1
|
de Gonzalo-Calvo D, Karaduzovic-Hadziabdic K, Dalgaard LT, Dieterich C, Perez-Pons M, Hatzigeorgiou A, Devaux Y, Kararigas G. Machine learning for catalysing the integration of noncoding RNA in research and clinical practice. EBioMedicine 2024; 106:105247. [PMID: 39029428 DOI: 10.1016/j.ebiom.2024.105247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 06/17/2024] [Accepted: 07/02/2024] [Indexed: 07/21/2024] Open
Abstract
The human transcriptome predominantly consists of noncoding RNAs (ncRNAs), transcripts that do not encode proteins. The noncoding transcriptome governs a multitude of pathophysiological processes, offering a rich source of next-generation biomarkers. Toward achieving a holistic view of disease, the integration of these transcripts with clinical records and additional data from omic technologies ("multiomic" strategies) has motivated the adoption of artificial intelligence (AI) approaches. Given their intricate biological complexity, machine learning (ML) techniques are becoming a key component of ncRNA-based research. This article presents an overview of the potential and challenges associated with employing AI/ML-driven approaches to identify clinically relevant ncRNA biomarkers and to decipher ncRNA-associated pathogenetic mechanisms. Methodological and conceptual constraints are discussed, along with an exploration of ethical considerations inherent to AI applications for healthcare and research. The ultimate goal is to provide a comprehensive examination of the multifaceted landscape of this innovative field and its clinical implications.
Collapse
Affiliation(s)
- David de Gonzalo-Calvo
- Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain.
| | | | | | - Christoph Dieterich
- Klaus Tschira Institute for Integrative Computational Cardiology and Department of Internal Medicine III, University Hospital Heidelberg, Germany; German Center for Cardiovascular Research (DZHK) - Partner Site Heidelberg/Mannheim, Germany
| | - Manel Perez-Pons
- Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain
| | - Artemis Hatzigeorgiou
- DIANA-Lab, Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece; Hellenic Pasteur Institute, Athens, Greece
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Georgios Kararigas
- Department of Physiology, Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
| |
Collapse
|
2
|
Graham Y, Spencer AE, Velez GE, Herbell K. Engaging Youth Voice and Family Partnerships to Improve Children's Mental Health Outcomes. Child Adolesc Psychiatr Clin N Am 2024; 33:343-354. [PMID: 38823808 DOI: 10.1016/j.chc.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/03/2024]
Abstract
Promoting active participation of families and youth in mental health systems of care is the cornerstone of creating a more inclusive, effective, and responsive care network. This article focuses on the inclusion of parent and youth voice in transforming our mental health care system to promote increased engagement at all levels of service delivery. Youth and parent peer support delivery models, digital innovation, and technology not only empower the individuals involved, but also have the potential to enhance the overall efficacy of the mental health care system.
Collapse
Affiliation(s)
- Yolanda Graham
- Morehouse School of Medicine, Devereux Advanced Behavioral Health, 444 Devereux Drive, Villanova, PA 19085, USA.
| | - Andrea E Spencer
- Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, 225 East Chicago Avenue, Chicago, IL 60611, USA
| | - German E Velez
- New York-Presbyterian Hospital, Weill Cornell Medical College/ Columbia University College of Physicians and Surgeons, 525 E. 68th Street, Box 140, New York, NY 10065, USA
| | - Kayla Herbell
- Martha S. Pitzer Center for Women, Children, and Youth, The Ohio State University, 1577 Neil Avenue, Columbus, OH 43210, USA
| |
Collapse
|
3
|
Walsh CG, Ripperger MA, Novak L, Reale C, Anders S, Spann A, Kolli J, Robinson K, Chen Q, Isaacs D, Acosta LMY, Phibbs F, Fielstein E, Wilimitis D, Musacchio Schafer K, Hilton R, Albert D, Shelton J, Stroh J, Stead WW, Johnson KB. Randomized Controlled Comparative Effectiveness Trial of Risk Model-Guided Clinical Decision Support for Suicide Screening. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.14.24304318. [PMID: 38562678 PMCID: PMC10984050 DOI: 10.1101/2024.03.14.24304318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Suicide prevention requires risk identification, appropriate intervention, and follow-up. Traditional risk identification relies on patient self-reporting, support network reporting, or face-to-face screening with validated instruments or history and physical exam. In the last decade, statistical risk models have been studied and more recently deployed to augment clinical judgment. Models have generally been found to be low precision or problematic at scale due to low incidence. Few have been tested in clinical practice, and none have been tested in clinical trials to our knowledge. Methods We report the results of a pragmatic randomized controlled trial (RCT) in three outpatient adult Neurology clinic settings. This two-arm trial compared the effectiveness of Interruptive and Non-Interruptive Clinical Decision Support (CDS) to prompt further screening of suicidal ideation for those predicted to be high risk using a real-time, validated statistical risk model of suicide attempt risk, with the decision to screen as the primary end point. Secondary outcomes included rates of suicidal ideation and attempts in both arms. Manual chart review of every trial encounter was used to determine if suicide risk assessment was subsequently documented. Results From August 16, 2022, through February 16, 2023, our study randomized 596 patient encounters across 561 patients for providers to receive either Interruptive or Non-Interruptive CDS in a 1:1 ratio. Adjusting for provider cluster effects, Interruptive CDS led to significantly higher numbers of decisions to screen (42%=121/289 encounters) compared to Non-Interruptive CDS (4%=12/307) (odds ratio=17.7, p-value <0.001). Secondarily, no documented episodes of suicidal ideation or attempts occurred in either arm. While the proportion of documented assessments among those noting the decision to screen was higher for providers in the Non-Interruptive arm (92%=11/12) than in the Interruptive arm (52%=63/121), the interruptive CDS was associated with more frequent documentation of suicide risk assessment (63/289 encounters compared to 11/307, p-value<0.001). Conclusions In this pragmatic RCT of real-time predictive CDS to guide suicide risk assessment, Interruptive CDS led to higher numbers of decisions to screen and documented suicide risk assessments. Well-powered large-scale trials randomizing this type of CDS compared to standard of care are indicated to measure effectiveness in reducing suicidal self-harm. ClinicalTrials.gov Identifier: NCT05312437.
Collapse
|
4
|
Thakkar A, Gupta A, De Sousa A. Artificial intelligence in positive mental health: a narrative review. Front Digit Health 2024; 6:1280235. [PMID: 38562663 PMCID: PMC10982476 DOI: 10.3389/fdgth.2024.1280235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
The paper reviews the entire spectrum of Artificial Intelligence (AI) in mental health and its positive role in mental health. AI has a huge number of promises to offer mental health care and this paper looks at multiple facets of the same. The paper first defines AI and its scope in the area of mental health. It then looks at various facets of AI like machine learning, supervised machine learning and unsupervised machine learning and other facets of AI. The role of AI in various psychiatric disorders like neurodegenerative disorders, intellectual disability and seizures are discussed along with the role of AI in awareness, diagnosis and intervention in mental health disorders. The role of AI in positive emotional regulation and its impact in schizophrenia, autism spectrum disorders and mood disorders is also highlighted. The article also discusses the limitations of AI based approaches and the need for AI based approaches in mental health to be culturally aware, with structured flexible algorithms and an awareness of biases that can arise in AI. The ethical issues that may arise with the use of AI in mental health are also visited.
Collapse
|
5
|
Trinkley KE, An R, Maw AM, Glasgow RE, Brownson RC. Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions. Implement Sci 2024; 19:17. [PMID: 38383393 PMCID: PMC10880216 DOI: 10.1186/s13012-024-01346-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. MAIN TEXT This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. CONCLUSIONS Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.
