Cloud based mental state monitoring system for suicide risk reconnaissance using wearable bio-sensors

Alam, Md. Golam Rabiul; Cho, Eung Jun; Huh, Eui‐Nam; Hong, Choong Seon

doi:10.1145/2557977.2558020

Cited by 29 publications

(17 citation statements)

References 13 publications

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“…Aside from mental health detection and diagnosis, a significant proportion of the papers described approaches to understanding and predicting mental health risks (n = 8). Predominantly this included efforts to predict future suicide risks from either sensor data [ 6 ], health records [ 2 , 192 ], or text [ 145 , 134 ]. Examples include: the analysis of written suicide notes [ 145 ]; of suicidality periods from the SMS messages of individuals with a history of suicidal behaviors [ 134 ]; and of suicide risk at time of a person's referral to mental health services [ 192 ], and subsequent periods [ 2 ].…”

Section: Understanding Detecting and Diagnosis Of Mental Health Statusmentioning

confidence: 99%

Machine Learning in Mental Health

Thieme

Belgrave

Doherty

2020

ACM Trans. Comput.-Hum. Interact.

242

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High prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression; and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. Aiming to guide future research and identify new directions for advancing development in this important domain, this article presents an introduction to, and a systematic review of, current ML work regarding psycho-socially based mental health conditions from the computing and HCI literature. A quantitative synthesis and qualitative narrative review of 54 papers that were included in the analysis surfaced common trends, gaps, and challenges in this space. Discussing our findings, we (i) reflect on the current state-of-the-art of ML work for mental health, (ii) provide concrete suggestions for a stronger integration of human-centered and multidisciplinary approaches in research and development, and (iii) invite more consideration of the potentially far-reaching personal, social, and ethical implications that ML models and interventions can have, if they are to find widespread, successful adoption in real-world mental health contexts.

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Section: Understanding Detecting and Diagnosis Of Mental Health Statusmentioning

confidence: 99%

Machine Learning in Mental Health

Thieme

Belgrave

Doherty

2020

ACM Trans. Comput.-Hum. Interact.

242

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“…local unemployment rate) (Nordt, Warnke, Seifritz, & Kawohl, 2015); (ii) historical US Department of Defense administrative data known to predict post-discharge suicides (Reger et al, 2015); (iii) data from commercial search engines calling up various public records (e.g. legal, financial, criminal justice) that might predict suicides (http://www.accurint.com/); (iv) surveillance of postings on social media for patients who consent to monitoring; (v) surveillance of data from phone apps (Onnela & Rauch, 2016) and wearables (Alam, Cho, Huh, & Hong, 2014).…”

Section: Generalized Boosted Regression Modelsmentioning

confidence: 99%

Developing a practical suicide risk prediction model for targeting high‐risk patients in the Veterans health Administration

Kessler

Hwang

Hoffmire

et al. 2017

Int J Methods Psych Res

154

125

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Objectives The US Veterans Health Administration (VHA) has begun using predictive modeling to identify Veterans at high suicide risk to target care. Initial analyses are reported here. Methods A penalized logistic regression model was compared with an earlier proof-of-concept logistic model. Exploratory analyses then considered commonly-used machine learning algorithms. Analyses were based on electronic medical records for all 6,360 individuals classified in the National Death Index as having died by suicide in fiscal years 2009–2011 who used VHA services the year of their death or prior year and a 1% probability sample of time-matched VHA service users alive at the index date (n = 2,112,008). Results A penalized logistic model with 61 predictors had sensitivity comparable to the proof-of-concept model (which had 381 predictors) at target thresholds. The machine learning algorithms had relatively similar sensitivities, the highest being for Bayesian additive regression trees, with 10.7% of suicides occurred among the 1.0% of Veterans with highest predicted risk and 28.1% among the 5.0% of with highest predicted risk. Conclusions Based on these results, VHA is using penalized logistic regression in initial intervention implementation. The paper concludes with a discussion of other practical issues that might be explored to increase model performance.

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“…Emergency psychiatry deals with psychiatric emergencies, which are acute disturbances of behavior, thought and mood in mentally-disordered patients, who are at risk of potential danger to themselves (e.g., suicide) and to others (e.g., homicide) [ 13 ]. However, this research is focused only on the suicidal psychiatric emergency due to the unavailability and unobtainability of datasets of the homicide psychiatric emergency.…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

Alam

Abedin

Ameen

et al. 2016

Sensors

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Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study.

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Cloud based mental state monitoring system for suicide risk reconnaissance using wearable bio-sensors

Cited by 29 publications

References 13 publications

Machine Learning in Mental Health

Machine Learning in Mental Health

Developing a practical suicide risk prediction model for targeting high‐risk patients in the Veterans health Administration

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

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