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import os |
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import pandas as pd |
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import datasets |
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import sys |
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import pickle |
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import subprocess |
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import shutil |
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from urllib.request import urlretrieve |
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from sklearn.model_selection import train_test_split |
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from sklearn.preprocessing import LabelEncoder |
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import numpy as np |
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from tqdm import tqdm |
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import yaml |
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import time |
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import torch |
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_DESCRIPTION = """\ |
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Dataset for mimic4 data, by default for the Mortality task. |
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Available tasks are: Mortality, Length of Stay, Readmission, Phenotype. |
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The data is extracted from the mimic4 database using this pipeline: 'https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main' |
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mimic path should have this form : "path/to/mimic4data/from/username/mimiciv/2.2" |
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If you choose a Custom task provide a configuration file for the Time series. |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset" |
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_CITATION = "https://proceedings.mlr.press/v193/gupta22a.html" |
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_URL = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline" |
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_DATA_GEN = 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/data_generation_icu_modify.py' |
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_DATA_GEN_HOSP= 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/data_generation_modify.py' |
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_DAY_INT= 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/day_intervals_cohort_v22.py' |
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_CONFIG_URLS = {'los' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/los.config', |
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'mortality' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/mortality.config', |
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'phenotype' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/phenotype.config', |
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'readmission' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/readmission.config' |
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} |
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def check_config(task,config_file): |
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with open(config_file) as f: |
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config = yaml.safe_load(f) |
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if task=='Phenotype': |
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disease_label = config['disease_label'] |
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else : |
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disease_label = "" |
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time = config['timePrediction'] |
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label = task |
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timeW = config['timeWindow'] |
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include=int(timeW.split()[1]) |
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bucket = config['timebucket'] |
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radimp = config['radimp'] |
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predW = config['predW'] |
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disease_filter = config['disease_filter'] |
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icu_no_icu = config['icu_no_icu'] |
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groupingDiag = config['groupingDiag'] |
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assert( icu_no_icu in ['ICU','Non-ICU' ], "Chossen data should be one of the following: ICU, Non-ICU") |
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data_icu = icu_no_icu=='ICU' |
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if data_icu: |
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chart_flag = config['chart'] |
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output_flag = config['output'] |
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select_chart = config['select_chart'] |
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lab_flag = False |
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select_lab = False |
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else: |
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lab_flag =config['lab'] |
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select_lab = config['select_lab'] |
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groupingMed = config['groupingMed'] |
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groupingProc = config['groupingProc'] |
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chart_flag = False |
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output_flag = False |
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select_chart = False |
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diag_flag= config['diagnosis'] |
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proc_flag = config['proc'] |
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meds_flag = config['meds'] |
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select_diag= config['select_diag'] |
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select_med= config['select_med'] |
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select_proc= config['select_proc'] |
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select_out = config['select_out'] |
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outlier_removal=config['outlier_removal'] |
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thresh=config['outlier'] |
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left_thresh=config['left_outlier'] |
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if data_icu: |
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assert (isinstance(select_diag,bool) and isinstance(select_med,bool) and isinstance(select_proc,bool) and isinstance(select_out,bool) and isinstance(select_chart,bool), " select_diag, select_chart, select_med, select_proc, select_out should be boolean") |
