Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care. Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.
Université Claude Bernard - Lyon 1（里昂第一大学（克洛德·贝尔纳大学））；New York University Abu Dhabi（纽约大学阿布扎比分校）
Social media can be an important source of information facilitating the detection of new safety signals in pharmacovigilance. Various approaches have investigated the analysis of social media data using AI such as NLP techniques for detecting adverse drug events. Existing approaches have focused on the extraction and identification of Adverse Drug Reactions, Drug-Drug Interactions and drug misuse. However, non of the works tackled the detection of potential safety signals by taking into account the evolution in time of relevant indicators. Moreover, despite the success of deep learning in vari…
Large language models provide unsafe answers to patient-posed medical questions
Glass Box Medicine；University of Virginia（弗吉尼亚大学）；Riverside Community Hospital（河滨社区医院）；Inova Schar Heart and Vascular（Inova Schar 心脏与血管研究所）；Inova Fairfax Medical Campus（Inova Fairfax 医疗园区）；Cooper University Hospital（库珀大学医院）；South Texas Research Institute（南德克萨斯研究所）；University of California（加利福尼亚大学）；Valley View Hospital（Valley View 医院）；University of Louisville（路易斯维尔大学）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）；Northwell Health；Emory University（埃默里大学）；Brigham and Women's Hospital（布莱根妇女医院）；Castilleja School（卡斯蒂列亚学校）；Piedmont Healthcare；AmyMD Wellness
University of Warwick（华威大学）；Northeastern University（东北大学）；AstraZeneca（阿斯利康）；King’s College London（伦敦国王学院）；The Alan Turing Institute（阿兰·图灵研究所）
The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients. In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals. Unfortunately, public resource…
Rethinking Patient Education as Multi-turn Multi-modal Interaction
VA Bedford Health Care（VA贝德福德医疗中心）；UMass Amherst（马萨诸塞大学阿默斯特分校）；UMass Lowell（马萨诸塞大学洛厄尔分校）；Yale University（耶鲁大学）；National University of Singapore（新加坡国立大学）；Yale School of Medicine（耶鲁医学院）
MedRiskEval: Medical Risk Evaluation Benchmark of Language Models, On the Importance of User Perspectives in Healthcare Settings
Microsoft Healthcare & Life Sciences（微软医疗与生命科学）；Microsoft Research Montréal, Canada（微软研究蒙特利尔分校）；Université catholique de Louvain, Belgium（列日大学）；Mila, Université de Montréal, Canada（蒙特利尔大学Mila）
