Meeting Global Health Needs via Infectious Disease Forecasting: Development of a Reliable Data-Driven Framework

Abstract BackgroundInfectious diseases (IDs) have a significant detrimental impact on global health. Timely and accurate ID forecasting can result in more informed implementation of control measures and prevention policies. ObjectiveTo meet the operational decision-making needs of real-world circumstances, we aimed to build a standardized, reliable, and trustworthy ID forecasting pipeline and visualization dashboard that is generalizable across a wide range of modeling techniques, IDs, and global locations. MethodsWe forecasted 6 diverse, zoonotic diseases (brucellosis, campylobacteriosis, Middle East respiratory syndrome, Q fever, tick-borne encephalitis, and tularemia) across 4 continents and 8 countries. We included a wide range of statistical, machine learning, and deep learning models (n=9) and trained them on a multitude of features (average n=2326) within the One Health landscape, including demography, landscape, climate, and socioeconomic factors. The pipeline and dashboard were created in consideration of crucial operational metrics—prediction accuracy, computational efficiency, spatiotemporal generalizability, uncertainty quantification, and interpretability—which are essential to strategic data-driven decisions. ResultsWhile no single best model was suitable for all disease, region, and country combinations, our ensemble technique selects the best-performing model for each given scenario to achieve the closest prediction. For new or emerging diseases in a region, the ensemble model can predict how the disease may behave in the new region using a pretrained model from a similar region with a history of that disease. The data visualization dashboard provides a clean interface of important analytical metrics, such as ID temporal patterns, forecasts, prediction uncertainties, and model feature importance across all geographic locations and disease combinations. ConclusionsAs the need for real-time, operational ID forecasting capabilities increases, this standardized and automated platform for data collection, analysis, and reporting is a major step forward in enabling evidence-based public health decisions and policies for the prevention and mitigation of future ID outbreaks

Preparing For The Next Pandemic: Transfer Learning From Existing Diseases Via Hierarchical Multi-Modal BERT Models to Predict COVID-19 Outcomes

Abstract Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model (TransMED) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of TransMED's predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. TransMED's superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.</jats:p

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

AbstractDeveloping prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model ({\textsc {TransMED}} T R A N S MED ) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of {\textsc {TransMED}} T R A N S MED ’s predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. {\textsc {TransMED}} T R A N S MED ’s superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.</jats:p

54. PREDICTION OF ANTIPSYCHOTIC-INDUCED METABOLIC ADVERSE EFFECTS USING MULTIMODAL ARTIFICIAL INTELLIGENCE

Antipsychotic medications are a mainstay of pharmacotherapy of psychiatric illnesses but have been shown to cause considerable metabolic adverse effects such as weight gain, diabetes, and hypercholesterolemia. Advancing methods to predict such effect could spur the development of Precision Psychiatry modalities. We examined two cohorts of veterans receiving antipsychotics: the Corporate Data Warehouse (CDW, N=869,128) and the Million Veteran Program (MVP, N= 137,771 genotyped). We integrated multiple modalities of patient electronic Health Record (EHR) data including demographics, diagnoses, drug codes, and lab results, along with polygenic risk scores (PRSs) of psychiatric (ANX, BIP, MDD, SCZ) and metabolic (obesity, LDL, HDL, TGL and T2D) traits, to predict patient metabolic outcomes using multi-modal BERT architecture. In the CDW cohort, clinically significant weight gain (> 7%) during antipsychotic use was related to Asian ethnicity, pre-treatment elevation in triglycerides, and use of thiothixene, systemic contraceptives, and antipsoriatics, but inversely related to antimigraine agents, opioid antagonist analgesics, and immune suppressants. In the MVP cohort, BMI increase was related to Hispanic ancestry, first-generation antipsychotics, older age, higher T2D PRS, and inversely related to BP PRS. This is the largest study to date of genetic and environmental factors associated with antipsychotic-induced metabolic adverse effects. While our results require replication in independent samples, they suggest that multimodal AI could be useful in the identification of both risk and protective factors of psychotropic adverse effects and therefore, a potentially powerful tool in Precision Psychiatry. Nothing to disclose