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Pigoni A, Delvecchio G, Turtulici N, Madonna D, Pietrini P, Cecchetti L, Brambilla P. Machine learning and the prediction of suicide in psychiatric populations: a systematic review. Transl Psychiatry 2024; 14:140. [PMID: 38461283 PMCID: PMC10925059 DOI: 10.1038/s41398-024-02852-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.
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Affiliation(s)
- Alessandro Pigoni
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Domenico Madonna
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Pietro Pietrini
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Luca Cecchetti
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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Sufyan NS, Fadhel FH, Alkhathami SS, Mukhadi JYA. Artificial intelligence and social intelligence: preliminary comparison study between AI models and psychologists. Front Psychol 2024; 15:1353022. [PMID: 38379623 PMCID: PMC10878391 DOI: 10.3389/fpsyg.2024.1353022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 01/22/2024] [Indexed: 02/22/2024] Open
Abstract
Background Social intelligence (SI) is of great importance in the success of the counseling and psychotherapy, whether for the psychologist or for the artificial intelligence systems that help the psychologist, as it is the ability to understand the feelings, emotions, and needs of people during the counseling process. Therefore, this study aims to identify the Social Intelligence (SI) of artificial intelligence represented by its large linguistic models, "ChatGPT; Google Bard; and Bing" compared to psychologists. Methods A stratified random manner sample of 180 students of counseling psychology from the bachelor's and doctoral stages at King Khalid University was selected, while the large linguistic models included ChatGPT-4, Google Bard, and Bing. They (the psychologists and the AI models) responded to the social intelligence scale. Results There were significant differences in SI between psychologists and AI's ChatGPT-4 and Bing. ChatGPT-4 exceeded 100% of all the psychologists, and Bing outperformed 50% of PhD holders and 90% of bachelor's holders. The differences in SI between Google Bard and bachelor students were not significant, whereas the differences with PhDs were significant; Where 90% of PhD holders excel on Google Bird. Conclusion We explored the possibility of using human measures on AI entities, especially language models, and the results indicate that the development of AI in understanding emotions and social behavior related to social intelligence is very rapid. AI will help the psychotherapist a great deal in new ways. The psychotherapist needs to be aware of possible areas of further development of AI given their benefits in counseling and psychotherapy. Studies using humanistic and non-humanistic criteria with large linguistic models are needed.
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Affiliation(s)
- Nabil Saleh Sufyan
- Psychology Department, College of Education, King Khalid University, Abha, Saudi Arabia
| | - Fahmi H. Fadhel
- Psychology Program, Social Science Department, College of Arts and Sciences, Qatar University, Doha, Qatar
| | | | - Jubran Y. A. Mukhadi
- Psychology Department, College of Education, King Khalid University, Abha, Saudi Arabia
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Bartlett LK, Pirrone A, Javed N, Gobet F. Computational Scientific Discovery in Psychology. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2023; 18:178-189. [PMID: 35943820 PMCID: PMC9902966 DOI: 10.1177/17456916221091833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.
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Affiliation(s)
- Laura K. Bartlett
- Laura K. Bartlett, Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science
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Hopkins D, Rickwood DJ, Hallford DJ, Watsford C. Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis. Front Digit Health 2022; 4:945006. [PMID: 35983407 PMCID: PMC9378826 DOI: 10.3389/fdgth.2022.945006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/29/2022] [Indexed: 11/23/2022] Open
Abstract
Suicide remains a leading cause of preventable death worldwide, despite advances in research and decreases in mental health stigma through government health campaigns. Machine learning (ML), a type of artificial intelligence (AI), is the use of algorithms to simulate and imitate human cognition. Given the lack of improvement in clinician-based suicide prediction over time, advancements in technology have allowed for novel approaches to predicting suicide risk. This systematic review and meta-analysis aimed to synthesize current research regarding data sources in ML prediction of suicide risk, incorporating and comparing outcomes between structured data (human interpretable such as psychometric instruments) and unstructured data (only machine interpretable such as electronic health records). Online databases and gray literature were searched for studies relating to ML and suicide risk prediction. There were 31 eligible studies. The outcome for all studies combined was AUC = 0.860, structured data showed AUC = 0.873, and unstructured data was calculated at AUC = 0.866. There was substantial heterogeneity between the studies, the sources of which were unable to be defined. The studies showed good accuracy levels in the prediction of suicide risk behavior overall. Structured data and unstructured data also showed similar outcome accuracy according to meta-analysis, despite different volumes and types of input data.