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assert (isinstance(chart_flag,bool) and isinstance(output_flag,bool) and isinstance(diag_flag,bool) and isinstance(proc_flag,bool) and isinstance(meds_flag,bool), "chart_flag, output_flag, diag_flag, proc_flag, meds_flag should be boolean") |
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else: |
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assert (isinstance(select_diag,bool) and isinstance(select_med,bool) and isinstance(select_proc,bool) and isinstance(select_out,bool) and isinstance(select_lab,bool), " select_diag, select_lab, select_med, select_proc, select_out should be boolean") |
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assert (isinstance(lab_flag,bool) and isinstance(diag_flag,bool) and isinstance(proc_flag,bool) and isinstance(meds_flag,bool), "lab_flag, diag_flag, proc_flag, meds_flag should be boolean") |
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if task=='Phenotype': |
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if disease_label=='Heart Failure': |
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label='Readmission' |
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time=30 |
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disease_label='I50' |
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elif disease_label=='CAD': |
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label='Readmission' |
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time=30 |
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disease_label='I25' |
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elif disease_label=='CKD': |
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label='Readmission' |
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time=30 |
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disease_label='N18' |
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elif disease_label=='COPD': |
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label='Readmission' |
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time=30 |
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disease_label='J44' |
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else : |
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raise ValueError('Disease label not correct provide one in the list: Heart Failure, CAD, CKD, COPD') |
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predW=0 |
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assert (timeW[0]=='Last' and include<=72 and include>=24, "Time window should be between Last 24 and Last 72") |
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elif task=='Mortality': |
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time=0 |
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label= 'Mortality' |
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assert (predW<=8 and predW>=2, "Prediction window should be between 2 and 8") |
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assert (timeW[0]=='Fisrt' and include<=72 and include>=24, "Time window should be between First 24 and First 72") |
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elif task=='Length of Stay': |
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label= 'Length of Stay' |
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assert (timeW[0]=='Fisrt' and include<=72 and include>=24, "Time window should be between Fisrt 24 and Fisrt 72") |
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assert (time<=10 and time>=1, "Length of stay should be between 1 and 10") |
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predW=0 |
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elif task=='Readmission': |
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label= 'Readmission' |
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assert (timeW[0]=='Last' and include<=72 and include>=24, "Time window should be between Last 24 and Last 72") |
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assert (time<=150 and time>=10 and time%10==0, "Readmission window should be between 10 and 150 with a step of 10") |
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predW=0 |
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else: |
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raise ValueError('Task not correct') |
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assert( disease_filter in ['Heart Failure','COPD','CKD','CAD',""], "Disease filter should be one of the following: Heart Failure, COPD, CKD, CAD or empty") |
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assert( groupingDiag in ['Convert ICD-9 to ICD-10 and group ICD-10 codes','Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes'], "Grouping ICD should be one of the following: Convert ICD-9 to ICD-10 and group ICD-10 codes, Keep both ICD-9 and ICD-10 codes, Convert ICD-9 to ICD-10 codes") |
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assert (bucket<=6 and bucket>=1 and isinstance(bucket, int), "Time bucket should be between 1 and 6 and an integer") |
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assert (radimp in ['No Imputation', 'forward fill and mean','forward fill and median'], "imputation should be one of the following: No Imputation, forward fill and mean, forward fill and median") |
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if chart_flag: |
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assert (left_thresh>=0 and left_thresh<=10 and isinstance(left_thresh, int), "Left outlier threshold should be between 0 and 10 and an integer") |
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assert (thresh>=90 and thresh<=99 and isinstance(thresh, int), "Outlier threshold should be between 90 and 99 and an integer") |
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assert (outlier_removal in ['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'], "Outlier removal should be one of the following: No outlier detection, Impute Outlier (default:98), Remove outliers (default:98)") |
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if lab_flag: |
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assert (left_thresh>=0 and left_thresh<=10 and isinstance(left_thresh, int), "Left outlier threshold should be between 0 and 10 and an integer") |
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assert (thresh>=90 and thresh<=99 and isinstance(thresh, int), "Outlier threshold should be between 90 and 99 and an integer") |
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assert (outlier_removal in ['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'], "Outlier removal should be one of the following: No outlier detection, Impute Outlier (default:98), Remove outliers (default:98)") |
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assert (groupingProc in ['ICD-9 and ICD-10','ICD-10'], "Grouping procedure should be one of the following: ICD-9 and ICD-10, ICD-10") |
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assert (groupingMed in ['Yes','No'], "Do you want to group Medication codes to use Non propietary names? : Grouping medication should be one of the following: Yes, No") |
