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

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