ArticleJMIR mental health2016
Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records.
Article in JMIR mental health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- Development and validation of the suicide risk score: a novel suicide risk prediction tool for patients with end-stage kidney disease.Clinical kidney journal · 2026Article
- A cohort study of predictors of short-term nonfatal suicidal and self-harm events among individuals with mental health disorders treated in the emergency department.Journal of psychiatric research · 2025Article
- Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health.Journal of medical Internet research · 2023Article
- An efficient landmark model for prediction of suicide attempts in multiple clinical settings.Psychiatry research · 2023Article
- Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.Medical care · 2021Article
- A Brain Capital Grand Strategy: toward economic reimagination.Molecular psychiatry · 2021Article
- AI enabled suicide prediction tools: a qualitative narrative review.BMJ health & care informatics · 2020Review
- Primary and Secondary Prevention of Youth Suicide.Pediatrics · 2020Article
- Clinician-recalled quoted speech in electronic health records and risk of suicide attempt: a case-crossover study.BMJ open · 2020Article
- Predicting death by suicide following an emergency department visit for parasuicide with administrative health care system data and machine learning.EClinicalMedicine · 2020Article
- Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records.Translational psychiatry · 2020Article
- An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support.JMIR mental health · 2019Article
- Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.Yearbook of medical informatics · 2017Review
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Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk.
objectiveThe aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data.
methodsWe used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC).
resultsThe best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians.
conclusionsThis study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk.
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