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return label, time, disease_label, predW |
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def create_vocab(file,task): |
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with open ('./data/dict/'+task+'/'+file, 'rb') as fp: |
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condVocab = pickle.load(fp) |
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condVocabDict={} |
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condVocabDict[0]=0 |
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for val in range(len(condVocab)): |
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condVocabDict[condVocab[val]]= val+1 |
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return condVocabDict |
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def gender_vocab(): |
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genderVocabDict={} |
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genderVocabDict['<PAD>']=0 |
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genderVocabDict['M']=1 |
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genderVocabDict['F']=2 |
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return genderVocabDict |
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def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag): |
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condVocabDict={} |
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procVocabDict={} |
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medVocabDict={} |
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outVocabDict={} |
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chartVocabDict={} |
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labVocabDict={} |
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ethVocabDict={} |
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ageVocabDict={} |
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genderVocabDict={} |
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insVocabDict={} |
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ethVocabDict=create_vocab('ethVocab',task) |
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with open('./data/dict/'+task+'/ethVocabDict', 'wb') as fp: |
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pickle.dump(ethVocabDict, fp) |
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ageVocabDict=create_vocab('ageVocab',task) |
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with open('./data/dict/'+task+'/ageVocabDict', 'wb') as fp: |
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pickle.dump(ageVocabDict, fp) |
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genderVocabDict=gender_vocab() |
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with open('./data/dict/'+task+'/genderVocabDict', 'wb') as fp: |
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pickle.dump(genderVocabDict, fp) |
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insVocabDict=create_vocab('insVocab',task) |
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with open('./data/dict/'+task+'/insVocabDict', 'wb') as fp: |
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pickle.dump(insVocabDict, fp) |
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if diag_flag: |
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file='condVocab' |
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with open ('./data/dict/'+task+'/'+file, 'rb') as fp: |
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condVocabDict = pickle.load(fp) |
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if proc_flag: |
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file='procVocab' |
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with open ('./data/dict/'+task+'/'+file, 'rb') as fp: |
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procVocabDict = pickle.load(fp) |
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if med_flag: |
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file='medVocab' |
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with open ('./data/dict/'+task+'/'+file, 'rb') as fp: |
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medVocabDict = pickle.load(fp) |
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if out_flag: |
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file='outVocab' |
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with open ('./data/dict/'+task+'/'+file, 'rb') as fp: |
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outVocabDict = pickle.load(fp) |
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if chart_flag: |
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file='chartVocab' |
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with open ('./data/dict/'+task+'/'+file, 'rb') as fp: |
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chartVocabDict = pickle.load(fp) |
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if lab_flag: |
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file='labsVocab' |
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with open ('./data/dict/'+task+'/'+file, 'rb') as fp: |
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labVocabDict = pickle.load(fp) |
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return len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict |
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def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab): |
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meds=data['Med'] |
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proc = data['Proc'] |
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out = data['Out'] |
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chart = data['Chart'] |
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cond= data['Cond']['fids'] |
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cond_df=pd.DataFrame() |
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proc_df=pd.DataFrame() |
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out_df=pd.DataFrame() |
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chart_df=pd.DataFrame() |
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meds_df=pd.DataFrame() |
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demo=pd.DataFrame(columns=['Age','gender','ethnicity','label','insurance']) |
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new_row = {'Age': data['age'], 'gender': data['gender'], 'ethnicity': data['ethnicity'], 'label': data['label'], 'insurance': data['insurance']} |
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demo = demo.append(new_row, ignore_index=True) |
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if (feat_cond): |
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with open("./data/dict/"+task+"/condVocab", 'rb') as fp: |
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conDict = pickle.load(fp) |
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conds=pd.DataFrame(conDict,columns=['COND']) |
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features=pd.DataFrame(np.zeros([1,len(conds)]),columns=conds['COND']) |
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if(cond ==[]): |
