do embody principles of expert systems. One recent example in mental health care is the TMAP project from UT-Southwestern Medical School for computer-assisted depression medication treatment (Shelton & Trivedi, 2011; Trivedi et al., 2009; Trivedi, Kern, Grannemann, Altshuler, & Sunderajan, 2004). The system used algorithms to suggest appropriate changes to medications and/or dosing via electronic health record systems. It worked well in research studies, though it faced various implementation challenges in practice (see “Ethics and Challenges” section below).

CDSS tools – both those based on expert system models and otherwise – have had a mixed history of success (Garg et al., 2005; Jaspers, Smeulers, Vermeulen, & Peute, 2011; Kawamoto, Houlihan, Balas, & Lobach, 2005). Many are based on evidence-based guidelines (typically derived from expert opinion or statistical averages) that prescribe a one-size-fits-all treatment regimen for every patient, or a standardized sequence of treatment options (Bauer, 2002; Bennett, Doub, & Selove, 2012; Green, 2008). However, real-world patients display individualized characteristics and symptoms that impact treatment effectiveness. As such, clinicians quickly learn to ignore recommendations that say...

Produkt-Details

• Erscheint lt. Verlag: 10.9.2015
• Sprache: englisch
• Themenwelt: Geisteswissenschaften ► Psychologie ► Allgemeine Psychologie
• Themenwelt: Geisteswissenschaften ► Psychologie ► Klinische Psychologie
• Themenwelt: Informatik ► Software Entwicklung ► User Interfaces (HCI)
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Medizin / Pharmazie ► Gesundheitsfachberufe
• ISBN-13: 9780128007921 / 9780128007921