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Affiliation(s)
- Danielle Hopkins
- Faculty of Health, University of Canberra, Canberra, ACT, Australia
- *Correspondence: Danielle Hopkins
| | | | | | - Clare Watsford
- Faculty of Health, University of Canberra, Canberra, ACT, Australia
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Okawa T, Mizuno T, Hanabusa S, Ikeda T, Mizokami F, Koseki T, Takahashi K, Yuzawa Y, Tsuboi N, Yamada S, Kameya Y. Prediction model of acute kidney injury induced by cisplatin in older adults using a machine learning algorithm. PLoS One 2022; 17:e0262021. [PMID: 35041690 PMCID: PMC8765666 DOI: 10.1371/journal.pone.0262021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 12/15/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Early detection and prediction of cisplatin-induced acute kidney injury (Cis-AKI) are essential for the management of patients on chemotherapy with cisplatin. This study aimed to evaluate the performance of a prediction model for Cis-AKI. METHODS Japanese patients, who received cisplatin as the first-line chemotherapy at Fujita Health University Hospital, were enrolled in the study. The main metrics for evaluating the machine learning model were the area under the curve (AUC), accuracy, precision, recall, and F-measure. In addition, the rank of contribution as a predictive factor of Cis-AKI was determined by machine learning. RESULTS A total of 1,014 and 226 patients were assigned to the development and validation data groups, respectively. The current prediction model showed the highest performance in patients 65 years old and above (AUC: 0.78, accuracy: 0.77, precision: 0.38, recall: 0.70, F-measure: 0.49). The maximum daily cisplatin dose and serum albumin levels contributed the most to the prediction of Cis-AKI. CONCLUSION Our prediction model for Cis-AKI performed effectively in older patients.
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Affiliation(s)
- Takaya Okawa
- Department of Clinical Pharmacy, Fujita Health University School of Medicine, Toyoake, Japan
| | - Tomohiro Mizuno
- Department of Clinical Pharmacy, Fujita Health University School of Medicine, Toyoake, Japan
| | - Shogo Hanabusa
- Department of Information Engineering, Meijo University, Nagoya, Japan
| | - Takeshi Ikeda
- Department of Information Engineering, Meijo University, Nagoya, Japan
| | - Fumihiro Mizokami
- Department of Pharmacy, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Takenao Koseki
- Department of Clinical Pharmacy, Fujita Health University School of Medicine, Toyoake, Japan
| | - Kazuo Takahashi
- Department of Biomedical Molecular Sciences, Fujita Health University School of Medicine, Toyoake, Japan
| | - Yukio Yuzawa
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Naotake Tsuboi
- Department of Nephrology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Shigeki Yamada
- Department of Clinical Pharmacy, Fujita Health University School of Medicine, Toyoake, Japan
| | - Yoshitaka Kameya
- Department of Information Engineering, Meijo University, Nagoya, Japan
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Chan EC, Wallace K, Yang EH, Roper L, Aryal G, Lodhi RJ, Baskys A, Isenberg R, Carnes P, Green B, Aitchison KJ. Internal consistency and concurrent validity of self-report components of a new instrument for the assessment of suicidality, the Suicide Ideation and Behavior Assessment Tool (SIBAT). Psychiatry Res 2021; 304:114128. [PMID: 34343876 DOI: 10.1016/j.psychres.2021.114128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 10/20/2022]
Abstract
This study aimed to assess the internal consistency of self-report components of the Suicide Ideation and Behavior Assessment Tool (SIBAT) and validate it with relevant elements of the Mini International Neuropsychiatric Interview (MINI). The SIBAT is a newly developed instrument for the evaluation of suicidality. In this study, we invited university students and trainees participating in a study of addictions to complete the self-report component of the SIBAT as an add-on study. We evaluated the internal consistency of the self-report component of the SIBAT and validated it against the suicidality component of the MINI. Data were analysed using both complete case analysis and multiple imputation. SIBAT data were collected for 394 participants, 314 of whom had also completed the MINI. The internal consistency of modules 2, 3, and 5 of the SIBAT was high. Each item from module 5 had a statistically significant association with the corresponding item from the MINI. The sum of scores from modules 2 and 3 had a moderate correlation with the assessment of suicide risk determined by the MINI, and a strong correlation with the total score of SIBAT module 5. The completion median time of modules 2, 3 and 5 was 14.3 min.