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cond_df=pd.DataFrame(np.zeros([1,len(features)]),columns=features['COND']) |
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cond_df=cond_df.fillna(0) |
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else: |
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cond_df=pd.DataFrame(cond,columns=['COND']) |
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cond_df['val']=1 |
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cond_df=(cond_df.drop_duplicates()).pivot(columns='COND',values='val').reset_index(drop=True) |
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cond_df=cond_df.fillna(0) |
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oneh = cond_df.sum().to_frame().T |
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combined_df = pd.concat([features,oneh],ignore_index=True).fillna(0) |
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combined_oneh=combined_df.sum().to_frame().T |
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cond_df=combined_oneh |
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if (feat_proc): |
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with open("./data/dict/"+task+"/procVocab", 'rb') as fp: |
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procDic = pickle.load(fp) |
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if proc : |
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feat=proc.keys() |
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proc_val=[proc[key] for key in feat] |
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procedures=pd.DataFrame(procDic,columns=['PROC']) |
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features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC']) |
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features.columns=pd.MultiIndex.from_product([["PROC"], features.columns]) |
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procs=pd.DataFrame(columns=feat) |
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for p,v in zip(feat,proc_val): |
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procs[p]=v |
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procs.columns=pd.MultiIndex.from_product([["PROC"], procs.columns]) |
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proc_df = pd.concat([features,procs],ignore_index=True).fillna(0) |
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else: |
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procedures=pd.DataFrame(procDic,columns=['PROC']) |
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features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC']) |
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features.columns=pd.MultiIndex.from_product([["PROC"], features.columns]) |
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proc_df=features.fillna(0) |
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if (feat_out): |
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with open("./data/dict/"+task+"/outVocab", 'rb') as fp: |
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outDic = pickle.load(fp) |
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if out : |
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feat=out.keys() |
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out_val=[out[key] for key in feat] |
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outputs=pd.DataFrame(outDic,columns=['OUT']) |
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features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT']) |
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features.columns=pd.MultiIndex.from_product([["OUT"], features.columns]) |
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outs=pd.DataFrame(columns=feat) |
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for o,v in zip(feat,out_val): |
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outs[o]=v |
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outs.columns=pd.MultiIndex.from_product([["OUT"], outs.columns]) |
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out_df = pd.concat([features,outs],ignore_index=True).fillna(0) |
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else: |
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outputs=pd.DataFrame(outDic,columns=['OUT']) |
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features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT']) |
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features.columns=pd.MultiIndex.from_product([["OUT"], features.columns]) |
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out_df=features.fillna(0) |
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if (feat_chart): |
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with open("./data/dict/"+task+"/chartVocab", 'rb') as fp: |
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chartDic = pickle.load(fp) |
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if chart: |
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charts=chart['val'] |
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feat=charts.keys() |
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chart_val=[charts[key] for key in feat] |
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charts=pd.DataFrame(chartDic,columns=['CHART']) |
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART']) |
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features.columns=pd.MultiIndex.from_product([["CHART"], features.columns]) |
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chart=pd.DataFrame(columns=feat) |
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for c,v in zip(feat,chart_val): |
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chart[c]=v |
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chart.columns=pd.MultiIndex.from_product([["CHART"], chart.columns]) |
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chart_df = pd.concat([features,chart],ignore_index=True).fillna(0) |
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else: |
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charts=pd.DataFrame(chartDic,columns=['CHART']) |
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART']) |
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features.columns=pd.MultiIndex.from_product([["CHART"], features.columns]) |
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chart_df=features.fillna(0) |
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if (feat_lab): |
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with open("./data/dict/"+task+"/labsVocab", 'rb') as fp: |
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chartDic = pickle.load(fp) |
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if chart: |
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charts=chart['val'] |
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feat=charts.keys() |
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chart_val=[charts[key] for key in feat] |
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charts=pd.DataFrame(chartDic,columns=['LAB']) |
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB']) |