Collapse
Affiliation(s)
- Katy E Trinkley
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Colorado Center for Personalized Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Ruopeng An
- Brown School and Division of Computational and Data Sciences at Washington University in St. Louis, St. Louis, MO, USA
| | - Anna M Maw
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- School of Medicine, Division of Hospital Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Russell E Glasgow
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Adult and Child Center for Outcomes Research and Delivery Science Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ross C Brownson
- Prevention Research Center, Brown School at Washington University in St. Louis, St. Louis, MO, USA
- Department of Surgery, Division of Public Health Sciences, and Alvin J. Siteman Cancer Center, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
6
|
Walsh CG, Ripperger MA, Hu Y, Sheu YH, Lee H, Wilimitis D, Zheutlin AB, Rocha D, Choi KW, Castro VM, Kirchner HL, Chabris CF, Davis LK, Smoller JW. Development and multi-site external validation of a generalizable risk prediction model for bipolar disorder. Transl Psychiatry 2024; 14:58. [PMID: 38272862 PMCID: PMC10810911 DOI: 10.1038/s41398-023-02720-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 11/29/2023] [Accepted: 12/15/2023] [Indexed: 01/27/2024] Open
Abstract
Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Network across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and valid with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82-0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Network website.
Collapse
Affiliation(s)
- Colin G Walsh
- Vanderbilt University Medical Center Health System, Nashville, TN, USA.
| | | | - Yirui Hu
- Geisinger Health System, Danville, PA, USA
| | - Yi-Han Sheu
- Massachusetts General-Brigham Health System, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Hyunjoon Lee
- Vanderbilt University Medical Center Health System, Nashville, TN, USA
| | - Drew Wilimitis
- Vanderbilt University Medical Center Health System, Nashville, TN, USA
| | | | | | - Karmel W Choi
- Massachusetts General-Brigham Health System, Boston, MA, USA
| | - Victor M Castro
- Massachusetts General-Brigham Health System, Boston, MA, USA
| | | | | | - Lea K Davis
- Vanderbilt University Medical Center Health System, Nashville, TN, USA
| | - Jordan W Smoller
- Massachusetts General-Brigham Health System, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
7
|
Popp Z, Low S, Igwe A, Rahman MS, Kim M, Khan R, Oh E, Kumar A, De Anda‐Duran I, Ding H, Hwang PH, Sunderaraman P, Shih LC, Lin H, Kolachalama VB, Au R. Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research. J Am Heart Assoc 2024; 13:e031247. [PMID: 38226518 PMCID: PMC10926806 DOI: 10.1161/jaha.123.031247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.
Collapse
Affiliation(s)
- Zachary Popp
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Spencer Low
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Akwaugo Igwe
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Md Salman Rahman
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Minzae Kim
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Raiyan Khan
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Emily Oh
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ankita Kumar
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ileana De Anda‐Duran
- Department of EpidemiologyTulane University School of Public Health & Tropical MedicineNew OrleansLAUSA
| | - Huitong Ding
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Phillip H. Hwang
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Preeti Sunderaraman
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Ludy C. Shih
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMA
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Rhoda Au
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| |
Collapse
|
8
|
Koch E, Pardiñas AF, O'Connell KS, Selvaggi P, Camacho Collados J, Babic A, Marshall SE, Van der Eycken E, Angulo C, Lu Y, Sullivan PF, Dale AM, Molden E, Posthuma D, White N, Schubert A, Djurovic S, Heimer H, Stefánsson H, Stefánsson K, Werge T, Sønderby I, O'Donovan MC, Walters JTR, Milani L, Andreassen OA. How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry. Biol Psychiatry 2024:S0006-3223(24)00003-9. [PMID: 38185234 DOI: 10.1016/j.biopsych.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/20/2023] [Accepted: 01/02/2024] [Indexed: 01/09/2024]
Abstract
Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.
Collapse
Affiliation(s)
- Elise Koch
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pierluigi Selvaggi
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - José Camacho Collados
- CardiffNLP, School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | | | | | - Erik Van der Eycken
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Cecilia Angulo
- Global Alliance of Mental Illness Advocacy Networks-Europe, Brussels, Belgium
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden; Departments of Genetics and Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, California; Departments of Radiology, Psychiatry, and Neurosciences, University of California, San Diego, La Jolla, California
| | - Espen Molden
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nathan White
- CorTechs Laboratories, Inc., San Diego, California
| | | | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; The Norwegian Centre for Mental Disorders Research Centre, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Hakon Heimer
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Nordic Society of Human Genetics and Precision Medicine, Copenhagen, Denmark
| | | | | | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark; Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark; Lundbeck Foundation GeoGenetics Centre, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Ida Sønderby
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Michael C O'Donovan
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - James T R Walters
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Lili Milani
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia; Genetics and Personalized Medicine Clinic, Tartu University Hospital, Tartu, Estonia
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway.
| |
Collapse
|
9
|
Ueda D, Kakinuma T, Fujita S, Kamagata K, Fushimi Y, Ito R, Matsui Y, Nozaki T, Nakaura T, Fujima N, Tatsugami F, Yanagawa M, Hirata K, Yamada A, Tsuboyama T, Kawamura M, Fujioka T, Naganawa S. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 2024; 42:3-15. [PMID: 37540463 PMCID: PMC10764412 DOI: 10.1007/s11604-023-01474-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/17/2023] [Indexed: 08/05/2023]
Abstract
In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.
Collapse
Affiliation(s)
- Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-ku, Osaka, 545-8585, Japan.
| | | | - Shohei Fujita
- Department of Radiology, University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-ku, Okayama, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-ku, Kumamoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-ku, Hiroshima, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita-ku, Sapporo, Hokkaido, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| |
Collapse
|
10
|
Scheibein F, Caballeria E, Taher MA, Arya S, Bancroft A, Dannatt L, De Kock C, Chaudhary NI, Gayo RP, Ghosh A, Gelberg L, Goos C, Gordon R, Gual A, Hill P, Jeziorska I, Kurcevič E, Lakhov A, Maharjan I, Matrai S, Morgan N, Paraskevopoulos I, Puharić Z, Sibeko G, Stola J, Tiburcio M, Tay Wee Teck J, Tsereteli Z, López-Pelayo H. Optimizing Digital Tools for the Field of Substance Use and Substance Use Disorders: Backcasting Exercise. JMIR Hum Factors 2023; 10:e46678. [PMID: 38085569 PMCID: PMC10751634 DOI: 10.2196/46678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/14/2023] [Accepted: 08/12/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Substance use trends are complex; they often rapidly evolve and necessitate an intersectional approach in research, service, and policy making. Current and emerging digital tools related to substance use are promising but also create a range of challenges and opportunities. OBJECTIVE This paper reports on a backcasting exercise aimed at the development of a roadmap that identifies values, challenges, facilitators, and milestones to achieve optimal use of digital tools in the substance use field by 2030. METHODS A backcasting exercise method was adopted, wherein the core elements are identifying key values, challenges, facilitators, milestones, cornerstones and a current, desired, and future scenario. A structured approach was used by means of (1) an Open Science Framework page as a web-based collaborative working space and (2) key stakeholders' collaborative engagement during the 2022 Lisbon Addiction Conference. RESULTS The identified key values were digital rights, evidence-based tools, user-friendliness, accessibility and availability, and person-centeredness. The key challenges identified were ethical funding, regulations, commercialization, best practice models, digital literacy, and access or reach. The key facilitators identified were scientific research, interoperable infrastructure and a culture of innovation, expertise, ethical funding, user-friendly designs, and digital rights and regulations. A range of milestones were identified. The overarching identified cornerstones consisted of creating ethical frameworks, increasing access to digital tools, and continuous trend analysis. CONCLUSIONS The use of digital tools in the field of substance use is linked to a range of risks and opportunities that need to be managed. The current trajectories of the use of such tools are heavily influenced by large multinational for-profit companies with relatively little involvement of key stakeholders such as people who use drugs, service providers, and researchers. The current funding models are problematic and lack the necessary flexibility associated with best practice business approaches such as lean and agile principles to design and execute customer discovery methods. Accessibility and availability, digital rights, user-friendly design, and person-focused approaches should be at the forefront in the further development of digital tools. Global legislative and technical infrastructures by means of a global action plan and strategy are necessary and should include ethical frameworks, accessibility of digital tools for substance use, and continuous trend analysis as cornerstones.