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Affiliation(s)
- Eric C Chan
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada.
| | - Keanna Wallace
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Esther H Yang
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Leslie Roper
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Garima Aryal
- Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada
| | - Rohit J Lodhi
- Department of Psychiatry, University of Saskatchewan, Saskatoon, SK, Canada
| | - Andrius Baskys
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Graduate College of Biomedical Sciences and University Medical Center, Western University of Health Sciences, Pomona, California, United States; Memory Disorders and Genomic Medicine Clinic, Riverside, California, United States
| | - Richard Isenberg
- American Foundation for Addiction Research, Psychological Counseling Services, Scottsdale, Arizona, United States
| | - Patrick Carnes
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada
| | - Bradley Green
- Department of Psychology, University of Texas at Tyler, Tyler, United States
| | - Katherine J Aitchison
- Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada; Department of Medical Genetics, University of Alberta, Edmonton, Alberta, Canada.
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Ballard ED, Gilbert JR, Wusinich C, Zarate CA. New Methods for Assessing Rapid Changes in Suicide Risk. Front Psychiatry 2021; 12:598434. [PMID: 33574775 PMCID: PMC7870718 DOI: 10.3389/fpsyt.2021.598434] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/05/2021] [Indexed: 01/16/2023] Open
Abstract
Rapid-acting interventions for the suicide crisis have the potential to transform treatment. In addition, recent innovations in suicide research methods may similarly expand our understanding of the psychological and neurobiological correlates of suicidal thoughts and behaviors. This review discusses the limitations and challenges associated with current methods of suicide risk assessment and presents new techniques currently being developed to measure rapid changes in suicidal thoughts and behavior. These novel assessment strategies include ecological momentary assessment, digital phenotyping, cognitive and implicit bias metrics, and neuroimaging paradigms and analysis methodologies to identify neural circuits associated with suicide risk. This review is intended to both describe the current state of our ability to assess rapid changes in suicide risk as well as to explore future directions for clinical, neurobiological, and computational markers research in suicide-focused clinical trials.
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Affiliation(s)
- Elizabeth D. Ballard
- Section on the Neurobiology and Treatment of Mood Disorders, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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Morales S, Barros J. Mental Pain Surrounding Suicidal Behaviour: A Review of What Has Been Described and Clinical Recommendations for Help. Front Psychiatry 2021; 12:750651. [PMID: 35153847 PMCID: PMC8828913 DOI: 10.3389/fpsyt.2021.750651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 12/21/2021] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To conduct a comprehensive review of scientific publications related to mental pain and suicide risk in order to deepen relevant aspects to guide clinical interventions. METHOD Using a text analysis tool, we collected the terms most frequently linked with that situation in published results of research using various tools to evaluate mental pain or psychache. DISCUSSION We propose clinical interventions for the clinical conditions most commonly associated with mental pain.
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Affiliation(s)
- Susana Morales
- Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Institute for Research in Depression and Personality (MIDAP), Santiago, Chile
| | - Jorge Barros
- Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Institute for Research in Depression and Personality (MIDAP), Santiago, Chile
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Bernert RA, Hilberg AM, Melia R, Kim JP, Shah NH, Abnousi F. Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E5929. [PMID: 32824149 PMCID: PMC7460360 DOI: 10.3390/ijerph17165929] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 07/28/2020] [Indexed: 12/12/2022]
Abstract
Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.
