急诊分诊患者数据集

数据集包含了北大人民医院的4095例急诊分诊患者数据，提取了急诊分诊患者的异构医疗数据，包含姓名、性别等人口信息，分诊时间、分诊印象、分诊耗时、分诊级别、分诊科室、是否二次分诊等分诊信息，体温、脉搏、心率、血氧等生命体征信息，流行病学史、群伤等病史信息。急诊分诊是一种生命攸关的医疗行为，需要在短时间内快速准确判定疾病分诊与分级。为了解决这一决策问题，可以先使用恰当的编码器将该医疗数据转换成机器可读的数据类型，再使用合适的机器学习模型进行训练与决策，以帮助临床医生推荐急诊分诊分级的最佳参考方案，为真实复杂医疗场景下的实时、精准医疗决策提供辅助支持。

This dataset contains 4095 emergency triage patient records from Peking University People's Hospital. It collects heterogeneous medical data of emergency triage patients, including demographic information such as name and gender, triage-related information such as triage time, triage impression, triage duration, triage level, triage department, and whether secondary triage was performed, vital signs such as body temperature, pulse, heart rate, and blood oxygen saturation, as well as medical history information including epidemiological history and mass injury history. Emergency triage is a life-critical medical practice that requires rapid and accurate determination of disease triage and grading within a short timeframe. To address this decision-making challenge, appropriate encoders can first be used to convert the medical data into machine-readable formats, followed by training and decision-making with suitable machine learning models, to help clinicians recommend optimal reference schemes for emergency triage grading, and provide auxiliary support for real-time and precise medical decision-making in real-world complex medical scenarios.