Collapse
Affiliation(s)
- Florian Scheibein
- School of Health Sciences, South East Technological University, Waterford, Ireland
| | - Elsa Caballeria
- Health and Addictions Research Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Md Abu Taher
- United Nations Office of Drugs and Crime, Dhaka, Bangladesh
| | - Sidharth Arya
- Institute of Mental Health, Pandit Bhagwat Dayal Sharma University of Health Sciences, Rohtak, India
| | - Angus Bancroft
- School of Social and Political Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Lisa Dannatt
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Charlotte De Kock
- Institute for Social Drug Research, Ghent University, Ghent, Belgium
| | - Nazish Idrees Chaudhary
- International Grace Rehab, Lahore School of Behavioral Sciences, The University of Lahore, Lahore, Pakistan
| | | | - Abhishek Ghosh
- Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Lillian Gelberg
- Department of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Cees Goos
- European Centre for Social Welfare Policy and Research, Vienna, Austria
| | - Rebecca Gordon
- Health and Addictions Research Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Antoni Gual
- Health and Addictions Research Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Penelope Hill
- The National Centre for Clinical Research on Emerging Drugs, Randwick, Australia
- The National Drug and Alcohol Research Centre, University of New South Wales, Randwick, Australia
- National Drug Research Institute, Curtin University, Melbourne, Australia
| | - Iga Jeziorska
- Correlation European Harm Reduction Network, Amsterdam, Netherlands
- Department of Public Policy, Institute of Social and Political Sciences, Corvinus University of Budapest, Budapest, Hungary
| | | | - Aleksey Lakhov
- Humanitarian Action Charitable Fund, St Petersburg, Russian Federation
| | | | - Silvia Matrai
- Health and Addictions Research Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Nirvana Morgan
- Network of Early Career Professionals in Addiction Medicine, Seligenstadt, Germany
| | | | - Zrinka Puharić
- Faculty of Dental Medicine and Health Osijek, Bjelovar University of Applied Sciences, Bjelovar, Croatia
| | - Goodman Sibeko
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Jan Stola
- Youth Organisations for Drug Action, Warsaw, Poland
| | - Marcela Tiburcio
- Head of the Department of Social Sciences in Health, Directorate of Epidemiological and Psychosocial Research, Mexico City, Mexico
| | - Joseph Tay Wee Teck
- DigitAS Project, Population and Behavioural Science, School of Medicine, University of St. Andrews, St Andrews, United Kingdom
| | - Zaza Tsereteli
- Alcohol and Substance Use Expert Group, Northern Dimension Partnership in Public Health and Social Well-Being, Tallinn, Estonia
| | - Hugo López-Pelayo
- Health and Addictions Research Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| |
Collapse
|
11
|
Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. J Med Internet Res 2023; 25:e44502. [PMID: 37792430 PMCID: PMC10585447 DOI: 10.2196/44502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 07/10/2023] [Accepted: 08/21/2023] [Indexed: 10/05/2023] Open
Abstract
The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.
Collapse
Affiliation(s)
- Antoine Oudin
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Redwan Maatoug
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Alexis Bourla
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
- Medical Strategy and Innovation Department, Clariane, Paris, France
- NeuroStim Psychiatry Practice, Paris, France
| | - Florian Ferreri
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Olivier Bonnot
- Department of Child and Adolescent Psychiatry, Nantes University Hospital, Nantes, France
- Pays de la Loire Psychology Laboratory, Nantes University, Nantes, France
| | - Bruno Millet
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Félix Schoeller
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Stéphane Mouchabac
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Vladimir Adrien
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| |
Collapse
|
12
|
Fraile Navarro D, Ijaz K, Rezazadegan D, Rahimi-Ardabili H, Dras M, Coiera E, Berkovsky S. Clinical named entity recognition and relation extraction using natural language processing of medical free text: A systematic review. Int J Med Inform 2023; 177:105122. [PMID: 37295138 DOI: 10.1016/j.ijmedinf.2023.105122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 04/14/2023] [Accepted: 06/03/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Natural Language Processing (NLP) applications have developed over the past years in various fields including its application to clinical free text for named entity recognition and relation extraction. However, there has been rapid developments the last few years that there's currently no overview of it. Moreover, it is unclear how these models and tools have been translated into clinical practice. We aim to synthesize and review these developments. METHODS We reviewed literature from 2010 to date, searching PubMed, Scopus, the Association of Computational Linguistics (ACL), and Association of Computer Machinery (ACM) libraries for studies of NLP systems performing general-purpose (i.e., not disease- or treatment-specific) information extraction and relation extraction tasks in unstructured clinical text (e.g., discharge summaries). RESULTS We included in the review 94 studies with 30 studies published in the last three years. Machine learning methods were used in 68 studies, rule-based in 5 studies, and both in 22 studies. 63 studies focused on Named Entity Recognition, 13 on Relation Extraction and 18 performed both. The most frequently extracted entities were "problem", "test" and "treatment". 72 studies used public datasets and 22 studies used proprietary datasets alone. Only 14 studies defined clearly a clinical or information task to be addressed by the system and just three studies reported its use outside the experimental setting. Only 7 studies shared a pre-trained model and only 8 an available software tool. DISCUSSION Machine learning-based methods have dominated the NLP field on information extraction tasks. More recently, Transformer-based language models are taking the lead and showing the strongest performance. However, these developments are mostly based on a few datasets and generic annotations, with very few real-world use cases. This may raise questions about the generalizability of findings, translation into practice and highlights the need for robust clinical evaluation.
Collapse
Affiliation(s)
- David Fraile Navarro
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
| | - Kiran Ijaz
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Dana Rezazadegan
- Department of Computer Science and Software Engineering. School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia
| | - Hania Rahimi-Ardabili
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Mark Dras
- Department of Computing, Macquarie University, Sydney, Australia
| | - Enrico Coiera
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Shlomo Berkovsky
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| |
Collapse
|
13
|
Nayebi A, Tipirneni S, Reddy CK, Foreman B, Subbian V. WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values. J Biomed Inform 2023; 144:104438. [PMID: 37414368 PMCID: PMC10552726 DOI: 10.1016/j.jbi.2023.104438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 06/29/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
Unpacking and comprehending how black-box machine learning algorithms (such as deep learning models) make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models, e.g., to determine how different variables and time points influence the clinical outcome. However, existing approaches to explain such models are frequently unique to architectures and data where the features do not have a time-varying component. In this paper, we introduce WindowSHAP, a model-agnostic framework for explaining time-series classifiers using Shapley values. We intend for WindowSHAP to mitigate the computational complexity of calculating Shapley values for long time-series data as well as improve the quality of explanations. WindowSHAP is based on partitioning a sequence into time windows. Under this framework, we present three distinct algorithms of Stationary, Sliding and Dynamic WindowSHAP, each evaluated against baseline approaches, KernelSHAP and TimeSHAP, using perturbation and sequence analyses metrics. We applied our framework to clinical time-series data from both a specialized clinical domain (Traumatic Brain Injury - TBI) as well as a broad clinical domain (critical care medicine). The experimental results demonstrate that, based on the two quantitative metrics, our framework is superior at explaining clinical time-series classifiers, while also reducing the complexity of computations. We show that for time-series data with 120 time steps (hours), merging 10 adjacent time points can reduce the CPU time of WindowSHAP by 80 % compared to KernelSHAP. We also show that our Dynamic WindowSHAP algorithm focuses more on the most important time steps and provides more understandable explanations. As a result, WindowSHAP not only accelerates the calculation of Shapley values for time-series data, but also delivers more understandable explanations with higher quality.
