PMID- 32442152 OWN - NLM STAT- MEDLINE DCOM- 20210427 LR - 20221207 IS - 2291-5222 (Electronic) IS - 2291-5222 (Linking) VI - 8 IP - 6 DP - 2020 Jun 26 TI - Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study. PG - e15901 LID - 10.2196/15901 [doi] LID - e15901 AB - 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 5x2 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. CI - (c)Alina Haines-Delmont, Gurdit Chahal, Ashley Jane Bruen, Abbie Wall, Christina Tara Khan, Ramesh Sadashiv, David Fearnley. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 26.06.2020. FAU - Haines-Delmont, Alina AU - Haines-Delmont A AUID- ORCID: 0000-0001-6989-0943 AD - Faculty of Health, Psychology and Social Care, Manchester Metropolitan University, Manchester, United Kingdom. FAU - Chahal, Gurdit AU - Chahal G AUID- ORCID: 0000-0002-2933-291X AD - CLARA Labs, CLARA Analytics, Santa Clara, CA, United States. FAU - Bruen, Ashley Jane AU - Bruen AJ AUID- ORCID: 0000-0002-7256-8960 AD - University of Liverpool, Health Services Research, Liverpool, United Kingdom. FAU - Wall, Abbie AU - Wall A AUID- ORCID: 0000-0002-2812-6874 AD - University of Liverpool, Health Services Research, Liverpool, United Kingdom. FAU - Khan, Christina Tara AU - Khan CT AUID- ORCID: 0000-0003-1042-2445 AD - Stanford University School of Medicine, Stanford, CA, United States. FAU - Sadashiv, Ramesh AU - Sadashiv R AUID- ORCID: 0000-0002-4620-0433 AD - CLARA Labs, CLARA Analytics, Santa Clara, CA, United States. FAU - Fearnley, David AU - Fearnley D AUID- ORCID: 0000-0002-5158-1437 AD - Mersey Care NHS Foundation Trust, Prescot, United Kingdom. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20200626 PL - Canada TA - JMIR Mhealth Uhealth JT - JMIR mHealth and uHealth JID - 101624439 SB - IM MH - Algorithms MH - Feasibility Studies MH - Humans MH - Machine Learning MH - *Mental Health MH - *Suicide Prevention PMC - PMC7380988 OTO - NOTNLM OT - cell phone OT - digital phenotyping OT - machine learning OT - nearest neighbor algorithm OT - smartphone OT - suicidal ideation OT - suicide COIS- Conflicts of Interest: AH, GC, AB, AW, and RS have nothing to disclose. CK reports grants from the Stanford University School of Medicine, during the conduct of the study, and personal fees from The Risk Authority in 2016 before the study, outside the submitted work. DF reports being a member of the Board of Managers for Innovence Augmented Intelligence Medical Systems Psychiatry, a limited liability company between Mersey Care National Health Service Foundation Trust and The Risk Authority, Stanford. This has overseen the development of technology that is undergoing evaluation in this research study. EDAT- 2020/05/23 06:00 MHDA- 2021/04/28 06:00 PMCR- 2020/06/26 CRDT- 2020/05/23 06:00 PHST- 2019/08/16 00:00 [received] PHST- 2020/02/29 00:00 [accepted] PHST- 2020/02/21 00:00 [revised] PHST- 2020/05/23 06:00 [pubmed] PHST- 2021/04/28 06:00 [medline] PHST- 2020/05/23 06:00 [entrez] PHST- 2020/06/26 00:00 [pmc-release] AID - v8i6e15901 [pii] AID - 10.2196/15901 [doi] PST - epublish SO - JMIR Mhealth Uhealth. 2020 Jun 26;8(6):e15901. doi: 10.2196/15901.