David W. Bates

Yining Hua, Hang Jiang, Shixu Lin et al. · amazon-science, harvard

Understanding public discourse on emergency use of unproven therapeutics is crucial for monitoring safe use and combating misinformation. We developed a natural language processing-based pipeline to comprehend public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter over time. This retrospective study included 609,189 US-based tweets from January 29, 2020, to November 30, 2021, about four drugs that garnered significant public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatments for eligible patients. Time-trend analysis was employed to understand popularity trends and related events. Content and demographic analyses were conducted to explore potential rationales behind people's stances on each drug. Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin discussions were highly politicized, related to conspiracy theories, hearsay, and celebrity influences. The distribution of stances between the two major US political parties was significantly different (P < .001); Republicans were more likely to support Hydroxychloroquine (55%) and Ivermectin (30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (7%) more than the general population, while the general population was more likely to support Ivermectin (14%). Our study found that social media users have varying perceptions and stances on off-label versus FDA-authorized drug use at different stages of COVID-19. This indicates that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation to promote safe drug use.

Pawel Renc, Yugang Jia, Anthony E. Samir et al.

Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a novel application of the transformer deep-learning architecture for analyzing high-dimensional, heterogeneous, and episodic health data. ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories, leveraging a zero-shot learning approach. ETHOS represents a significant advancement in foundation model development for healthcare analytics, eliminating the need for labeled data and model fine-tuning. Its ability to simulate various treatment pathways and consider patient-specific factors positions ETHOS as a tool for care optimization and addressing biases in healthcare delivery. Future developments will expand ETHOS' capabilities to incorporate a wider range of data types and data sources. Our work demonstrates a pathway toward accelerated AI development and deployment in healthcare.

Pawel Renc, Michal K. Grzeszczyk, Nassim Oufattole et al.

Hospitals struggle to predict critical outcomes. Traditional early warning systems, like NEWS and MEWS, rely on static variables and fixed thresholds, limiting their adaptability, accuracy, and personalization. We previously developed the Enhanced Transformer for Health Outcome Simulation (ETHOS), an AI model that tokenizes patient health timelines (PHTs) from EHRs and uses transformer-based architectures to predict future PHTs. ETHOS is a versatile framework for developing a wide range of applications. In this work, we develop the Adaptive Risk Estimation System (ARES) that leverages ETHOS to compute dynamic, personalized risk probabilities for clinician-defined critical events. ARES also features a personalized explainability module that highlights key clinical factors influencing risk estimates. We evaluated ARES using the MIMIC-IV v2.2 dataset together with its Emergency Department (ED) extension and benchmarked performance against both classical early warning systems and contemporary machine learning models. The entire dataset was tokenized resulting in 285,622 PHTs, comprising over 360 million tokens. ETHOS outperformed benchmark models in predicting hospital admissions, ICU admissions, and prolonged stays, achieving superior AUC scores. Its risk estimates were robust across demographic subgroups, with calibration curves confirming model reliability. The explainability module provided valuable insights into patient-specific risk factors. ARES, powered by ETHOS, advances predictive healthcare AI by delivering dynamic, real-time, personalized risk estimation with patient-specific explainability. Although our results are promising, the clinical impact remains uncertain. Demonstrating ARES's true utility in real-world settings will be the focus of our future work. We release the source code to facilitate future research.