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Affiliation(s)
- Rebecca A. Bernert
- Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Amanda M. Hilberg
- Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Ruth Melia
- Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
- Department of Psychology, National University of Ireland, Galway, Ireland
| | - Jane Paik Kim
- Stanford Suicide Prevention Research Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Nigam H. Shah
- Department of Medicine, Center for Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA 94304, USA
- Informatics, Stanford Center for Clinical and Translational Research, and Education (Spectrum), Stanford University, Stanford CA 94304, USA
| | - Freddy Abnousi
- Facebook, Menlo Park, CA 94025, USA
- Yale University School of Medicine, New Haven, CT 06510, USA
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Haines-Delmont A, Chahal G, Bruen AJ, Wall A, Khan CT, Sadashiv R, Fearnley D. Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study. JMIR Mhealth Uhealth 2020; 8:e15901. [PMID: 32442152 PMCID: PMC7380988 DOI: 10.2196/15901] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 02/21/2020] [Accepted: 02/29/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Digital phenotyping and machine learning are currently being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance mental health globally. OBJECTIVE This study aimed to apply machine learning in an acute mental health setting for suicide risk prediction. This study uses a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data, which have typically been collected from health care records. METHODS We created a smartphone app called Strength Within Me, which was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of inpatients with acute mental health (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine the best fit. RESULTS K-nearest neighbors (KNN; k=2) with uniform weighting and the Euclidean distance metric emerged as the most promising algorithm, with 68% mean accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross-validation) and an average area under the curve of 0.65. We applied a combined 5×2 F test to test the model performance of KNN against the baseline classifier that guesses training majority, random forest, support vector machine and logistic regression, and achieved F statistics of 10.7 (P=.009) and 17.6 (P=.003) for training majority and random forest, respectively, rejecting the null of performance being the same. Therefore, we have taken the first steps in prototyping a system that could continuously and accurately assess the risk of suicide via mobile devices. CONCLUSIONS Predicting for suicidality is an underaddressed area of research to which this paper makes a useful contribution. This is part of the first generation of studies to suggest that it is feasible to utilize smartphone-generated user input and passive sensor data to generate a risk algorithm among inpatients at suicide risk. The model reveals fair concordance between phone-derived and research-generated clinical data, and with iterative development, it has the potential for accurate discriminant risk prediction. However, although full automation and independence of clinical judgment or input would be a worthy development for those individuals who are less likely to access specialist mental health services, and for providing a timely response in a crisis situation, the ethical and legal implications of such advances in the field of psychiatry need to be acknowledged.
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Affiliation(s)
- Alina Haines-Delmont
- Faculty of Health, Psychology and Social Care, Manchester Metropolitan University, Manchester, United Kingdom
| | - Gurdit Chahal
- CLARA Labs, CLARA Analytics, Santa Clara, CA, United States
| | - Ashley Jane Bruen
- University of Liverpool, Health Services Research, Liverpool, United Kingdom
| | - Abbie Wall
- University of Liverpool, Health Services Research, Liverpool, United Kingdom
| | | | | | - David Fearnley
- Mersey Care NHS Foundation Trust, Prescot, United Kingdom
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Barros J, Morales S, García A, Echávarri O, Fischman R, Szmulewicz M, Moya C, Núñez C, Tomicic A. Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample. BMC Psychiatry 2020; 20:138. [PMID: 32228548 PMCID: PMC7106600 DOI: 10.1186/s12888-020-02535-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 03/06/2020] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional dependence relationships among variables for each individual subject studied. These conditional dependencies represented the different states that patients could experience in relation to suicidal behavior (SB). The clinical sample included 650 mental health patients with mood and anxiety symptomatology. RESULTS Mainly indicated that variables within the Bayesian network are part of each patient's state of psychological vulnerability and have the potential to impact such states and that these variables coexist and are relatively stable over time. These results have enabled us to offer a tool to detect states of psychological vulnerability associated with suicide risk. CONCLUSION If we accept that suicidal behaviors (vulnerability, ideation, and suicidal attempts) exist in constant change and are unstable, we can investigate what individuals experience at specific moments to become better able to intervene in a timely manner to prevent such behaviors. Future testing of the tool developed in this study is needed, not only in specialized mental health environments but also in other environments with high rates of mental illness, such as primary healthcare facilities and educational institutions.
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Affiliation(s)
- Jorge Barros
- Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile
| | - Susana Morales
- Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile.
- Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile.
| | - Arnol García
- Independent mathematical engineer, Santiago, Chile
| | - Orietta Echávarri
- Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile
- Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile
| | - Ronit Fischman
- Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile
| | - Marta Szmulewicz
- Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile
- Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile
| | - Claudia Moya
- Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile
- School of Nursing, San Sebastian University, Santiago, Chile
| | - Catalina Núñez
- Psychiatry Department, School of Medicine, Pontificia Universidad Católica de Chile, La Reconquista 498, Las Condes, Santiago, Chile
| | - Alemka Tomicic
- Millennium Institute for Research in Depression and Personality MIDAP, Santiago, Chile
- School of Psychology, Diego Portales University, Santiago, Chile
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Sun X, Li H, Song W, Jiang S, Shen C, Wang X. ROC analysis of three‐dimensional psychological pain in suicide ideation and suicide attempt among patients with major depressive disorder. J Clin Psychol 2019; 76:210-227. [PMID: 31576558 DOI: 10.1002/jclp.22870] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Xuemei Sun
- Department of PsychologyRenmin University of ChinaBeijing China
| | - Huanhuan Li
- Department of PsychologyRenmin University of ChinaBeijing China
| | - Wei Song
- Department of PsychologyRenmin University of ChinaBeijing China
| | - Songyuan Jiang
- Department of PsychologyRenmin University of ChinaBeijing China
| | - Chengfeng Shen
- Department of PsychologyRenmin University of ChinaBeijing China
| | - Xiang Wang
- Medical Institute of PsychologySecond Xiangya Hospital of Central South UniversityChangsha China
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Burke TA, Ammerman BA, Jacobucci R. The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review. J Affect Disord 2019; 245:869-884. [PMID: 30699872 DOI: 10.1016/j.jad.2018.11.073] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 10/20/2018] [Accepted: 11/11/2018] [Indexed: 12/23/2022]
Abstract
BACKGROUND Machine learning techniques offer promise to improve suicide risk prediction. In the current systematic review, we aimed to review the existing literature on the application of machine learning techniques to predict self-injurious thoughts and behaviors (SITBs). METHOD We systematically searched PsycINFO, PsycARTICLES, ERIC, CINAHL, and MEDLINE for articles published through February 2018. RESULTS Thirty-five articles met criteria to be included in the review. Included articles were reviewed by outcome: suicide death, suicide attempt, suicide plan, suicidal ideation, suicide risk, and non-suicidal self-injury. We observed three general aims in the use of SITB-focused machine learning analyses: (1) improving prediction accuracy, (2) identifying important model indicators (i.e., variable selection) and indicator interactions, and (3) modeling underlying subgroups. For studies with the aim of boosting predictive accuracy, we observed greater prediction accuracy of SITBs than in previous studies using traditional statistical methods. Studies using machine learning for variable selection purposes have both replicated findings of well-known SITB risk factors and identified novel variables that may augment model performance. Finally, some of these studies have allowed for subgroup identification, which in turn has helped to inform clinical cutoffs. LIMITATIONS Limitations of the current review include relatively low paper sample size, inconsistent reporting procedures resulting in an inability to compare model accuracy across studies, and lack of model validation on external samples. CONCLUSIONS We concluded that leveraging machine learning techniques to further predictive accuracy and identify novel indicators will aid in the prediction and prevention of suicide.
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Affiliation(s)
- Taylor A Burke
- Temple University, Department of Psychology, Philadelphia, PA, USA.
| | - Brooke A Ammerman
- University of Notre Dame, Department of Psychology, Notre Dame, IN, USA
| | - Ross Jacobucci
- University of Notre Dame, Department of Psychology, Notre Dame, IN, USA
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14
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de Mello FL, de Souza SA. Psychotherapy and Artificial Intelligence: A Proposal for Alignment. Front Psychol 2019; 10:263. [PMID: 30804863 PMCID: PMC6378280 DOI: 10.3389/fpsyg.2019.00263] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 01/28/2019] [Indexed: 12/26/2022] Open
Abstract
Brief Psychotherapy assists patients to become aware and change their behavior when facing an immediate emotional conflict, and to implement a transformation process through actions of listening, observing, increasing awareness and making interventions. Therapeutic work employs tools and techniques to trigger a process of change, emphasizing cognitive and affective understanding. This article presents an approach that combines Psychology and Artificial Intelligence with the purpose of enhancing psychotherapy with computer-implemented tools. This approach highlights the intersection between these two knowledge areas and shows how machine intelligence can help to characterize affective areas, construct genograms, determine degree of differentiation of self, investigate cognitive interaction patterns, and achieve self-awareness and redefinition. The conceptual proposal was implemented by a web application, and a sample of computer-aided analysis is presented.
