Temporally informed random forests for suicide risk prediction

Bayramli, Ilkin; Castro, Víctor M.; Barak–Corren, Yuval; Madsen, Emily; Nock, Matthew K.; Smoller, Jordan W.; Reis, Ben Y.

doi:10.1093/jamia/ocab225

Cited by 17 publications

(10 citation statements)

References 20 publications

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“…Predictive models trained for practical purposes would be designed for predicting at any point during the patient’s longitudinal history. One approach for doing this with random forests is to sample random visits in the patient’s medical timeline and include cumulative feature history up until that visit as “snapshots.” We have explored such multi-temporal suicide risk predictions with random forests in a separate study 10 .…”

Section: Discussionmentioning

confidence: 99%

Predictive structured–unstructured interactions in EHR models: A case study of suicide prediction

et al. 2022

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Clinical risk prediction models powered by electronic health records (EHRs) are becoming increasingly widespread in clinical practice. With suicide-related mortality rates rising in recent years, it is becoming increasingly urgent to understand, predict, and prevent suicidal behavior. Here, we compare the predictive value of structured and unstructured EHR data for predicting suicide risk. We find that Naive Bayes Classifier (NBC) and Random Forest (RF) models trained on structured EHR data perform better than those based on unstructured EHR data. An NBC model trained on both structured and unstructured data yields similar performance (AUC = 0.743) to an NBC model trained on structured data alone (0.742, p = 0.668), while an RF model trained on both data types yields significantly better results (AUC = 0.903) than an RF model trained on structured data alone (0.887, p < 0.001), likely due to the RF model’s ability to capture interactions between the two data types. To investigate these interactions, we propose and implement a general framework for identifying specific structured-unstructured feature pairs whose interactions differ between case and non-case cohorts, and thus have the potential to improve predictive performance and increase understanding of clinical risk. We find that such feature pairs tend to capture heterogeneous pairs of general concepts, rather than homogeneous pairs of specific concepts. These findings and this framework can be used to improve current and future EHR-based clinical modeling efforts.

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Section: Discussionmentioning

confidence: 99%

Predictive structured–unstructured interactions in EHR models: A case study of suicide prediction

et al. 2022

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“…To assemble the cohort for this study, we queried the MGB RPDR for 1 546 440 patients who self-identified as non-Hispanic White (i.e. 76% of the overall MGB patient population) having at least three visits after 2005, more than 30 days apart between the first and last visits, and at least one visit greater than age 10 and less than age 90, as of February 2020 (Bayramli et al, 2021; Castro et al, 2021) (see Fig. 1).…”

Section: Methodsmentioning

confidence: 99%

Impact of selection bias on polygenic risk score estimates in healthcare settings

et al. 2023

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Background Hospital-based biobanks are being increasingly considered as a resource for translating polygenic risk scores (PRS) into clinical practice. However, since these biobanks originate from patient populations, there is a possibility of bias in polygenic risk estimation due to overrepresentation of patients with higher frequency of healthcare interactions. Methods PRS for schizophrenia, bipolar disorder, and depression were calculated using summary statistics from the largest available genomic studies for a sample of 24 153 European ancestry participants in the Mass General Brigham (MGB) Biobank. To correct for selection bias, we fitted logistic regression models with inverse probability (IP) weights, which were estimated using 1839 sociodemographic, clinical, and healthcare utilization features extracted from electronic health records of 1 546 440 non-Hispanic White patients eligible to participate in the Biobank study at their first visit to the MGB-affiliated hospitals. Results Case prevalence of bipolar disorder among participants in the top decile of bipolar disorder PRS was 10.0% (95% CI 8.8–11.2%) in the unweighted analysis but only 6.2% (5.0–7.5%) when selection bias was accounted for using IP weights. Similarly, case prevalence of depression among those in the top decile of depression PRS was reduced from 33.5% (31.7–35.4%) to 28.9% (25.8–31.9%) after IP weighting. Conclusions Non-random selection of participants into volunteer biobanks may induce clinically relevant selection bias that could impact implementation of PRS in research and clinical settings. As efforts to integrate PRS in medical practice expand, recognition and mitigation of these biases should be considered and may need to be optimized in a context-specific manner.

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“…Random forests are popular for its ability to model complex and non-linear interactions of effects. Random forests have also proven successful in the biomedical community (Antoniadi, Galvin, Heverin, Hardiman, & Mooney, 2021;Bayramli et al, 2021;Chen & Ishwaran, 2012;Hu & Steingrimsson, 2018;Kim, Yoo, Oh, & Kim, 2013;Wongvibulsin, Wu, & Zeger, 2019).…”

Section: Random Forestmentioning

confidence: 99%

Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure

et al. 2022

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Background Posttraumatic stress symptoms (PTSS) are common following traumatic stress exposure (TSE). Identification of individuals with PTSS risk in the early aftermath of TSE is important to enable targeted administration of preventive interventions. In this study, we used baseline survey data from two prospective cohort studies to identify the most influential predictors of substantial PTSS. Methods Self-identifying black and white American women and men (n = 1546) presenting to one of 16 emergency departments (EDs) within 24 h of motor vehicle collision (MVC) TSE were enrolled. Individuals with substantial PTSS (⩾33, Impact of Events Scale – Revised) 6 months after MVC were identified via follow-up questionnaire. Sociodemographic, pain, general health, event, and psychological/cognitive characteristics were collected in the ED and used in prediction modeling. Ensemble learning methods and Monte Carlo cross-validation were used for feature selection and to determine prediction accuracy. External validation was performed on a hold-out sample (30% of total sample). Results Twenty-five percent (n = 394) of individuals reported PTSS 6 months following MVC. Regularized linear regression was the top performing learning method. The top 30 factors together showed good reliability in predicting PTSS in the external sample (Area under the curve = 0.79 ± 0.002). Top predictors included acute pain severity, recovery expectations, socioeconomic status, self-reported race, and psychological symptoms. Conclusions These analyses add to a growing literature indicating that influential predictors of PTSS can be identified and risk for future PTSS estimated from characteristics easily available/assessable at the time of ED presentation following TSE.

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Temporally informed random forests for suicide risk prediction

Cited by 17 publications

References 20 publications

Predictive structured–unstructured interactions in EHR models: A case study of suicide prediction

Predictive structured–unstructured interactions in EHR models: A case study of suicide prediction

Impact of selection bias on polygenic risk score estimates in healthcare settings

Derivation and validation of risk prediction for posttraumatic stress symptoms following trauma exposure

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