Half of people who die by suicide are seen by a health care professional within the month preceding their death. While this contact with a medical professional represents an opportunity to intervene and to prevent suicide, it turns out that as clinicians, we lack the tools to reliably predict who is at greatest risk for suicide. Matthew Nock, PhD, the Edgar Pierce Professor of Psychology at Harvard College and Jordan Smoller, MD ScD, Director of the Center for Precision Psychiatry have been working together to develop better tools for identifying people at high risk fo suicide and strategies for decreasing suicide risk in this population.
For example, Nock and Smoller have explored how data included in the electronic health record (EHR) and machine learning models could be used to more accurately predict who is at greatest risk for suicide. Preliminary results from this project indicates that the ability to identify patients at high risk of suicide attempt could be improved using a combination of patient self-report questions and EHR data.
The mission is to use recent innovations in suicide science to propose and test improvements in how suicidal patients are identified and treated. — Matthew Nock, PhD
To build on and expand the use of this research, the Center for Suicide Research and Prevention to be co-directed by Smoller and Nock was launched last month with the help of a $17 million grant from the National Institute of Mental Health (NIMH). This center is part of a larger suicide-reduction initiative from the NIMH, which is planning to provide funding to practice-based research centers across the United States.
Read More
Barak-Corren Y, Castro VM, Javitt S, Hoffnagle AG, Dai Y, Perlis RH, Nock MK, Smoller JW, Reis BY. Predicting Suicidal Behavior From Longitudinal Electronic Health Records. Am J Psychiatry. 2017 Feb 1;174(2):154-162.
Barak-Corren Y, Castro VM, Nock MK, Mandl KD, Madsen EM, Seiger A, Adams WG, Applegate RJ, Bernstam EV, Klann JG, McCarthy EP, Murphy SN, Natter M, Ostasiewski B, Patibandla N, Rosenthal GE, Silva GS, Wei K, Weber GM, Weiler SR, Reis BY, Smoller JW. Validation of an Electronic Health Record-Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems. JAMA Netw Open. 2020 Mar 2;3(3):e201262.
Bentley KH, Zuromski KL, Fortgang RG, Madsen EM, Kessler D, Lee H, Nock MK, Reis BY, Castro VM, Smoller JW. Implementing Machine Learning Models for Suicide Risk Prediction in Clinical Practice: Focus Group Study With Hospital Providers. JMIR Form Res. 2022 Mar 11;6(3):e30946.Kessler RC, Bauer MS, Bishop TM, Bossarte RM, Castro VM, Demler OV, Gildea SM, Goulet JL, King AJ, Kennedy CJ, Landes SJ, Liu H, Luedtke A, Mair P, Marx BP, Nock MK, Petukhova MV, Pigeon WR, Sampson NA, Smoller JW, Miller A, Haas G, Benware J, Bradley J, Owen RR, House S, Urosevic S, Weinstock LM. Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention. JAMA Psychiatry. 2023 Mar 1;80(3):230-240.
Nock MK, Millner AJ, Ross EL, Kennedy CJ, Al-Suwaidi M, Barak-Corren Y, Castro VM, Castro-Ramirez F, Lauricella T, Murman N, Petukhova M, Bird SA, Reis B, Smoller JW, Kessler RC. Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records. JAMA Netw Open. 2022 Jan 4;5(1):e2144373.
Ross EL, Zuromski KL, Reis BY, Nock MK, Kessler RC, Smoller JW. Accuracy Requirements for Cost-effective Suicide Risk Prediction Among Primary Care Patients in the US. JAMA Psychiatry. 2021 Jun 1;78(6):642-650.
In the News
Seizing the chance to stop a suicide (Harvard Gazette)
Tracking rapidly changing patterns of suicidal thought (Harvard Gazette)
