Missing social history makes automated medical coding challenging

Prerequisites

• [ x] Put an X between the brackets on this line if you have done all of the following:
  • Checked the online documentation: https://mimic.mit.edu/
  • Checked that your issue isn't already addressed: https://github.com/MIT-LCP/mimic-code/issues?utf8=%E2%9C%93&q=

Description

Automated medical coding (also called medical code prediction) is a growing machine learning task that aims to predict medical codes given a discharge summary. MIMIC-IV has become a popular dataset to train and evaluate such models. However, there is an issue. Since your de-identification algorithm removed the social history section, certain annotated medical codes are impossible to predict. For instance, the medical codes representing whether the patient smokes (e.g., F17.210 and Z87.891) are often annotated in MIMIC-IV without being mentioned in the discharge summary. This is because of the missing social history.

The consequences of the missing section are that the models are trained on labels that are impossible to predict and are evaluated unfairly every time the necessary information would have been in the social history. Consequently, MIMIC-IV is a noisier dataset for automated medical coding than MIMIC-III (MIMIC-III contains the social history).

Is there a way to de-identify the discharge summaries without removing the social histories?

首先查阅 MIMIC-IV 的在线文档，以及 issue 中关于缺失社会史和受影响代码的示例。确定出院总结是否能够在满足去标识化要求的同时保留社会史；要认定完成，需要一种有文档记录且经过验证的方法，而不是对仓库进行小幅修改。