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features.columns=pd.MultiIndex.from_product([["LAB"], features.columns]) |
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chart=pd.DataFrame(columns=feat) |
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for c,v in zip(feat,chart_val): |
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chart[c]=v |
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chart.columns=pd.MultiIndex.from_product([["LAB"], chart.columns]) |
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chart_df = pd.concat([features,chart],ignore_index=True).fillna(0) |
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else: |
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charts=pd.DataFrame(chartDic,columns=['LAB']) |
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features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB']) |
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features.columns=pd.MultiIndex.from_product([["LAB"], features.columns]) |
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chart_df=features.fillna(0) |
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if (feat_meds): |
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with open("./data/dict/"+task+"/medVocab", 'rb') as fp: |
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medDic = pickle.load(fp) |
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if meds: |
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feat=meds['signal'].keys() |
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med_val=[meds['amount'][key] for key in feat] |
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meds=pd.DataFrame(medDic,columns=['MEDS']) |
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features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS']) |
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features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns]) |
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med=pd.DataFrame(columns=feat) |
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for m,v in zip(feat,med_val): |
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med[m]=v |
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med.columns=pd.MultiIndex.from_product([["MEDS"], med.columns]) |
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meds_df = pd.concat([features,med],ignore_index=True).fillna(0) |
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else: |
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meds=pd.DataFrame(medDic,columns=['MEDS']) |
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features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS']) |
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features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns]) |
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meds_df=features.fillna(0) |
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dyn_df = pd.concat([meds_df,proc_df,out_df,chart_df], axis=1) |
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return dyn_df,cond_df,demo |
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def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab): |
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stat_df = torch.zeros(size=(1,0)) |
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demo_df = torch.zeros(size=(1,0)) |
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meds = torch.zeros(size=(0,0)) |
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charts = torch.zeros(size=(0,0)) |
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proc = torch.zeros(size=(0,0)) |
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out = torch.zeros(size=(0,0)) |
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lab = torch.zeros(size=(0,0)) |
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stat_df = torch.zeros(size=(1,0)) |
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demo_df = torch.zeros(size=(1,0)) |
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size_cond, size_proc, size_meds, size_out, size_chart, size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False) |
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dyn,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab) |
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if feat_chart: |
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charts = dyn['CHART'] |
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charts=charts.to_numpy() |
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charts = torch.tensor(charts, dtype=torch.long) |
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charts = charts.tolist() |
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if feat_meds: |
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meds = dyn['MEDS'] |
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meds=meds.to_numpy() |
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meds = torch.tensor(meds, dtype=torch.long) |
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meds = meds.tolist() |
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if feat_proc: |
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proc = dyn['PROC'] |
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proc=proc.to_numpy() |
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proc = torch.tensor(proc, dtype=torch.long) |
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proc = proc.tolist() |
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if feat_out: |
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out = dyn['OUT'] |
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out=out.to_numpy() |
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out = torch.tensor(out, dtype=torch.long) |
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out=out.view(out.shape[1],out.shape[2]) |
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out = out.tolist() |
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if feat_lab: |
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lab = dyn['LAB'] |
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lab=lab.to_numpy() |
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lab = torch.tensor(lab, dtype=torch.long) |
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lab = lab.tolist() |
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|
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stat=cond_df |
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stat = stat.to_numpy() |
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stat = torch.tensor(stat) |
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if stat_df[0].nelement(): |
|
stat_df = torch.cat((stat_df,stat),0) |
|
else: |
|
stat_df = stat |
|
|
|
y = int(demo['label']) |
|
demo["gender"].replace(gender_vocab, inplace=True) |
|
demo["ethnicity"].replace(eth_vocab, inplace=True) |
|
demo["insurance"].replace(ins_vocab, inplace=True) |
|
demo["Age"].replace(age_vocab, inplace=True) |
|
demo=demo[["gender","ethnicity","insurance","Age"]] |
|
demo = demo.values |
|
demo = torch.tensor(demo) |
|
if demo_df[0].nelement(): |
|
demo_df = torch.cat((demo_df,demo),0) |
|
else: |
|
demo_df = demo |
|
stat_df = torch.tensor(stat_df) |
