Medical Code Prediction

The Medical Information Mart for Intensive Care III (MIMIC-III) dataset is a large, de-identified and publicly-available collection of medical records. Each record in the dataset includes ICD-9 codes, which identify diagnoses and procedures performed. Each code is partitioned into sub-codes, which often include specific circumstantial details. The dataset consists of 112,000 clinical reports records (average length 709.3 tokens) and 1,159 top-level ICD-9 codes. Each report is assigned to 7.6 codes, on average. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. Source: MIT Laboratory for Computational Biology

MIMIC-IV ICD-10 contains 122,279 discharge summaries—free-text medical documents—annotated with ICD-10 diagnosis and procedure codes. It contains data for patients admitted to the Beth Israel Deaconess Medical Center emergency department or ICU between 2008-2019. All codes with fewer than ten examples have been removed, and the train-val-test split was created using [multi-label stratified sampling](MIMIC-IV ICD-9). The dataset is described further in Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study, and the code to use the dataset is found here. The dataset is intended for medical code prediction and was created using MIMIC-IV v2.2 and MIMIC-IV-NOTE v2.2. Using the two datasets requires a license obtained in Physionet; this can take a couple of days.

The MIMIC-IV-ICD10-full dataset, including occurring labels.

The MIMIC-IV-ICD9 dataset, including all occurring labels.

MIMIC-IV ICD-9 contains 209,326 discharge summaries—free-text medical documents—annotated with ICD-9 diagnosis and procedure codes. It contains data for patients admitted to the Beth Israel Deaconess Medical Center emergency department or ICU between 2008-2019. All codes with fewer than ten examples have been removed, and the train-val-test split was created using [multi-label stratified sampling](MIMIC-IV ICD-9). The dataset is described further in Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study, and the code to use the dataset is found here. The dataset is intended for medical code prediction and was created using MIMIC-IV v2.2 and MIMIC-IV-NOTE v2.2. Using the two datasets requires a license obtained in Physionet; this can take a couple of days.

The MIMIC-IV-ICD10 dataset, featuring the top 50 most frequently occurring labels.

The MIMIC-IV-ICD9 dataset, featuring the top 50 most frequently occurring labels.