Cheng, MRoseberry, KyleChoi, YQuast, LGaines, MadelynnSandusky, GeorgeKline, JABogdan, PaulNiculescu, Alexander2024-06-202024-06-202022-06-13Cheng M, Roseberry K, Choi Y, Quast L, Gaines M, Sandusky G, Kline JA, Bogdan P, Niculescu AB. Polyphenic risk score shows robust predictive ability for long-term future suicidality. Discov Ment Health. 2022;2(1):13. doi: 10.1007/s44192-022-00016-z. Epub 2022 Jun 13. PMID: 35722470; PMCID: PMC9192379.https://hdl.handle.net/1805/41648Suicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument (Convergent Functional Information for Suicidality, CFI-S), which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years (occurrence- ROC AUC 80%, severity- Pearson correlation 0.44, imminence-Cox regression Hazard Ratio 1.33). The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient's risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants.enEmergency departmentMachine learningPredictionRiskSocial isolationSuicidalityArticle