pytorch implementation for the CAMP model in ICDM2019 paper "CAMP: Co-Attention Memory Networks for Diagnosis Prediction in Healthcare"

Packages

• python 3.6
• torch 1.0.1
• numpy 1.15.4

Run

• Step1: Create a folder named "data" and two sub-folders named "ccs" and "mimic". Then download the CCS-single-level file and CCS-multi-level file from https://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp into the "ccs" folder. Download the mimic files "PATIENTS.csv", "DIAGNOSES _ICD.csv" and "ADMISSIONS.csv" from https://mimic.physionet.org/ into the "mimic" folder.
• Step2: Use processMIMIC.py to extract patient historical records and demographics from MIMIC-III tables. After this step, we get "mimic.seqs", "mimic.profiles" and "mimic.pids" in the "mimic" folder.
• Step3: Use preprocess.py to split the dataset into three parts: training set, validation set and testing set. After this step, we get "mimic.train", "mimic.valid" and "mimic.test" in the "mimic" folder.
• Step4: Use mkHierarchy.py to extract the taxonomy from the CCS-multi-level file. After this step, we get "mimic.forgram" in the "mimic" folder.
• Step5: With "mimic.train", "mimic.valid", "mimic.test", and "mimic.forgram" ready in the "mimic" folder, please use run.py to run our model, including training, validation in each epoch, test and evaluation.