|
stat_df = stat_df.type(torch.LongTensor) |
|
stat_df = stat_df.squeeze() |
|
demo_df = torch.tensor(demo_df) |
|
demo_df = demo_df.type(torch.LongTensor) |
|
demo_df=demo_df.squeeze() |
|
y_df = torch.tensor(y) |
|
y_df = y_df.type(torch.LongTensor) |
|
|
|
return stat_df, demo_df, meds, charts, out, proc, lab, y_df |
|
|
|
def getXY(dyn,stat,demo,concat_cols,concat): |
|
X_df=pd.DataFrame() |
|
if concat: |
|
dyna=dyn.copy() |
|
dyna.columns=dyna.columns.droplevel(0) |
|
dyna=dyna.to_numpy() |
|
dyna=np.nan_to_num(dyna, copy=False) |
|
dyna=dyna.reshape(1,-1) |
|
dyn_df=pd.DataFrame(data=dyna,columns=concat_cols) |
|
else: |
|
dyn_df=pd.DataFrame() |
|
for key in dyn.columns.levels[0]: |
|
dyn_temp=dyn[key] |
|
if ((key=="CHART") or (key=="MEDS")): |
|
agg=dyn_temp.aggregate("mean") |
|
agg=agg.reset_index() |
|
else: |
|
agg=dyn_temp.aggregate("max") |
|
agg=agg.reset_index() |
|
|
|
if dyn_df.empty: |
|
dyn_df=agg |
|
else: |
|
dyn_df=pd.concat([dyn_df,agg],axis=0) |
|
dyn_df=dyn_df.T |
|
dyn_df.columns = dyn_df.iloc[0] |
|
dyn_df=dyn_df.iloc[1:,:] |
|
|
|
X_df=pd.concat([dyn_df,stat],axis=1) |
|
X_df=pd.concat([X_df,demo],axis=1) |
|
return X_df |
|
|
|
|
|
|
|
|
|
|
|
|
|
def task_cohort(task, mimic_path, config_path): |
|
sys.path.append('./preprocessing/day_intervals_preproc') |
|
sys.path.append('./utils') |
|
sys.path.append('./preprocessing/hosp_module_preproc') |
|
sys.path.append('./model') |
|
import day_intervals_cohort_v22 |
|
import day_intervals_cohort |
|
import feature_selection_icu |
|
import data_generation_icu_modify |
|
import data_generation_modify |
|
import feature_selection_hosp |
|
|
|
|
|
root_dir = os.path.dirname(os.path.abspath('UserInterface.ipynb')) |
|
config_path='./config/'+config_path |
|
with open(config_path) as f: |
|
config = yaml.safe_load(f) |
|
version_path = mimic_path+'/' |
|
print(version_path) |
|
version = mimic_path.split('/')[-1][0] |
|
start = time.time() |
|
|
|
label, tim, disease_label, predW = check_config(task,config_path) |
|
icu_no_icu = config['icu_no_icu'] |
|
timeW = config['timeWindow'] |
|
include=int(timeW.split()[1]) |
|
bucket = config['timebucket'] |
|
radimp = config['radimp'] |
|
|
|
diag_flag = config['diagnosis'] |
|
proc_flag= config['proc'] |
|
med_flag = config['meds'] |
|
disease_filter = config['disease_filter'] |
|
groupingDiag = config['groupingDiag'] |
|
select_diag= config['select_diag'] |
|
select_med= config['select_med'] |
|
select_proc= config['select_proc'] |
|
|
|
if icu_no_icu=='ICU': |
|
out_flag = config['output'] |
|
chart_flag = config['chart'] |
|
select_out= config['select_out'] |
|
select_chart= config['select_chart'] |
|
lab_flag = False |
|
select_lab = False |
|
else: |
|
lab_flag = config['lab'] |
|
groupingMed = config['groupingMed'] |
|
groupingProc = config['groupingProc'] |
|
select_lab= config['select_lab'] |
|
out_flag = False |
|
chart_flag = False |
|
select_out= False |
|
select_chart= False |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
data_icu=icu_no_icu=="ICU" |
|
data_mort=label=="Mortality" |
|
data_admn=label=='Readmission' |
|
data_los=label=='Length of Stay' |
|
|
|
if (disease_filter=="Heart Failure"): |
|
icd_code='I50' |
|
elif (disease_filter=="CKD"): |
|
icd_code='N18' |
|
elif (disease_filter=="COPD"): |
|
icd_code='J44' |
|
elif (disease_filter=="CAD"): |
|
icd_code='I25' |
|
else: |
|
icd_code='No Disease Filter' |
|
|
|
|
|
if version == '2': |
|
cohort_output = day_intervals_cohort_v22.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label) |
|
|
|
elif version == '1': |
|
cohort_output = day_intervals_cohort.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label) |
|
|
|
|
|
if data_icu : |
|
feature_selection_icu.feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag) |
|
else: |
|
feature_selection_hosp.feature_nonicu(cohort_output, version_path,diag_flag,lab_flag,proc_flag,med_flag) |
|
|
|
if data_icu: |
|
if diag_flag: |
|
group_diag=groupingDiag |
|
feature_selection_icu.preprocess_features_icu(cohort_output, diag_flag, group_diag,False,False,False,0,0) |
|
|
|
else: |
|
if diag_flag: |
|
group_diag=groupingDiag |
|
if med_flag: |
|
group_med=groupingMed |
|
if proc_flag: |
|
group_proc=groupingProc |
|
feature_selection_hosp.preprocess_features_hosp(cohort_output, diag_flag,proc_flag,med_flag,False,group_diag,group_med,group_proc,False,False,0,0) |
|
|
|
if data_icu: |
|
feature_selection_icu.generate_summary_icu(diag_flag,proc_flag,med_flag,out_flag,chart_flag) |
|
else: |
|
feature_selection_hosp.generate_summary_hosp(diag_flag,proc_flag,med_flag,lab_flag) |
|
|
|
|
|
if data_icu: |
|
feature_selection_icu.features_selection_icu(cohort_output, diag_flag,proc_flag,med_flag,out_flag, chart_flag,select_diag,select_med,select_proc,select_out,select_chart) |
|
else: |
|
feature_selection_hosp.features_selection_hosp(cohort_output, diag_flag,proc_flag,med_flag,lab_flag,select_diag,select_med,select_proc,select_lab) |
|
|
|
|
|
thresh=0 |
|
if data_icu: |
|
if chart_flag: |
|
outlier_removal=config['outlier_removal'] |
|
clean_chart=outlier_removal!='No outlier detection' |
|
impute_outlier_chart=outlier_removal=='Impute Outlier (default:98)' |
|
thresh=config['outlier'] |
|
left_thresh=config['left_outlier'] |
|
feature_selection_icu.preprocess_features_icu(cohort_output, False, False,chart_flag,clean_chart,impute_outlier_chart,thresh,left_thresh) |
|
else: |
|
if lab_flag: |
|
outlier_removal=config['outlier_removal'] |
|
clean_chart=outlier_removal!='No outlier detection' |
|
impute_outlier_chart=outlier_removal=='Impute Outlier (default:98)' |
|
thresh=config['outlier'] |
|
left_thresh=config['left_outlier'] |
|
feature_selection_hosp.preprocess_features_hosp(cohort_output, False,False, False,lab_flag,False,False,False,clean_chart,impute_outlier_chart,thresh,left_thresh) |
|
|
|
if radimp == 'forward fill and mean' : |
|
impute='Mean' |
|
elif radimp =='forward fill and median': |
|
impute = 'Median' |
|
else : |
|
impute = False |
|
|
|
if data_icu: |
|
gen=data_generation_icu_modify.Generator(task,cohort_output,data_mort,data_admn,data_los,diag_flag,proc_flag,out_flag,chart_flag,med_flag,impute,include,bucket,predW) |
|
else: |
|
gen=data_generation_modify.Generator(cohort_output,data_mort,data_admn,data_los,diag_flag,lab_flag,proc_flag,med_flag,impute,include,bucket,predW) |
|
|
|
end = time.time() |
|
print("Time elapsed : ", round((end - start)/60,2),"mins") |
|
print("[============TASK COHORT SUCCESSFULLY CREATED============]") |
|
|
|
|
|
|
|
|
|
|
|
class Mimic4DatasetConfig(datasets.BuilderConfig): |
|
"""BuilderConfig for Mimic4Dataset.""" |
|
|
|
def __init__( |
|
self, |
|
**kwargs, |
|
): |
|
super().__init__(**kwargs) |
|
|
|
class Mimic4Dataset(datasets.GeneratorBasedBuilder): |
|
VERSION = datasets.Version("1.0.0") |
|
|
|
def __init__(self, **kwargs): |
|
self.mimic_path = kwargs.pop("mimic_path", None) |
|
self.encoding = kwargs.pop("encoding",'raw') |
|
self.config_path = kwargs.pop("config_path",None) |
|
self.test_size = kwargs.pop("test_size",0.2) |
|
self.val_size = kwargs.pop("val_size",0.1) |
|
self.generate_cohort = kwargs.pop("generate_cohort",True) |
|
|
|
if self.encoding == 'concat': |
|
self.concat = True |
|
else: |
|
self.concat = False |