Collapse
Affiliation(s)
- Amin Nayebi
- Department of Systems and Industrial Engineering, University of Arizona, AZ, USA.
| | | | | | | | - Vignesh Subbian
- Department of Systems and Industrial Engineering, University of Arizona, AZ, USA; Department of Biomedical Engineering, University of Arizona, AZ, USA
| |
Collapse
|
14
|
Solans Noguero D, Ramírez-Cifuentes D, Ríssola EA, Freire A. Gender Bias When Using Artificial Intelligence to Assess Anorexia Nervosa on Social Media: Data-Driven Study. J Med Internet Res 2023; 25:e45184. [PMID: 37289496 PMCID: PMC10288345 DOI: 10.2196/45184] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/12/2023] [Accepted: 04/26/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Social media sites are becoming an increasingly important source of information about mental health disorders. Among them, eating disorders are complex psychological problems that involve unhealthy eating habits. In particular, there is evidence showing that signs and symptoms of anorexia nervosa can be traced in social media platforms. Knowing that input data biases tend to be amplified by artificial intelligence algorithms and, in particular, machine learning, these methods should be revised to mitigate biased discrimination in such important domains. OBJECTIVE The main goal of this study was to detect and analyze the performance disparities across genders in algorithms trained for the detection of anorexia nervosa on social media posts. We used a collection of automated predictors trained on a data set in Spanish containing cases of 177 users that showed signs of anorexia (471,262 tweets) and 326 control cases (910,967 tweets). METHODS We first inspected the predictive performance differences between the algorithms for male and female users. Once biases were detected, we applied a feature-level bias characterization to evaluate the source of such biases and performed a comparative analysis of such features and those that are relevant for clinicians. Finally, we showcased different bias mitigation strategies to develop fairer automated classifiers, particularly for risk assessment in sensitive domains. RESULTS Our results revealed concerning predictive performance differences, with substantially higher false negative rates (FNRs) for female samples (FNR=0.082) compared with male samples (FNR=0.005). The findings show that biological processes and suicide risk factors were relevant for classifying positive male cases, whereas age, emotions, and personal concerns were more relevant for female cases. We also proposed techniques for bias mitigation, and we could see that, even though disparities can be mitigated, they cannot be eliminated. CONCLUSIONS We concluded that more attention should be paid to the assessment of biases in automated methods dedicated to the detection of mental health issues. This is particularly relevant before the deployment of systems that are thought to assist clinicians, especially considering that the outputs of such systems can have an impact on the diagnosis of people at risk.
Collapse
Affiliation(s)
- David Solans Noguero
- Telefonica I+D, Telefónica Research, Barcelona, Spain
- Web Science and Social Computing group, Universidad Pompeu Fabra, Barcelona, Spain
| | - Diana Ramírez-Cifuentes
- Web Science and Social Computing group, Universidad Pompeu Fabra, Barcelona, Spain
- Computer Vision Center, Bellaterra (Cerdanyola del Vallès), Spain
| | | | - Ana Freire
- Innovation and Sustainability Data Lab, UPF Barcelona School of Management, Barcelona, Spain
| |
Collapse
|
15
|
Keim-Malpass J, Kausch SL. Data Science and Precision Oncology Nursing: Creating an Analytic Ecosystem to Support Personalized Supportive Care across the Trajectory of Illness. Semin Oncol Nurs 2023; 39:151432. [PMID: 37149440 PMCID: PMC10330746 DOI: 10.1016/j.soncn.2023.151432] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES The authors' objective is to present an overarching framework of an analytic ecosystem using diverse data domains and data science approaches that can be used and implemented across the cancer continuum. Analytic ecosystems can improve quality practices and offer enhanced anticipatory guidance in the era of precision oncology nursing. DATA SOURCES Published scientific articles supporting the development of a novel framework with a case exemplar to provide applied examples of current barriers in data integration and use. CONCLUSION The combination of diverse data sets and data science analytic approaches has the potential to extend precision oncology nursing research and practice. Integration of this framework can be implemented within a learning health system where models can update as new data become available across the continuum of the cancer care trajectory. To date, data science approaches have been underused in extending personalized toxicity assessments, precision supportive care, and enhancing end-of-life care practices. IMPLICATIONS FOR NURSING PRACTICE Nurses and nurse scientists have a unique role in the convergence of data science applications to support precision oncology across the trajectory of illness. Nurses also have specific expertise in supportive care needs that have been dramatically underrepresented in existing data science approaches thus far. They also have a role in centering the patient and family perspectives and needs as these frameworks and analytic capabilities evolve.
Collapse
Affiliation(s)
- Jessica Keim-Malpass
- Associate Professor, Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, Virginia, USA; Member, Center for Advanced Medical Analytics, University of Virginia, Charlottesville, Virginia, USA.
| | - Sherry L Kausch
- Member, Center for Advanced Medical Analytics, University of Virginia, Charlottesville, Virginia, USA; Data scientist, Department of Pediatrics, University of Virginia, Charlottesville, Virginia, USA
| |
Collapse
|
16
|
Ford E, Milne R, Curlewis K. Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2023; 13:e1492. [PMID: 38439952 PMCID: PMC10909482 DOI: 10.1002/widm.1492] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 03/06/2024]
Abstract
Dementia poses a growing challenge for health services but remains stigmatized and under-recognized. Digital technologies to aid the earlier detection of dementia are approaching market. These include traditional cognitive screening tools presented on mobile devices, smartphone native applications, passive data collection from wearable, in-home and in-car sensors, as well as machine learning techniques applied to clinic and imaging data. It has been suggested that earlier detection and diagnosis may help patients plan for their future, achieve a better quality of life, and access clinical trials and possible future disease modifying treatments. In this review, we explore whether digital tools for the early detection of dementia can or should be deployed, by assessing them against the principles of ethical screening programs. We conclude that while the importance of dementia as a health problem is unquestionable, significant challenges remain. There is no available treatment which improves the prognosis of diagnosed disease. Progression from early-stage disease to dementia is neither given nor currently predictable. Available technologies are generally not both minimally invasive and highly accurate. Digital deployment risks exacerbating health inequalities due to biased training data and inequity in digital access. Finally, the acceptability of early dementia detection is not established, and resources would be needed to ensure follow-up and support for those flagged by any new system. We conclude that early dementia detection deployed at scale via digital technologies does not meet standards for a screening program and we offer recommendations for moving toward an ethical mode of implementation. This article is categorized under:Application Areas > Health CareCommercial, Legal, and Ethical Issues > Ethical ConsiderationsTechnologies > Artificial Intelligence.
Collapse
Affiliation(s)
- Elizabeth Ford
- Department of Primary Care and Public HealthBrighton and Sussex Medical SchoolBrightonUK
| | - Richard Milne
- Kavli Centre for Ethics, Science and the PublicUniversity of CambridgeCambridgeUK
- Engagement and SocietyWellcome Connecting ScienceCambridgeUK
| | | |
Collapse
|
17
|
Safarlou CW, Jongsma KR, Vermeulen R, Bredenoord AL. The ethical aspects of exposome research: a systematic review. EXPOSOME 2023; 3:osad004. [PMID: 37745046 PMCID: PMC7615114 DOI: 10.1093/exposome/osad004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
In recent years, exposome research has been put forward as the next frontier for the study of human health and disease. Exposome research entails the analysis of the totality of environmental exposures and their corresponding biological responses within the human body. Increasingly, this is operationalized by big-data approaches to map the effects of internal as well as external exposures using smart sensors and multiomics technologies. However, the ethical implications of exposome research are still only rarely discussed in the literature. Therefore, we conducted a systematic review of the academic literature regarding both the exposome and underlying research fields and approaches, to map the ethical aspects that are relevant to exposome research. We identify five ethical themes that are prominent in ethics discussions: the goals of exposome research, its standards, its tools, how it relates to study participants, and the consequences of its products. Furthermore, we provide a number of general principles for how future ethics research can best make use of our comprehensive overview of the ethical aspects of exposome research. Lastly, we highlight three aspects of exposome research that are most in need of ethical reflection: the actionability of its findings, the epidemiological or clinical norms applicable to exposome research, and the meaning and action-implications of bias.
Collapse
Affiliation(s)
- Caspar W. Safarlou
- Department of Global Public Health and Bioethics, Julius Center for
Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The
Netherlands
| | - Karin R. Jongsma
- Department of Global Public Health and Bioethics, Julius Center for
Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The
Netherlands
| | - Roel Vermeulen
- Department of Global Public Health and Bioethics, Julius Center for
Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The
Netherlands
- Department of Population Health Sciences, Utrecht University,
Utrecht, The Netherlands
| | - Annelien L. Bredenoord
- Department of Global Public Health and Bioethics, Julius Center for
Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The
Netherlands
- Erasmus School of Philosophy, Erasmus University Rotterdam,
Rotterdam, The Netherlands
| |
Collapse
|
18
|
Lyu H, Imtiaz A, Zhao Y, Luo J. Human behavior in the time of COVID-19: Learning from big data. Front Big Data 2023; 6:1099182. [PMID: 37091459 PMCID: PMC10118015 DOI: 10.3389/fdata.2023.1099182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/21/2023] [Indexed: 04/09/2023] Open
Abstract
Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups-using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.
