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

Miranda, Oshin; Fan, Peihao; Qi, Xiguang; Wang, Haohan; Brannock, M. Daniel; Kosten, Thomas R.; Ryan, Neal David; Kirisci, Levent; Wang, Lirong

doi:10.3390/jpm14010094

Cited by 3 publications

(1 citation statement)

References 111 publications

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“…Employing a two-stage virtual twins model (random forest + decision tree), the research identified factors influencing completion probability (e.g., race/ethnicity, income source), revealing that those without co-occurring mental health conditions, with job-related income, and white non-Hispanics are more likely to complete treatment. Miranda, et al [12] employed deep learning and natural language processing to develop DeepBiomarker2 that accurately predicts alcohol and substance use disorder risk in post-traumatic stress disorder patients and identifies medications and social determinants of health parameters that may reduce this risk. Adams, et al [13] performed a study in Denmark that focused on individuals with substance use disorders (SUDs) and their elevated suicide risk.…”

Section: Introductionmentioning

confidence: 99%

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

Acharya,

Kar,

Ally

et al. 2024

Applied Sciences

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Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental health (MH) and substance use disorders (SUD) among women. By employing various modeling algorithms for binary classification, including Random Forest, Gradient Boosted Trees, XGBoost, Extra Trees, SGD, Deep Neural Network, Single-Layer Perceptron, K Nearest Neighbors (grid), and a super learning model (constructed by combining the predictions of a Random Forest model and an XGBoost model), the research aims to provide healthcare practitioners with a powerful tool for earlier identification, intervention, and personalised support for women at risk. The present research presents a machine learning (ML) methodology for more accurately predicting the co-occurrence of mental health (MH) and substance use disorders (SUD) in women, utilising the Treatment Episode Data Set Admissions (TEDS-A) from the year 2020 (n = 497,175). A super learning model was constructed by combining the predictions of a Random Forest model and an XGBoost model. The model demonstrated promising predictive performance in predicting co-occurring MH and SUD in women with an AUC = 0.817, Accuracy = 0.751, Precision = 0.743, Recall = 0.926 and F1 Score = 0.825. The use of accurate prediction models can substantially facilitate the prompt identification and implementation of intervention strategies.

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Section: Introductionmentioning

confidence: 99%

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

Acharya,

Kar,

Ally

et al. 2024

Applied Sciences

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Opportunities for utilizing hospital data to improve medical care quality and patient safety

Skaletskyі,

Yavorovskyі,

Brukhno

et al. 2024

Dovkil Zdor

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Objective. The purpose was to investigate the use of hospital data to enhance patient safety, while also considering hygiene and occupational safety concerns for medical staff. Materials and methods. In the course of the work, bibliosemantic, questionnaire-survey, hygienic and statistical research methods were used. Research results. The utilization of hospital data proves beneficial for improving the quality and safety of medical care and enhancing the efficiency of healthcare facilities. Despite the existing data collection system in healthcare, the national regulatory framework practically overlooks the issues of utilizing this data to enhance the performance of hospital institutions. A significant challenge in the effective use of medical statistics data is their generalized nature, which could be adressed through the implementation of an electronic healthcare system. A relatively detailed analysis of medical records of deceased patients only confirms the importance of developing organizational measures and recommendations that could improve the quality and safety of medical care not only within specific healthcare institutions but also within the healthcare system as a whole. Conclusions. Enhancing the regulatory framework regarding the utilization of medical statistics data is a pertinent task for domestic science and practice.

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Integrating Drug Target Information in Deep Learning Models to Predict the Risk of Adverse Events in Patients with Comorbid Post-Traumatic Stress Disorder and Alcohol Use Disorder

Miranda,

Qi,

Brannock

et al. 2024

Biomedicines

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Background/Objectives: Comorbid post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD) patients are at a significantly higher risk of adverse outcomes, including opioid use disorder, depression, suicidal behaviors, and death, yet limited treatment options exist for this population. This study aimed to build on previous research by incorporating drug target information into a novel deep learning model, T-DeepBiomarker, to predict adverse outcomes and identify potential therapeutic medications. Methods: We utilized electronic medical record (EMR) data from the University of Pittsburgh Medical Center (UPMC), analyzing 5565 PTSD + AUD patients. T-DeepBiomarker was developed by integrating multimodal data, including lab results, drug target information, comorbidities, neighborhood-level social determinants of health (SDoH), and individual-level SDoH (e.g., psychotherapy and veteran status). The model was trained to predict adverse events, including opioid use disorder, suicidal behaviors, depression, and death, within three months following any clinical encounter. Candidate medications targeting significant proteins were identified through literature reviews. Results: T-DeepBiomarker achieved high predictive performance with an AUROC of 0.94 for adverse outcomes in PTSD + AUD patients. Several medications, including OnabotulinumtoxinA, Dronabinol, Acamprosate, Celecoxib, Exenatide, Melatonin, and Semaglutide, were identified as potentially reducing the risk of adverse events by targeting significant proteins. Conclusions: T-DeepBiomarker demonstrates high accuracy in predicting adverse outcomes in PTSD + AUD patients and highlights candidate drugs with potential therapeutic effects. These findings advance pharmacotherapy for this high-risk population and identify medications that warrant further investigation.

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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

Cited by 3 publications

References 111 publications

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

Opportunities for utilizing hospital data to improve medical care quality and patient safety

Integrating Drug Target Information in Deep Learning Models to Predict the Risk of Adverse Events in Patients with Comorbid Post-Traumatic Stress Disorder and Alcohol Use Disorder

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