|
|
|
super().__init__(**kwargs) |
|
|
|
|
|
BUILDER_CONFIGS = [ |
|
Mimic4DatasetConfig( |
|
name="Phenotype", |
|
version=VERSION, |
|
description="Dataset for mimic4 Phenotype task" |
|
), |
|
Mimic4DatasetConfig( |
|
name="Readmission", |
|
version=VERSION, |
|
description="Dataset for mimic4 Readmission task" |
|
), |
|
Mimic4DatasetConfig( |
|
name="Length of Stay", |
|
version=VERSION, |
|
description="Dataset for mimic4 Length of Stay task" |
|
), |
|
Mimic4DatasetConfig( |
|
name="Mortality", |
|
version=VERSION, |
|
description="Dataset for mimic4 Mortality task" |
|
), |
|
] |
|
|
|
DEFAULT_CONFIG_NAME = "Mortality" |
|
|
|
def create_cohort(self): |
|
if self.config_path==None: |
|
if self.config.name == 'Phenotype' : self.config_path = _CONFIG_URLS['phenotype'] |
|
if self.config.name == 'Readmission' : self.config_path = _CONFIG_URLS['readmission'] |
|
if self.config.name == 'Length of Stay' : self.config_path = _CONFIG_URLS['los'] |
|
if self.config.name == 'Mortality' : self.config_path = _CONFIG_URLS['mortality'] |
|
|
|
version = self.mimic_path.split('/')[-1] |
|
mimic_folder= self.mimic_path.split('/')[-2] |
|
mimic_complete_path='/'+mimic_folder+'/'+version |
|
|
|
current_directory = os.getcwd() |
|
if os.path.exists(os.path.dirname(current_directory)+'/MIMIC-IV-Data-Pipeline-main'): |
|
dir =os.path.dirname(current_directory) |
|
os.chdir(dir) |
|
else: |
|
|
|
dir = self.mimic_path.replace(mimic_complete_path,'') |
|
if dir[-1]!='/': |
|
dir=dir+'/' |
|
elif dir=='': |
|
dir="./" |
|
parent_dir = os.path.dirname(self.mimic_path) |
|
os.chdir(parent_dir) |
|
|
|
|
|
repo_url='https://github.com/healthylaife/MIMIC-IV-Data-Pipeline' |
|
if os.path.exists('MIMIC-IV-Data-Pipeline-main'): |
|
path_bench = './MIMIC-IV-Data-Pipeline-main' |
|
else: |
|
path_bench ='./MIMIC-IV-Data-Pipeline-main' |
|
subprocess.run(["git", "clone", repo_url, path_bench]) |
|
os.makedirs(path_bench+'/mimic-iv') |
|
shutil.move(version,path_bench+'/mimic-iv') |
|
|
|
os.chdir(path_bench) |
|
self.mimic_path = './mimic-iv/'+version |
|
|
|
|
|
|
|
if self.config_path[0:4] == 'http': |
|
c = self.config_path.split('/')[-1] |
|
file_path, head = urlretrieve(self.config_path,c) |
|
else : |
|
file_path = self.config_path |
|
|
|
if not os.path.exists('./config'): |
|
os.makedirs('config') |
|
|
|
self.conf='./config/'+file_path.split('/')[-1] |
|
if not os.path.exists(self.conf): |
|
shutil.move(file_path,'./config') |
|
with open(self.conf) as f: |
|
config = yaml.safe_load(f) |
|
timeW = config['timeWindow'] |
|
self.timeW=int(timeW.split()[1]) |
|
self.bucket = config['timebucket'] |
|
self.data_icu = config['icu_no_icu']=='ICU' |
|
if self.data_icu: |
|
self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out, self.lab = config['diagnosis'], config['chart'], config['proc'], config['meds'], config['output'], False |
|
self.feat_lab = False |
|
else: |
|
self.feat_cond, self.feat_lab, self.feat_proc, self.feat_meds, self.feat_chart, self.out = config['diagnosis'], config['lab'], config['proc'], config['meds'], False, False |
|
self.feat_out = False |
|
self.feat_chart = False |
|
|
|
if not os.path.exists('./model/data_generation_icu_modify.py'): |
|
file_path, head = urlretrieve(_DATA_GEN, "data_generation_icu_modify.py") |
|
shutil.move(file_path, './model') |
|
|
|
if not os.path.exists('./model/data_generation_modify.py'): |
|
file_path, head = urlretrieve(_DATA_GEN_HOSP, "data_generation_modify.py") |
|
shutil.move(file_path, './model') |
|
|
|
if not os.path.exists('./preprocessing/day_intervals_preproc/day_intervals_cohort_v22.py'): |
|
file_path, head = urlretrieve(_DAY_INT, "day_intervals_cohort_v22.py") |
|
shutil.move(file_path, './preprocessing/day_intervals_preproc') |
|
|
|
data_dir = "./data/dict/"+self.config.name.replace(" ","_")+"/dataDic" |
|
sys.path.append(path_bench) |
|
config = self.config_path.split('/')[-1] |
|
|
|
|
|
if self.generate_cohort: |
|
task_cohort(self.config.name.replace(" ","_"),self.mimic_path,config) |
|
|
|
|
|
with open(data_dir, 'rb') as fp: |
|
dataDic = pickle.load(fp) |
|
data = pd.DataFrame.from_dict(dataDic) |
|
|
|
dict_dir = "./data/dict/"+self.config.name.replace(" ","_") |
|
|
|
data=data.T |
|
train_data, test_data = train_test_split(data, test_size=self.test_size, random_state=42) |
|
if self.val_size > 0 : |
|
train_data, val_data = train_test_split(train_data, test_size=self.val_size, random_state=42) |
|
val_dic = val_data.to_dict('index') |
|
val_path = dict_dir+'/val_data.pkl' |
|
with open(val_path, 'wb') as f: |
|
pickle.dump(val_dic, f) |
|
|
|
train_dic = train_data.to_dict('index') |
|
test_dic = test_data.to_dict('index') |
|
|
|
train_path = dict_dir+'/train_data.pkl' |
|
test_path = dict_dir+'/test_data.pkl' |
|
|
|
|
|
with open(train_path, 'wb') as f: |
|
pickle.dump(train_dic, f) |
|
|
|
with open(test_path, 'wb') as f: |
|
pickle.dump(test_dic, f) |
|
|
|
return dict_dir |
|
|
|
|
|
|
|
def _info_raw(self): |
|
features = datasets.Features( |
|
{ |
|
"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]), |
|
"gender": datasets.Value("string"), |
|
"ethnicity": datasets.Value("string"), |
|
"insurance": datasets.Value("string"), |
|
"age": datasets.Value("int32"), |
|
"COND": datasets.Sequence(datasets.Value("string")), |
|
"MEDS": { |
|
"signal": |
|
{ |
|
"id": datasets.Sequence(datasets.Value("int32")), |
|
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
|
} |
|
, |
|
"rate": |
|
{ |
|
"id": datasets.Sequence(datasets.Value("int32")), |
|
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
|
} |
|
, |
|
"amount": |
|
{ |
|
"id": datasets.Sequence(datasets.Value("int32")), |
|
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
|
} |
|
|
|
}, |
|
"PROC": { |
|
"id": datasets.Sequence(datasets.Value("int32")), |
|
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
|
}, |
|
"CHART/LAB": |
|
{ |
|
"signal" : { |
|
"id": datasets.Sequence(datasets.Value("int32")), |
|
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
|
}, |
|
"val" : { |
|
"id": datasets.Sequence(datasets.Value("int32")), |
|
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
|
}, |
|
}, |
|
"OUT": { |
|
"id": datasets.Sequence(datasets.Value("int32")), |
|
"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
|
}, |
|
|
|
} |
|
) |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
homepage=_HOMEPAGE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _generate_examples_raw(self, filepath): |
|
with open(filepath, 'rb') as fp: |
|
dataDic = pickle.load(fp) |
|
for hid, data in dataDic.items(): |
|
proc_features = data['Proc'] |
|
meds_features = data['Med'] |
|
out_features = data['Out'] |
|
cond_features = data['Cond']['fids'] |
|
eth= data['ethnicity'] |
|
age = data['age'] |
|
gender = data['gender'] |
|
label = data['label'] |
|
insurance=data['insurance'] |
|
|
|
items = list(proc_features.keys()) |
|
values =[proc_features[i] for i in items ] |
|
procs = {"id" : items, |
|
"value": values} |
|
|
|
items_outs = list(out_features.keys()) |
|
values_outs =[out_features[i] for i in items_outs ] |
|
outs = {"id" : items_outs, |
|
"value": values_outs} |
|
|
|
if self.data_icu: |
|
chart_features = data['Chart'] |
|
else: |
|
chart_features = data['Lab'] |
|
|
|
|