Collapse
Affiliation(s)
| | | | | | - Jiebo Luo
- Department of Computer Science, University of Rochester, Rochester, NY, United States
| |
Collapse
|
19
|
Salerno J, Coughlin SS, Goodman KW, Hlaing WM. Current ethical and social issues in epidemiology. Ann Epidemiol 2023; 80:37-42. [PMID: 36758845 DOI: 10.1016/j.annepidem.2023.02.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/01/2023] [Accepted: 02/01/2023] [Indexed: 02/10/2023]
Abstract
PURPOSE The American College of Epidemiology held its 2021 Annual Meeting virtually, September 8-10, with a conference theme of 'From Womb to Tomb: Insights from Health Emergencies'. The American College of Epidemiology Ethics Committee hosted a symposium session in recognition of the ethical and social challenges brought to light by the coronavirus disease 2019 pandemic and on the occasion of the publication of the third edition of the classic text, Ethics and Epidemiology. The American College of Epidemiology Ethics Committee invited the book editor and contributing authors to present at the symposium session titled 'Current Ethical and Social Issues in Epidemiology.' The purpose of this paper is to further highlight the ethical challenges and presentations. METHODS Three speakers with expertise in ethics, health law, health policy, global health, health information technology, and translational research in epidemiology and public health were selected to present on the social and ethical issues in the current landscape. Dr. S Coughlin presented on the 'Ethical and Social Issues in Epidemiology', Dr. L Beskow presented on 'Ethical Challenges in Genetic Epidemiology', and Dr. K Goodman presented on the 'Ethics of Health Informatics'. RESULTS New digital sources of data and technologies are driving the ethical challenges and opportunities in epidemiology and public health as it relates to the three emerging topic areas identified: (1) digital epidemiology, (2) genetic epidemiology, and (3) health informatics. New complexities such as the reliance on social media to control infectious disease outbreaks and the introduction of computing advancements are requiring re-evaluation of traditional bioethical frameworks for epidemiology research and public health practice. We identified several cross-cutting ethical and social issues related to informed consent, benefits, risks and harms, and privacy and confidentiality and summarized these alongside more nuanced ethical considerations such as algorithmic bias, group harms related to data (mis)representation, risks of misinformation, return of genomic research results, maintaining data security, and data sharing. We offered an integrated synthesis of the stages of epidemiology research planning and conduct with the ethical issues that are most relevant in these emerging topic areas. CONCLUSIONS New realities exist for epidemiology and public health as professional groups who are faced with addressing population health, and especially given the recent pandemic and the widespread use of digital tools and technologies. Many ethical issues can be understood in the context of existing ethical frameworks; however, they have yet to be clearly identified or connected with the new technical and methodological applications of digital tools and technologies currently in use for epidemiology research and public health practice. To address current ethical challenges, we offered a synthesis of traditional ethical principles in public health science alongside more nuanced ethical considerations for emerging technologies and aligned these with lifecycle stages of epidemiology research. By critically reflecting on the impact of new digital sources of data and technologies on epidemiology research and public health practice, specifically in the control of infectious outbreaks, we offered insights on cultivating these new areas of professional growth while striving to improve population health.
Collapse
Affiliation(s)
- Jennifer Salerno
- Department of Family Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada; Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
| | - Steven S Coughlin
- Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA; Institute of Public and Preventive Health, Augusta University, Augusta, GA
| | - Kenneth W Goodman
- Institute for Bioethics and Health Policy, University of Miami Miller School of Medicine, Miami, FL
| | - WayWay M Hlaing
- Division of Epidemiology and Population Sciences, Department of Public Health Sciences, University of Miami Miller School of Medicine, Miami, FL
| |
Collapse
|
20
|
Yarborough BJH, Stumbo SP. A Stakeholder-Informed Ethical Framework to Guide Implementation of Suicide Risk Prediction Models Derived from Electronic Health Records. Arch Suicide Res 2023; 27:704-717. [PMID: 35446244 PMCID: PMC9665102 DOI: 10.1080/13811118.2022.2064255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
OBJECTIVE Develop a stakeholder-informed ethical framework to provide practical guidance to health systems considering implementation of suicide risk prediction models. METHODS In this multi-method study, patients and family members participating in formative focus groups (n = 4 focus groups, 23 participants), patient advisors, and a bioethics consultant collectively informed the development of a web-based survey; survey results (n = 1,357 respondents) and themes from interviews with stakeholders (patients, health system administrators, clinicians, suicide risk model developers, and a bioethicist) were used to draft the ethical framework. RESULTS Clinical, ethical, operational, and technical issues reiterated by multiple stakeholder groups and corresponding questions for risk prediction model adopters to consider prior to and during suicide risk model implementation are organized within six ethical principles in the resulting stakeholder-informed framework. Key themes include: patients' rights to informed consent and choice to conceal or reveal risk (autonomy); appropriate application of risk models, data and model limitations and consequences including ambiguous risk predictors in opaque models (explainability); selecting actionable risk thresholds (beneficence, distributive justice); access to risk information and stigma (privacy); unanticipated harms (non-maleficence); and planning for expertise and resources to continuously audit models, monitor harms, and redress grievances (stewardship). CONCLUSIONS Enthusiasm for risk prediction in the context of suicide is understandable given the escalating suicide rate in the U.S. Attention to ethical and practical concerns in advance of automated suicide risk prediction model implementation may help avoid unnecessary harms that could thwart the promise of this innovation in suicide prevention. HIGHLIGHTSPatients' desire to consent/opt out of suicide risk prediction models.Recursive ethical questioning should occur throughout risk model implementation.Risk modeling resources are needed to continuously audit models and monitor harms.
Collapse
|
21
|
Hartmann KV, Primc N, Rubeis G. Lost in translation? Conceptions of privacy and independence in the technical development of AI-based AAL. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2023; 26:99-110. [PMID: 36348209 PMCID: PMC9984520 DOI: 10.1007/s11019-022-10126-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
AAL encompasses smart home technologies that are installed in the personal living environment in order to support older, disabled, as well as chronically ill people with the goal of delaying or reducing their need for nursing care in a care facility. Artificial intelligence (AI) is seen as an important tool for assisting the target group in their daily lives. A literature search and qualitative content analysis of 255 articles from computer science and engineering was conducted to explore the usage of ethical concepts. From an ethical point of view, the concept of independence and self-determination on the one hand and the possible loss of privacy on the other hand are widely discussed in the context of AAL. These concepts are adopted by the technical discourse in the sense that independence, self-determination and privacy are recognized as important values. Nevertheless, our research shows that these concepts have different usages and meanings in the ethical and the technical discourses. In the paper, we aim to map the different meanings of independence, self-determination and privacy as they can be found in the context of technological research on AI-based AAL systems. It investigates the interpretation of these ethical and social concepts which technicians try to build into AAL systems. In a second step, these interpretations are contextualized with concepts from the ethical discourse on AI-based assistive technologies.
Collapse
Affiliation(s)
- Kris Vera Hartmann
- Institute for History and Ethics of Medicine, Faculty of Medicine, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany.
| | - Nadia Primc
- Institute for History and Ethics of Medicine, Faculty of Medicine, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
| | - Giovanni Rubeis
- Department of General Health Studies, Division Biomedical and Public Health Ethics, Karl Landsteiner Private University for Health Sciences, Krems, Austria
| |
Collapse
|
22
|
Walsh CG, Ripperger MA, Hu Y, Sheu YH, Wilimitis D, Zheutlin AB, Rocha D, Choi KW, Castro VM, Kirchner HL, Chabris CF, Davis LK, Smoller JW. Development and Multi-Site External Validation of a Generalizable Risk Prediction Model for Bipolar Disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.21.23286251. [PMID: 36865341 PMCID: PMC9980254 DOI: 10.1101/2023.02.21.23286251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
Abstract
Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Consortium across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and validated with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82 - 0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Consortium website.