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Affiliation(s)
- Flávio Luis de Mello
- Electronic and Computer Engineering Department, Polytechnic School, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
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15
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Ebert DD, Harrer M, Apolinário-Hagen J, Baumeister H. Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1192:583-627. [PMID: 31705515 DOI: 10.1007/978-981-32-9721-0_29] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Mental disorders are highly prevalent and often remain untreated. Many limitations of conventional face-to-face psychological interventions could potentially be overcome through Internet-based and mobile-based interventions (IMIs). This chapter introduces core features of IMIs, describes areas of application, presents evidence on the efficacy of IMIs as well as potential effect mechanisms, and delineates how Artificial Intelligence combined with IMIs may improve current practices in the prevention and treatment of mental disorders in adults. Meta-analyses of randomized controlled trials clearly show that therapist-guided IMIs can be highly effective for a broad range of mental health problems. Whether the effects of unguided IMIs are also clinically relevant, particularly under routine care conditions, is less clear. First studies on IMIs for the prevention of mental disorders have shown promising results. Despite limitations and challenges, IMIs are increasingly implemented into routine care worldwide. IMIs are also well suited for applications of Artificial Intelligence and Machine Learning, which provides ample opportunities to improve the identification and treatment of mental disorders. Together with methodological innovations, these approaches may also deepen our understanding of how psychological interventions work, and why. Ethical and professional restraints as well as potential contraindications of IMIs, however, should also be considered. In sum, IMIs have a high potential for improving the prevention and treatment of mental health disorders across various indications, settings, and populations. Therefore, implementing IMIs into routine care as both adjunct and alternative to face-to-face treatment is highly desirable. Technological advancements may further enhance the variability and flexibility of IMIs, and thus even further increase their impact in people's lives in the future.
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Affiliation(s)
- David Daniel Ebert
- Department of Clinical Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 1, 1881 BT, Amsterdam, The Netherlands.
| | - Mathias Harrer
- Clinical Psychology and Psychotherapy, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | | | - Harald Baumeister
- Clinical Psychology and Psychotherapy, University of Ulm, Ulm, Germany
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16
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Alonso SG, de la Torre-Díez I, Hamrioui S, López-Coronado M, Barreno DC, Nozaleda LM, Franco M. Data Mining Algorithms and Techniques in Mental Health: A Systematic Review. J Med Syst 2018; 42:161. [PMID: 30030644 DOI: 10.1007/s10916-018-1018-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 07/16/2018] [Indexed: 12/12/2022]
Abstract
Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas. The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms of Data Mining in Mental Health, specifically in the most prevalent diseases such as: Dementia, Alzheimer, Schizophrenia and Depression. Academic databases that were used to perform the searches are Google Scholar, IEEE Xplore, PubMed, Science Direct, Scopus and Web of Science, taking into account as date of publication the last 10 years, from 2008 to the present. Several search criteria were established such as 'techniques' AND 'Data Mining' AND 'Mental Health', 'algorithms' AND 'Data Mining' AND 'dementia' AND 'schizophrenia' AND 'depression', etc. selecting the papers of greatest interest. A total of 211 articles were found related to techniques and algorithms of Data Mining applied to the main Mental Health diseases. 72 articles have been identified as relevant works of which 32% are Alzheimer's, 22% dementia, 24% depression, 14% schizophrenia and 8% bipolar disorders. Many of the papers show the prediction of risk factors in these diseases. From the review of the research articles analyzed, it can be said that use of Data Mining techniques applied to diseases such as dementia, schizophrenia, depression, etc. can be of great help to the clinical decision, diagnosis prediction and improve the patient's quality of life.
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Affiliation(s)
- Susel Góngora Alonso
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Isabel de la Torre-Díez
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain.
| | - Sofiane Hamrioui
- Bretagne Loire and Nantes Universities, UMR 6164, IETR Polytech Nantes, Nantes, France
| | - Miguel López-Coronado
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Diego Calvo Barreno
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Lola Morón Nozaleda
- Nozaleda and Lafora Mental Health Clinic, C/ José Ortega Y Gasset, 44, 28006, Madrid, Spain
| | - Manuel Franco
- Psiquiatry Service, Hospital Zamora, Hernán Cortés, Zamora, Spain
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