Resources

This study presents MIMIC-III-Ext-SBDH, a manually reviewed and consolidated annotation dataset of social and behavioral determinants of health (SBDH) derived from clinical notes in the MIMIC-III database. We previously developed SBDH-Reader, a larg…

Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented within clinical notes. As large language models (LLMs) are becoming more …

Electronic Health Records (EHR) contain patient records, stored in structured tables as well as unstructured clinical notes. The information in structured and unstructured EHR records is not strictly disjoint: information may be duplicated, contradi…

Automatically summarizing patients’ main problems from the daily care notes in the electronic health record can help mitigate information and cognitive overload for clinicians and provide augmented intelligence via computerized diagnostic decision s…

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The databas…

MIMIC is one of the most widely used public hospital datasets for machine learning on electronic health records [3], but its structured tables answer many clinical questions only through coded surrogates, or not at all. For example, a patient's past…

MIMIC-IV-Ext-SDBench contains 488 case records derived from MIMIC-IV emergency department encounters in which the patient was subsequently admitted as an inpatient. Each case is formatted primarily for use with the Sequential Diagnosis Benchmark, an…