Collapse
|
23
|
Koops S, Brederoo SG, de Boer JN, Nadema FG, Voppel AE, Sommer IE. Speech as a Biomarker for Depression. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2023; 22:152-160. [PMID: 34961469 DOI: 10.2174/1871527320666211213125847] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 10/10/2021] [Accepted: 10/10/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND Depression is a debilitating disorder that at present lacks a reliable biomarker to aid in diagnosis and early detection. Recent advances in computational analytic approaches have opened up new avenues in developing such a biomarker by taking advantage of the wealth of information that can be extracted from a person's speech. OBJECTIVE The current review provides an overview of the latest findings in the rapidly evolving field of computational language analysis for the detection of depression. We cover a wide range of both acoustic and content-related linguistic features, data types (i.e., spoken and written language), and data sources (i.e., lab settings, social media, and smartphone-based). We put special focus on the current methodological advances with regard to feature extraction and computational modeling techniques. Furthermore, we pay attention to potential hurdles in the implementation of automatic speech analysis. CONCLUSION Depressive speech is characterized by several anomalies, such as lower speech rate, less pitch variability and more self-referential speech. With current computational modeling techniques, such features can be used to detect depression with an accuracy of up to 91%. The performance of the models is optimized when machine learning techniques are implemented that suit the type and amount of data. Recent studies now work towards further optimization and generalizability of the computational language models to detect depression. Finally, privacy and ethical issues are of paramount importance to be addressed when automatic speech analysis techniques are further implemented in, for example, smartphones. Altogether, computational speech analysis is well underway towards becoming an effective diagnostic aid for depression.
Collapse
Affiliation(s)
- Sanne Koops
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Sanne G Brederoo
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
- University Center for Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
| | - Janna N de Boer
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University & Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Femke G Nadema
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Alban E Voppel
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| |
Collapse
|
24
|
Rubeis G, Fang ML, Sixsmith A. Equity in AgeTech for Ageing Well in Technology-Driven Places: The Role of Social Determinants in Designing AI-based Assistive Technologies. SCIENCE AND ENGINEERING ETHICS 2022; 28:49. [PMID: 36301408 PMCID: PMC9613787 DOI: 10.1007/s11948-022-00397-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 08/01/2022] [Indexed: 06/16/2023]
Abstract
AgeTech involves the use of emerging technologies to support the health, well-being and independent living of older adults. In this paper we focus on how AgeTech based on artificial intelligence (AI) may better support older adults to remain in their own living environment for longer, provide social connectedness, support wellbeing and mental health, and enable social participation. In order to assess and better understand the positive as well as negative outcomes of AI-based AgeTech, a critical analysis of ethical design, digital equity, and policy pathways is required. A crucial question is how AI-based AgeTech may drive practical, equitable, and inclusive multilevel solutions to support healthy, active ageing.In our paper, we aim to show that a focus on equity is key for AI-based AgeTech if it is to realize its full potential. We propose that equity should not just be an extra benefit or minimum requirement, but the explicit aim of designing AI-based health tech. This means that social determinants that affect the use of or access to these technologies have to be addressed. We will explore how complexity management as a crucial element of AI-based AgeTech may potentially create and exacerbate social inequities by marginalising or ignoring social determinants. We identify bias, standardization, and access as main ethical issues in this context and subsequently, make recommendations as to how inequities that stem form AI-based AgeTech can be addressed.
Collapse
Affiliation(s)
- Giovanni Rubeis
- Department General Health Studies, Division Biomedical and Public Health Ethics, Karl Landsteiner University of Health Sciences, Dr.-Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Mei Lan Fang
- Department General Health Studies, Division Biomedical and Public Health Ethics, Karl Landsteiner University of Health Sciences, Dr.-Karl-Dorrek-Straße 30, 3500 Krems, Austria
- School of Health Sciences, University of Dundee Nethergate, DD1 4HN Dundee Scotland, UK
| | - Andrew Sixsmith
- Department General Health Studies, Division Biomedical and Public Health Ethics, Karl Landsteiner University of Health Sciences, Dr.-Karl-Dorrek-Straße 30, 3500 Krems, Austria
- Department of Gerontology, Simon Fraser University Vancouver Harbour Centre, British Columbia, Room, 2800, V6B 5K3 Vancouver, Canada
| |
Collapse
|
25
|
Fusar-Poli P, Manchia M, Koutsouleris N, Leslie D, Woopen C, Calkins ME, Dunn M, Tourneau CL, Mannikko M, Mollema T, Oliver D, Rietschel M, Reininghaus EZ, Squassina A, Valmaggia L, Kessing LV, Vieta E, Correll CU, Arango C, Andreassen OA. Ethical considerations for precision psychiatry: A roadmap for research and clinical practice. Eur Neuropsychopharmacol 2022; 63:17-34. [PMID: 36041245 DOI: 10.1016/j.euroneuro.2022.08.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 07/04/2022] [Accepted: 08/05/2022] [Indexed: 12/14/2022]
Abstract
Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.
Collapse
Affiliation(s)
- Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy; Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy; Department of Pharmacology, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | | | | | - Monica E Calkins
- Neurodevelopment and Psychosis Section and Lifespan Brain Institute of Penn/CHOP, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, USA
| | - Michael Dunn
- Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore
| | - Christophe Le Tourneau
- Institut Curie, Department of Drug Development and Innovation (D3i), INSERM U900 Research unit, Paris-Saclay University, France
| | - Miia Mannikko
- European Federation of Associations of Families of People with Mental Illness (EUFAMI), Leuven, Belgium
| | - Tineke Mollema
- Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN), Brussels, Belgium
| | - Dominic Oliver
- Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Eva Z Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Medical University of Graz, Graz, Austria
| | - Alessio Squassina
- Section of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, University of Cagliari, Italy
| | - Lucia Valmaggia
- South London and Maudsley NHS Foundation Trust, London, UK; Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK; Department of Psychiatry, KU Leuven, Belgium
| | - Lars Vedel Kessing
- Copenhagen Affective disorder Research Center (CADIC), Psychiatric Center Copenhagen, Denmark; Department of clinical Medicine, University of Copenhagen, Denmark
| | - Eduard Vieta
- Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Christoph U Correll
- The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, NY, USA; Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Center for Psychiatric Neuroscience; The Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Gregorio Marañón; Health Research Institute (IiGSM), School of Medicine, Universidad Complutense de Madrid; Biomedical Research Center for Mental Health (CIBERSAM), Madrid, Spain
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | | |
Collapse
|
26
|
Improving ascertainment of suicidal ideation and suicide attempt with natural language processing. Sci Rep 2022; 12:15146. [PMID: 36071081 PMCID: PMC9452591 DOI: 10.1038/s41598-022-19358-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/29/2022] [Indexed: 12/03/2022] Open
Abstract
Methods relying on diagnostic codes to identify suicidal ideation and suicide attempt in Electronic Health Records (EHRs) at scale are suboptimal because suicide-related outcomes are heavily under-coded. We propose to improve the ascertainment of suicidal outcomes using natural language processing (NLP). We developed information retrieval methodologies to search over 200 million notes from the Vanderbilt EHR. Suicide query terms were extracted using word2vec. A weakly supervised approach was designed to label cases of suicidal outcomes. The NLP validation of the top 200 retrieved patients showed high performance for suicidal ideation (area under the receiver operator curve [AUROC]: 98.6, 95% confidence interval [CI] 97.1–99.5) and suicide attempt (AUROC: 97.3, 95% CI 95.2–98.7). Case extraction produced the best performance when combining NLP and diagnostic codes and when accounting for negated suicide expressions in notes. Overall, we demonstrated that scalable and accurate NLP methods can be developed to identify suicidal behavior in EHRs to enhance prevention efforts, predictive models, and precision medicine.
