Nosocomial Risk Datasets from MIMIC-III

Reliable longitudinal risk prediction for hospitalized patients is needed to provide quality care. Our goal is to foster the development of generalizable models capable of leveraging clinical notes to predict healthcare-associated diseases 24–96 hours in advance. We developed data to explore the problem of predicting the risk of hospital acquired (occurring ≥ \geq 48 hours after admission) acute kidney injury, pressure injury, or anemia ≥ \geq 24 hours before it is implicated by the patient’s chart, labs, or notes. We relied on the MIMIC-III critical care database and extract distinct positive and negative cohorts for each disease. We retrospectively determine the date-of-event using structured and unstructured criteria so that it may be used as a form of indirect supervision to train and evaluate automatic systems for predicting disease risk using clinical notes. This data was used as the experimental basis for the CANTRIP project.

为确保住院患者获得高质量的护理，迫切需要可靠的纵向风险预测模型。本研究的宗旨在于培育具有普遍适用性的模型，此类模型能够利用临床记录，提前24至96小时预测与医疗相关的疾病。本研究开发的数据旨在探讨预测医院获得性（入院后≥48小时发生）急性肾损伤、压力性损伤或贫血（患者病历、实验室检查或记录中提及前≥24小时）风险的问题。我们依托MIMIC-III重症监护数据库，为每种疾病提取了独立的阳性组和阴性组。我们通过结构化和非结构化标准回顾性地确定事件发生日期，以便将其用作间接监督的形式，用于训练和评估基于临床记录预测疾病风险的自动系统。这些数据被用作CANTRIP项目的实验基础。