Language
English
Publication Date
1-14-2024
Journal
Journal of Personalized Medicine
DOI
10.3390/jpm14010094
PMID
38248795
PMCID
PMC10817272
PubMedCentral® Posted Date
1-14-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Prediction of high-risk events amongst patients with mental disorders is critical for personalized interventions. We developed DeepBiomarker2 by leveraging deep learning and natural language processing to analyze lab tests, medication use, diagnosis, social determinants of health (SDoH) parameters, and psychotherapy for outcome prediction. To increase the model's interpretability, we further refined our contribution analysis to identify key features by scaling with a factor from a reference feature. We applied DeepBiomarker2 to analyze the EMR data of 38,807 patients from the University of Pittsburgh Medical Center diagnosed with post-traumatic stress disorder (PTSD) to determine their risk of developing alcohol and substance use disorder (ASUD). DeepBiomarker2 predicted whether a PTSD patient would have a diagnosis of ASUD within the following 3 months with an average c-statistic (receiver operating characteristic AUC) of 0.93 and average F1 score, precision, and recall of 0.880, 0.895, and 0.866 in the test sets, respectively. Our study found that the medications clindamycin, enalapril, penicillin, valacyclovir, Xarelto/rivaroxaban, moxifloxacin, and atropine and the SDoH parameters access to psychotherapy, living in zip codes with a high normalized vegetative index, Gini index, and low-income segregation may have potential to reduce the risk of ASUDs in PTSD. In conclusion, the integration of SDoH information, coupled with the refined feature contribution analysis, empowers DeepBiomarker2 to accurately predict ASUD risk. Moreover, the model can further identify potential indicators of increased risk along with medications with beneficial effects.
Keywords
post-traumatic stress disorder, alcohol, and substance use disorder, social determinants of health, psychotherapy, natural language processing, deep learning, biomarker identification
Published Open-Access
yes
Recommended Citation
Miranda, Oshin; Fan, Peihao; Qi, Xiguang; et al., "DeepBiomarker2: Prediction of Alcohol and Substance Use Disorder Risk in Post-Traumatic Stress Disorder Patients Using Electronic Medical Records and Multiple Social Determinants of Health" (2024). Faculty, Staff and Students Publications. 6038.
https://digitalcommons.library.tmc.edu/baylor_docs/6038
Included in
Medical Sciences Commons, Medical Specialties Commons, Mental and Social Health Commons, Psychiatry and Psychology Commons