Collapse
|
27
|
Štajer V, Milovanović IM, Todorović N, Ranisavljev M, Pišot S, Drid P. Let's (Tik) Talk About Fitness Trends. Front Public Health 2022; 10:899949. [PMID: 35899151 PMCID: PMC9310012 DOI: 10.3389/fpubh.2022.899949] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
Several factors that follow the development of society affect physical inactivity, which primarily includes the development of technology and digitalization and the increasing choice of unhealthy lifestyle habits. However, certain shifts in the fitness industry have been noted in the last decade. The development of wearable technologies and artificial intelligence is one of the leading fitness trends and undoubtedly represents the future of the fitness industry. On the other hand, the significant influence of social media and networks affects the development and attitudes of people related to physical activity. Therefore, this review paper evaluates the advantages and disadvantages of wearable technologies and artificial intelligence, the positive and negative effects of social networks, and points out the problems accompanying these new fitness trends. The development of fitness trends follows humanity's needs, and one of the biggest challenges is incorporating these novelties in a mission to improve physical activity levels worldwide.
Collapse
Affiliation(s)
- Valdemar Štajer
- Faculty of Sport and Physical Education, University of Novi Sad, Novi Sad, Serbia
| | - Ivana M. Milovanović
- Faculty of Sport and Physical Education, University of Novi Sad, Novi Sad, Serbia
| | - Nikola Todorović
- Faculty of Sport and Physical Education, University of Novi Sad, Novi Sad, Serbia
| | - Marijana Ranisavljev
- Faculty of Sport and Physical Education, University of Novi Sad, Novi Sad, Serbia
| | - Saša Pišot
- Institute for Kinesiology Research, Science and Research Centre Koper, Koper, Slovenia
| | - Patrik Drid
- Faculty of Sport and Physical Education, University of Novi Sad, Novi Sad, Serbia
- *Correspondence: Patrik Drid
| |
Collapse
|
28
|
Carrillo GA, Cohen-Wolkowiez M, D'Agostino EM, Marsolo K, Wruck LM, Johnson L, Topping J, Richmond A, Corbie G, Kibbe WA. Standardizing, Harmonizing, and Protecting Data Collection to Broaden the Impact of COVID-19 Research: The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-up) Initiative. J Am Med Inform Assoc 2022; 29:1480-1488. [PMID: 35678579 PMCID: PMC9382379 DOI: 10.1093/jamia/ocac097] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022] Open
Abstract
Objective The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program is a consortium of community-engaged research projects with the goal of increasing access to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests in underserved populations. To accelerate clinical research, common data elements (CDEs) were selected and refined to standardize data collection and enhance cross-consortium analysis. Materials and Methods The RADx-UP consortium began with more than 700 CDEs from the National Institutes of Health (NIH) CDE Repository, Disaster Research Response (DR2) guidelines, and the PHENotypes and eXposures (PhenX) Toolkit. Following a review of initial CDEs, we made selections and further refinements through an iterative process that included live forums, consultations, and surveys completed by the first 69 RADx-UP projects. Results Following a multistep CDE development process, we decreased the number of CDEs, modified the question types, and changed the CDE wording. Most research projects were willing to collect and share demographic NIH Tier 1 CDEs, with the top exception reason being a lack of CDE applicability to the project. The NIH RADx-UP Tier 1 CDE with the lowest frequency of collection and sharing was sexual orientation. Discussion We engaged a wide range of projects and solicited bidirectional input to create CDEs. These RADx-UP CDEs could serve as the foundation for a patient-centered informatics architecture allowing the integration of disease-specific databases to support hypothesis-driven clinical research in underserved populations. Conclusion A community-engaged approach using bidirectional feedback can lead to the better development and implementation of CDEs in underserved populations during public health emergencies.
Collapse
Affiliation(s)
- Gabriel A Carrillo
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.,Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Michael Cohen-Wolkowiez
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.,Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Emily M D'Agostino
- Department of Family Medicine and Community Health, Duke University School of Medicine, Durham, NC, USA.,Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Keith Marsolo
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Lisa M Wruck
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.,Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Laura Johnson
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - James Topping
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Al Richmond
- Community-Campus Partnerships for Health, Raleigh, NC, USA
| | - Giselle Corbie
- Center for Health Equity Research, University of North Carolina, Chapel Hill, NC, USA.,Department of Social Medicine and Department of Medicine, University of North Carolina, Chapel Hill, NC, USA.,Department of Internal Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Warren A Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.,Duke Cancer Institute, Duke University School of Medicine, Durham, NC, USA
| |
Collapse
|
29
|
Mental disorders on online social media through the lens of language and behaviour: Analysis and visualisation. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
30
|
Rubeis G. iHealth: The ethics of artificial intelligence and big data in mental healthcare. Internet Interv 2022; 28:100518. [PMID: 35257003 PMCID: PMC8897624 DOI: 10.1016/j.invent.2022.100518] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/11/2022] [Accepted: 02/24/2022] [Indexed: 01/13/2023] Open
Abstract
The concept of intelligent health (iHealth) in mental healthcare integrates artificial intelligence (AI) and Big Data analytics. This article is an attempt to outline ethical aspects linked to iHealth by focussing on three crucial elements that have been defined in the literature: self-monitoring, ecological momentary assessment (EMA), and data mining. The material for the analysis was obtained by a database search. Studies and reviews providing outcome data for each of the three elements were analyzed. An ethical framing of the results was conducted that shows the chances and challenges of iHealth. The synergy between self-monitoring, EMA, and data mining might enable the prevention of mental illness, the prediction of its onset, the personalization of treatment, and the participation of patients in the treatment process. Challenges arise when it comes to the autonomy of users, privacy and data security of users, and potential bias.
Collapse
|
31
|
Monteith S, Glenn T, Geddes J, Whybrow PC, Achtyes E, Bauer M. Expectations for Artificial Intelligence (AI) in Psychiatry. Curr Psychiatry Rep 2022; 24:709-721. [PMID: 36214931 PMCID: PMC9549456 DOI: 10.1007/s11920-022-01378-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/15/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. RECENT FINDINGS For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.
Collapse
Affiliation(s)
- Scott Monteith
- Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, 49684, USA.
| | | | - John Geddes
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Peter C. Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA USA
| | - Eric Achtyes
- Michigan State University College of Human Medicine, Grand Rapids, MI 49684 USA ,Network180, Grand Rapids, MI USA
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus Medical Faculty, Technische Universität Dresden, Dresden, Germany
| |
Collapse
|
32
|
Akter S, McCarthy G, Sajib S, Michael K, Dwivedi YK, D’Ambra J, Shen K. Algorithmic bias in data-driven innovation in the age of AI. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2021. [DOI: 10.1016/j.ijinfomgt.2021.102387] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
33
|
Coley RY, Johnson E, Simon GE, Cruz M, Shortreed SM. Racial/Ethnic Disparities in the Performance of Prediction Models for Death by Suicide After Mental Health Visits. JAMA Psychiatry 2021; 78:726-734. [PMID: 33909019 PMCID: PMC8082428 DOI: 10.1001/jamapsychiatry.2021.0493] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
IMPORTANCE Clinical prediction models estimated with health records data may perpetuate inequities. OBJECTIVE To evaluate racial/ethnic differences in the performance of statistical models that predict suicide. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic/prognostic study, performed from January 1, 2009, to September 30, 2017, with follow-up through December 31, 2017, all outpatient mental health visits to 7 large integrated health care systems by patients 13 years or older were evaluated. Prediction models were estimated using logistic regression with LASSO variable selection and random forest in a training set that contained all visits from a 50% random sample of patients (6 984 184 visits). Performance was evaluated in the remaining 6 996 386 visits, including visits from White (4 031 135 visits), Hispanic (1 664 166 visits), Black (578 508 visits), Asian (313 011 visits), and American Indian/Alaskan Native (48 025 visits) patients and patients without race/ethnicity recorded (274 702 visits). Data analysis was performed from January 1, 2019, to February 1, 2021. EXPOSURES Demographic, diagnosis, prescription, and utilization variables and Patient Health Questionnaire 9 responses. MAIN OUTCOMES AND MEASURES Suicide death in the 90 days after a visit. RESULTS This study included 13 980 570 visits by 1 433 543 patients (64% female; mean [SD] age, 42 [18] years. A total of 768 suicide deaths were observed within 90 days after 3143 visits. Suicide rates were highest for visits by patients with no race/ethnicity recorded (n = 313 visits followed by suicide within 90 days, rate = 5.71 per 10 000 visits), followed by visits by Asian (n = 187 visits followed by suicide within 90 days, rate = 2.99 per 10 000 visits), White (n = 2134 visits followed by suicide within 90 days, rate = 2.65 per 10 000 visits), American Indian/Alaskan Native (n = 21 visits followed by suicide within 90 days, rate = 2.18 per 10 000 visits), Hispanic (n = 392 visits followed by suicide within 90 days, rate = 1.18 per 10 000 visits), and Black (n = 65 visits followed by suicide within 90 days, rate = 0.56 per 10 000 visits) patients. The area under the curve (AUC) and sensitivity of both models were high for White, Hispanic, and Asian patients and poor for Black and American Indian/Alaskan Native patients and patients without race/ethnicity recorded. For example, the AUC for the logistic regression model was 0.828 (95% CI, 0.815-0.840) for White patients compared with 0.640 (95% CI, 0.598-0.681) for patients with unrecorded race/ethnicity and 0.599 (95% CI, 0.513-0.686) for American Indian/Alaskan Native patients. Sensitivity at the 90th percentile was 62.2% (95% CI, 59.2%-65.0%) for White patients compared with 27.5% (95% CI, 21.0%-34.7%) for patients with unrecorded race/ethnicity and 10.0% (95% CI, 0%-23.0%) for Black patients. Results were similar for random forest models, with an AUC of 0.812 (95% CI, 0.800-0.826) for White patients compared with 0.676 (95% CI, 0.638-0.714) for patients with unrecorded race/ethnicity and 0.642 (95% CI, 0.579-0.710) for American Indian/Alaskan Native patients and sensitivities at the 90th percentile of 52.8% (95% CI, 50.0%-55.8%) for White patients, 29.3% (95% CI, 22.8%-36.5%) for patients with unrecorded race/ethnicity, and 6.7% (95% CI, 0%-16.7%) for Black patients. CONCLUSIONS AND RELEVANCE These suicide prediction models may provide fewer benefits and more potential harms to American Indian/Alaskan Native or Black patients or those with undrecorded race/ethnicity compared with White, Hispanic, and Asian patients. Improving predictive performance in disadvantaged populations should be prioritized to improve, rather than exacerbate, health disparities.
