This paper is in the following e-collection/theme issue:

Depression and Mood Disorders; Suicide Prevention (1290) Clinical Information and Decision Making (1340) Decision Support for Health Professionals (1147) Electronic Health Records (971)

Published on in Vol 3, No 3 (2016): Jul-Sept

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records

Authors of this article:

Chandan Karmakar1 Author Orcid Image ;   Wei Luo1 Author Orcid Image ;   Truyen Tran1 Author Orcid Image ;   Michael Berk2 Author Orcid Image ;   Svetha Venkatesh1 Author Orcid Image
Article Authors Cited by (23) Tweetations (16) Metrics

Journals

  1. Sanderson M, Bulloch A, Wang J, Williamson T, Patten S. Predicting death by suicide using administrative health care system data: Can feedforward neural network models improve upon logistic regression models?. Journal of Affective Disorders 2019;257:741 View
  2. Zheng L, Wang O, Hao S, Ye C, Liu M, Xia M, Sabo A, Markovic L, Stearns F, Kanov L, Sylvester K, Widen E, McElhinney D, Zhang W, Liao J, Ling X. Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records. Translational Psychiatry 2020;10(1) View
  3. Berrouiguet S, Billot R, Larsen M, Lopez-Castroman J, Jaussent I, Walter M, Lenca P, Baca-García E, Courtet P. An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support. JMIR Mental Health 2019;6(5):e9766 View
  4. Sanderson M, Bulloch A, Wang J, Williams K, Williamson T, Patten S. Predicting death by suicide following an emergency department visit for parasuicide with administrative health care system data and machine learning. EClinicalMedicine 2020;20:100281 View
  5. Liang Y, Zheng X, Zeng D. A survey on big data-driven digital phenotyping of mental health. Information Fusion 2019;52:290 View
  6. Belsher B, Smolenski D, Pruitt L, Bush N, Beech E, Workman D, Morgan R, Evatt D, Tucker J, Skopp N. Prediction Models for Suicide Attempts and Deaths. JAMA Psychiatry 2019;76(6):642 View
  7. Sanderson M, Bulloch A, Wang J, Williamson T, Patten S. Predicting death by suicide using administrative health care system data: Can recurrent neural network, one-dimensional convolutional neural network, and gradient boosted trees models improve prediction performance?. Journal of Affective Disorders 2020;264:107 View
  8. Chock M, Lin J, Athyal V, Bostwick J. Differences in Health Care Utilization in the Year Before Suicide Death: A Population-Based Case-Control Study. Mayo Clinic Proceedings 2019;94(10):1983 View
  9. De la Cruz-Cano E. Association between FKBP5 and CRHR1 genes with suicidal behavior: A systematic review. Behavioural Brain Research 2017;317:46 View
  10. Jayasinghe L, Bittar A, Dutta R, Stewart R. Clinician-recalled quoted speech in electronic health records and risk of suicide attempt: a case–crossover study. BMJ Open 2020;10(4):e036186 View
  11. D’Hotman D, Loh E. AI enabled suicide prediction tools: a qualitative narrative review. BMJ Health & Care Informatics 2020;27(3):e100175 View
  12. Smith E, Ali D, Wilkerson B, Dawson W, Sobowale K, Reynolds C, Berk M, Lavretsky H, Jeste D, Ng C, Soares J, Aragam G, Wainer Z, Manji H, Licinio J, Lo A, Storch E, Fu E, Leboyer M, Tarnanas I, Ibanez A, Manes F, Caddick S, Fillit H, Abbott R, Robertson I, Chapman S, Au R, Altimus C, Hynes W, Brannelly P, Cummings J, Eyre H. A Brain Capital Grand Strategy: toward economic reimagination. Molecular Psychiatry 2021;26(1):3 View
  13. Decker B, Hill C, Baldassano S, Khankhanian P. Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches. Seizure 2021;85:138 View
  14. Edgcomb J, Thiruvalluru R, Pathak J, Brooks J. Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness. Medical Care 2021;59:S58 View
  15. Maruta N, Yaroslavcev S, Kalenskaya G, Oprya Y, Korop O, Denysenko M, Zavorotniy V. PHENOMENOLOGICAL ANALYSIS OF SUICIDAL BEHAVIOR IN PATIENTS WITH COGNITIVE IMPAIRMENT IN RECURRENT DEPRESSIVE DISORDER. Wiadomości Lekarskie 2022;75(1):293 View
  16. Horowitz L, Tipton M, Pao M. Primary and Secondary Prevention of Youth Suicide. Pediatrics 2020;145(Supplement_2):S195 View
  17. Sheu Y, Sun J, Lee H, Castro V, Barak-Corren Y, Song E, Madsen E, Gordon W, Kohane I, Churchill S, Reis B, Cai T, Smoller J. An efficient landmark model for prediction of suicide attempts in multiple clinical settings. Psychiatry Research 2023;323:115175 View
  18. Kim D, Quan L, Seo M, Kim K, Kim J, Zhu Y. Interpretable machine learning‐based approaches for understanding suicide risk and protective factors among South Korean females using survey and social media data. Suicide and Life-Threatening Behavior 2023;53(3):484 View
  19. 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. Journal of Medical Internet Research 2023;25:e44502 View
  20. Yaroslavtsev S. Phenomenological an a lysis of suicide behavior in patients with bipolar affective disorder. Experimental and Clinical Medicine 2020;87(2):36 View
  21. Somé N, Noormohammadpour P, Lange S. The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review. Frontiers in Psychiatry 2024;15 View
  22. Kim D, Jiang T, Baek J, Jang S, Zhu Y. Understanding and comparing risk factors and subtypes in South Korean adult and adolescent women's suicidal ideation or suicide attempt using survey and social media data. DIGITAL HEALTH 2024;10 View

Books/Policy Documents

  1. Larsen M, Vo L, Pratap A, Peters D. Tasman’s Psychiatry. View