|
if ('signal' in chart_features): |
|
items_chart_sig = list(chart_features['signal'].keys()) |
|
values_chart_sig =[chart_features['signal'][i] for i in items_chart_sig ] |
|
chart_sig = {"id" : items_chart_sig, |
|
"value": values_chart_sig} |
|
else: |
|
chart_sig = {"id" : [], |
|
"value": []} |
|
|
|
if ('val' in chart_features): |
|
items_chart_val = list(chart_features['val'].keys()) |
|
values_chart_val =[chart_features['val'][i] for i in items_chart_val ] |
|
chart_val = {"id" : items_chart_val, |
|
"value": values_chart_val} |
|
else: |
|
chart_val = {"id" : [], |
|
"value": []} |
|
|
|
charts = {"signal" : chart_sig, |
|
"val" : chart_val} |
|
|
|
|
|
if ('signal' in meds_features): |
|
items_meds_sig = list(meds_features['signal'].keys()) |
|
values_meds_sig =[meds_features['signal'][i] for i in items_meds_sig ] |
|
meds_sig = {"id" : items_meds_sig, |
|
"value": values_meds_sig} |
|
else: |
|
meds_sig = {"id" : [], |
|
"value": []} |
|
|
|
if ('rate' in meds_features): |
|
items_meds_rate = list(meds_features['rate'].keys()) |
|
values_meds_rate =[meds_features['rate'][i] for i in items_meds_rate ] |
|
meds_rate = {"id" : items_meds_rate, |
|
"value": values_meds_rate} |
|
else: |
|
meds_rate = {"id" : [], |
|
"value": []} |
|
|
|
if ('amount' in meds_features): |
|
items_meds_amount = list(meds_features['amount'].keys()) |
|
values_meds_amount =[meds_features['amount'][i] for i in items_meds_amount ] |
|
meds_amount = {"id" : items_meds_amount, |
|
"value": values_meds_amount} |
|
else: |
|
meds_amount = {"id" : [], |
|
"value": []} |
|
|
|
meds = {"signal" : meds_sig, |
|
"rate" : meds_rate, |
|
"amount" : meds_amount} |
|
|
|
|
|
yield int(hid), { |
|
"label" : label, |
|
"gender" : gender, |
|
"ethnicity" : eth, |
|
"insurance" : insurance, |
|
"age" : age, |
|
"COND" : cond_features, |
|
"PROC" : procs, |
|
"CHART/LAB" : charts, |
|
"OUT" : outs, |
|
"MEDS" : meds |
|
} |
|
|
|
|
|
|
|
|
|
|
|
def _info_encoded(self): |
|
features = datasets.Features( |
|
{ |
|
"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]), |
|
"features" : datasets.Sequence(datasets.Value("float32")), |
|
} |
|
) |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
homepage=_HOMEPAGE, |
|
citation=_CITATION, |
|
) |
|
|
|
def _generate_examples_encoded(self, filepath): |
|
path= './data/dict/'+self.config.name.replace(" ","_")+'/ethVocab' |
|
with open(path, 'rb') as fp: |
|
ethVocab = pickle.load(fp) |
|
|
|
path= './data/dict/'+self.config.name.replace(" ","_")+'/insVocab' |
|
with open(path, 'rb') as fp: |
|
insVocab = pickle.load(fp) |
|
|
|
genVocab = ['<PAD>', 'M', 'F'] |
|
gen_encoder = LabelEncoder() |
|
eth_encoder = LabelEncoder() |
|
ins_encoder = LabelEncoder() |
|
gen_encoder.fit(genVocab) |
|
eth_encoder.fit(ethVocab) |
|
ins_encoder.fit(insVocab) |
|
with open(filepath, 'rb') as fp: |
|
dico = pickle.load(fp) |
|
|
|
df = pd.DataFrame.from_dict(dico, orient='index') |
|
task=self.config.name.replace(" ","_") |
|
|
|
for i, data in df.iterrows(): |
|
concat_cols=[] |
|
dyn_df,cond_df,demo=concat_data(data,task,self.feat_cond,self.feat_proc,self.feat_out, self.feat_chart, self.feat_meds,self.feat_lab) |
|
dyn=dyn_df.copy() |
|
dyn.columns=dyn.columns.droplevel(0) |
|
cols=dyn.columns |
|
time=dyn.shape[0] |
|
for t in range(time): |
|
cols_t = [str(x) + "_"+str(t) for x in cols] |
|
concat_cols.extend(cols_t) |
|
demo['gender']=gen_encoder.transform(demo['gender']) |
|
demo['ethnicity']=eth_encoder.transform(demo['ethnicity']) |
|
demo['insurance']=ins_encoder.transform(demo['insurance']) |
|
label = data['label'] |
|
demo=demo.drop(['label'],axis=1) |
|
X= getXY(dyn_df,cond_df,demo,concat_cols,self.concat) |
|
X=X.values.tolist()[0] |
|
yield int(i), { |
|
"label": label, |
|
"features": X, |
|
} |
|
|
|
def _info_deep(self): |
|
features = datasets.Features( |
|
{ |
|
"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]), |
|
|
|
"DEMO": datasets.Sequence(datasets.Value("int64")), |
|
"COND" : datasets.Sequence(datasets.Value("int64")), |
|
|
|
"MEDS" : datasets.Array2D(shape=(None, self.size_meds), dtype='int64') , |
|
"PROC" : datasets.Array2D(shape=(None, self.size_proc), dtype='int64') , |
|
"CHART/LAB" : datasets.Array2D(shape=(None, self.size_chart), dtype='int64') , |
|
|
|
"OUT" : datasets.Array2D(shape=(None, self.size_out), dtype='int64') , |
|
|
|
} |
|
) |
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
homepage=_HOMEPAGE, |
|
citation=_CITATION, |
|
) |
|
|
|
|
|
def _generate_examples_deep(self, filepath): |
|
with open(filepath, 'rb') as fp: |
|
dico = pickle.load(fp) |
|
task=self.config.name.replace(" ","_") |
|
for key, data in dico.items(): |
|
stat, demo, meds, chart, out, proc, lab, y = getXY_deep(data, task, self.feat_cond, self.feat_proc, self.feat_out, self.feat_chart, self.feat_meds,self.feat_lab) |
|
|
|
verri=True |
|
if self.feat_proc: |
|
if (len(proc)<(self.timeW//self.bucket)): |
|
verri=False |
|
if self.feat_out: |
|
if (len(out)<(self.timeW//self.bucket)): |
|
verri=False |
|
if self.feat_chart: |
|
if (len(chart)<(self.timeW//self.bucket)): |
|
verri=False |
|
if self.feat_meds: |
|
if (len(meds)<(self.timeW//self.bucket)): |
|
verri=False |
|
if self.feat_lab: |
|
if (len(lab)<(self.timeW//self.bucket)): |
|
verri=False |
|
if verri: |
|
if self.data_icu: |
|
yield int(key), { |
|
'label': y, |
|
'DEMO': demo, |
|
'COND': stat, |
|
'MEDS': meds, |
|
'PROC': proc, |
|
'CHART/LAB': chart, |
|
'OUT': out, |
|
} |
|
else: |
|
yield int(key), { |
|
'label': y, |
|
'DEMO': demo, |
|
'COND': stat, |
|
'MEDS': meds, |
|
'PROC': proc, |
|
'CHART/LAB': lab, |
|
'OUT': out, |
|
} |
|
else: |
|
continue |
|
|
|
|
|
|
|
def _info(self): |
|
self.path = self.create_cohort() |
|
self.size_cond, self.size_proc, self.size_meds, self.size_out, self.size_chart, self.size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(self.config.name.replace(" ","_"),self.feat_cond,self.feat_proc,self.feat_out,self.feat_chart,self.feat_meds,self.feat_lab) |
|
|
|
if self.encoding == 'concat' : |
|
return self._info_encoded() |
|
|
|
elif self.encoding == 'aggreg' : |
|
return self._info_encoded() |
|
|
|
elif self.encoding == 'tensor' : |
|
return self._info_deep() |
|
|
|
else: |
|
return self._info_raw() |
|
|
|
|
|
def _split_generators(self, dl_manager): |
|
csv_dir = "./data/dict/"+self.config.name.replace(" ","_") |
|
if self.val_size > 0 : |
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": csv_dir+'/train_data.pkl'}), |
|
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": csv_dir+'/val_data.pkl'}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": csv_dir+'/test_data.pkl'}), |
|
] |
|
else : |
|
return [ |
|
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": csv_dir+'/train_data.pkl'}), |
|
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": csv_dir+'/test_data.pkl'}), |
|
] |
|
def _generate_examples(self, filepath): |
|
|
|
if self.encoding == 'concat' : |
|
yield from self._generate_examples_encoded(filepath) |
|
|
|
elif self.encoding == 'aggreg' : |
|
yield from self._generate_examples_encoded(filepath) |
|
|
|
elif self.encoding == 'tensor' : |
|
yield from self._generate_examples_deep(filepath) |
|
else : |
|
yield from self._generate_examples_raw(filepath) |