Collapse
Affiliation(s)
- R. Yates Coley
- Kaiser Permanente Washington Health Research Institute, Seattle,Department of Biostatistics, University of Washington School of Public Health, Seattle
| | - Eric Johnson
- Kaiser Permanente Washington Health Research Institute, Seattle
| | | | - Maricela Cruz
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Susan M. Shortreed
- Kaiser Permanente Washington Health Research Institute, Seattle,Department of Biostatistics, University of Washington School of Public Health, Seattle
| |
Collapse
|
34
|
Reale C, Novak LL, Robinson K, Simpson CL, Ribeiro JD, Franklin JC, Ripperger M, Walsh CG. User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:1050-1058. [PMID: 33936481 PMCID: PMC8075431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, we are developing an intervention that uses predictive analytics to inform care teams about their patients' risk of suicide attempt. We present our experience working with clinicians and staff in a military primary care setting to create preliminary designs and a context-specific usability testing plan for the deployment of the suicide risk indicator.
Collapse
Affiliation(s)
- Carrie Reale
- Vanderbilt University Medical Center, Nashville, TN
| | | | | | | | | | | | | | | |
Collapse
|
35
|
Zhang J, Oh YJ, Lange P, Yu Z, Fukuoka Y. Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint. J Med Internet Res 2020; 22:e22845. [PMID: 32996892 PMCID: PMC7557439 DOI: 10.2196/22845] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/03/2020] [Accepted: 09/17/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet. OBJECTIVE The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations. METHODS We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model. RESULTS Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper. CONCLUSIONS As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot's relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.
Collapse
Affiliation(s)
- Jingwen Zhang
- Department of Communication, University of California, Davis, Davis, CA, United States
- Department of Public Health Sciences, University of California, Davis, Davis, CA, United States
| | - Yoo Jung Oh
- Department of Communication, University of California, Davis, Davis, CA, United States
| | - Patrick Lange
- Department of Computer Science, University of California, Davis, Davis, CA, United States
| | - Zhou Yu
- Department of Computer Science, University of California, Davis, Davis, CA, United States
| | - Yoshimi Fukuoka
- Department of Physiological Nursing, University of California, San Francisco, San Francisco, CA, United States
| |
Collapse
|
36
|
Alami H, Lehoux P, Auclair Y, de Guise M, Gagnon MP, Shaw J, Roy D, Fleet R, Ag Ahmed MA, Fortin JP. Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity. J Med Internet Res 2020; 22:e17707. [PMID: 32406850 PMCID: PMC7380986 DOI: 10.2196/17707] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 04/25/2020] [Accepted: 05/13/2020] [Indexed: 12/12/2022] Open
Abstract
Artificial intelligence (AI) is seen as a strategic lever to improve access, quality, and efficiency of care and services and to build learning and value-based health systems. Many studies have examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services raises. To help decision makers address these issues in a systemic and holistic manner, this viewpoint paper relies on the health technology assessment core model to contrast the expectations of the health sector toward the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payers (ie, health system organizations and agencies) because of their central role in regulating, financing, and reimbursing novel technologies. This paper suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal, and ethical. The assessment of AI's value proposition should thus go beyond technical performance and cost logic by performing a holistic analysis of its value in a real-world context of care and services. To guide AI development, generate knowledge, and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical, and technological conditions for innovation should be created as a first step.
Collapse
Affiliation(s)
- Hassane Alami
- Public Health Research Center, Université de Montréal, Montreal, QC, Canada
- Department of Health Management, Evaluation and Policy, École de santé publique de l'Université de Montréal, Montreal, QC, Canada
- Institut national d'excellence en santé et services sociaux, Montréal, QC, Canada
| | - Pascale Lehoux
- Public Health Research Center, Université de Montréal, Montreal, QC, Canada
- Department of Health Management, Evaluation and Policy, École de santé publique de l'Université de Montréal, Montreal, QC, Canada
| | - Yannick Auclair
- Institut national d'excellence en santé et services sociaux, Montréal, QC, Canada
| | - Michèle de Guise
- Institut national d'excellence en santé et services sociaux, Montréal, QC, Canada
| | - Marie-Pierre Gagnon
- Research Center on Healthcare and Services in Primary Care, Université Laval, Quebec, QC, Canada
- Faculty of Nursing Science, Université Laval, Quebec, QC, Canada
| | - James Shaw
- Joint Centre for Bioethics, University of Toronto, Toronto, ON, Canada
- Institute for Health System Solutions and Virtual Care, Women's College Hospital, Toronto, ON, Canada
| | - Denis Roy
- Institut national d'excellence en santé et services sociaux, Montréal, QC, Canada
| | - Richard Fleet
- Research Center on Healthcare and Services in Primary Care, Université Laval, Quebec, QC, Canada
- Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université Laval, Quebec, QC, Canada
- Research Chair in Emergency Medicine, Université Laval - CHAU Hôtel-Dieu de Lévis, Lévis, QC, Canada
| | - Mohamed Ali Ag Ahmed
- Research Chair on Chronic Diseases in Primary Care, Université de Sherbrooke, Chicoutimi, QC, Canada
| | - Jean-Paul Fortin
- Research Center on Healthcare and Services in Primary Care, Université Laval, Quebec, QC, Canada
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec, QC, Canada
| |
Collapse
|
37
|
Owoyemi A, Owoyemi J, Osiyemi A, Boyd A. Artificial Intelligence for Healthcare in Africa. Front Digit Health 2020; 2:6. [PMID: 34713019 PMCID: PMC8521850 DOI: 10.3389/fdgth.2020.00006] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 05/08/2020] [Indexed: 11/13/2022] Open
Affiliation(s)
- Ayomide Owoyemi
- Department of Biomedical and Health Informatics, University of Illinois at Chicago, Chicago, IL, United States
| | | | - Adenekan Osiyemi
- Department of Family Medicine, University College Hospital, Ibadan, Nigeria
| | - Andy Boyd
- Department of Biomedical and Health Informatics, University of Illinois at Chicago, Chicago, IL, United States
| |
Collapse
|