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MIMIC_IV_HAIM_API.py
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MIMIC_IV_HAIM_API.py
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# %%
###########################################################################################################
# __ __ ___ __ .___ ___.
# | | | | / \ | | | \/ |
# | |__| | / ^ \ | | | \ / |
# | __ | / /_\ \ | | | |\/| |
# | | | | / _____ \ | | | | | |
# |__| |__| /__/ \__\ |__| |__| |__|
#
# HOLISTIC ARTIFICIAL INTELLIGENCE IN MEDICINE
#
###########################################################################################################
#
# Licensed under the Apache License, Version 2.0**
# You may not use this file except in compliance with the License. You may obtain a copy of the License at
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed under the License is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied. See the License for the specific language governing permissions and limitations under the License.
#-> Authors:
# Luis R Soenksen (<[email protected]>),
# Yu Ma (<[email protected]>),
# Cynthia Zeng (<[email protected]>),
# Leonard David Jean Boussioux (<[email protected]>),
# Kimberly M Villalobos Carballo (<[email protected]>),
# Liangyuan Na (<[email protected]>),
# Holly Mika Wiberg (<[email protected]>),
# Michael Lingzhi Li (<[email protected]>),
# Ignacio Fuentes (<[email protected]>),
# Dimitris J Bertsimas (<[email protected]>),
# -> Last Update: Dec 30th, 2021
# -> Changes:
# * Added embeddings extraction wrappers
# * Added Code for Patient parsing towards Multi-Input AI/ML to predict value of Next Lab/X-Ray with MIMIC-IV
# * Add Model helper functions
#-------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------
# PREREQUISITS |
# |
## -> To run this code, we first need to install serveral required packages
#!pip install tensorflow
#!pip install tqdm==4.19.9
#!pip install dask
#!pip install sklearn
#!pip install tsfresh
#!pip install missingno
#!pip install transformers
#!pip install torch==1.0.1
#!pip install torchvision==0.2.2
#!pip install torchxrayvision
#-------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------
# IMPORTS |
# System
import os
import sys
# Base
import cv2
import math
import copy
import pickle
import numpy as np
import pandas as pd
import pandas.io.sql as psql
import datetime as dt
import plotly.express as px
import matplotlib.pyplot as plt
import missingno as msno
from tqdm import tqdm
from glob import glob
from shutil import copyfile
from dask import dataframe as dd
from dask.diagnostics import ProgressBar
ProgressBar().register()
# Core AI/ML
import tensorflow as tf
import torch
import torch.nn.functional as F
import torchvision, torchvision.transforms
from torch.utils.data import Dataset, DataLoader
# Scipy
from scipy.stats import ks_2samp
from scipy.signal import find_peaks
# Scikit-learn
from sklearn.preprocessing import scale
from sklearn.preprocessing import MinMaxScaler, QuantileTransformer
# TSFresh
# from tsfresh import extract_features, select_features
# from tsfresh.utilities.dataframe_functions import impute
#TS (Manual) module
# from ts_embeddings import *
# # NLP
# from transformers import AutoTokenizer, AutoModel, logging
# logging.set_verbosity_error()
# # biobert_path = '../pretrained_models/bio_clinical_bert/biobert_pretrain_output_all_notes_150000/'
# biobert_path = 'pretrained_bert_tf/biobert_pretrain_output_all_notes_150000/'
# biobert_tokenizer = AutoTokenizer.from_pretrained(biobert_path)
# biobert_model = AutoModel.from_pretrained(biobert_path)
# # biobert_tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
# # biobert_model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
# # os.environ["TOKENIZERS_PARALLELISM"] = "false"
# Computer Vision
import cv2
# import skimage, skimage.io
# import torchxrayvision as xrv
# Deep Fusion for MIMIC-IV
# Warning handling
import warnings
warnings.filterwarnings("ignore")
#-------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------
# Initializations & Data Loading |
# |
"""
Resources to identify tables and variables of interest can be found in the MIMIC-IV official API (https://mimic-iv.mit.edu/docs/)
"""
# Define MIMIC IV Data Location
core_mimiciv_path = '../data/HAIM/physionet/files/mimiciv/1.0/'
# Define MIMIC IV Image Data Location (usually external drive)
core_mimiciv_imgcxr_path = '../data/HAIM/physionet/files/mimiciv/1.0/mimic-cxr-jpg/2.0.0.'
#-------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------
# Helper functions |
#
#-------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------
# HAIM-MIMICIV specific Patient Representation Function |
# |
# MIMICIV PATIENT CLASS STRUCTURE
class Patient_ICU(object):
def __init__(self, admissions, demographics, transfers, core,\
diagnoses_icd, drgcodes, emar, emar_detail, hcpcsevents,\
labevents, microbiologyevents, poe, poe_detail,\
prescriptions, procedures_icd, services, procedureevents,\
outputevents, inputevents, icustays, datetimeevents,\
chartevents, cxr, imcxr, noteevents, dsnotes, ecgnotes, \
echonotes, radnotes):
## CORE
self.admissions = admissions
self.demographics = demographics
self.transfers = transfers
self.core = core
## HOSP
self.diagnoses_icd = diagnoses_icd
self.drgcodes = drgcodes
self.emar = emar
self.emar_detail = emar_detail
self.hcpcsevents = hcpcsevents
self.labevents = labevents
self.microbiologyevents = microbiologyevents
self.poe = poe
self.poe_detail = poe_detail
self.prescriptions = prescriptions
self.procedures_icd = procedures_icd
self.services = services
## ICU
self.procedureevents = procedureevents
self.outputevents = outputevents
self.inputevents = inputevents
self.icustays = icustays
self.datetimeevents = datetimeevents
self.chartevents = chartevents
## CXR
self.cxr = cxr
self.imcxr = imcxr
## NOTES
self.noteevents = noteevents
self.dsnotes = dsnotes
self.ecgnotes = ecgnotes
self.echonotes = echonotes
self.radnotes = radnotes
# GET FULL MIMIC IV PATIENT RECORD USING DATABASE KEYS
def get_patient_icustay(key_subject_id, key_hadm_id, key_stay_id):
# Inputs:
# key_subject_id -> subject_id is unique to a patient
# key_hadm_id -> hadm_id is unique to a patient hospital stay
# key_stay_id -> stay_id is unique to a patient ward stay
#
# NOTES: Identifiers which specify the patient. More information about
# these identifiers is available at https://mimic-iv.mit.edu/basics/identifiers
# Outputs:
# Patient_ICUstay -> ICU patient stay structure
#-> FILTER data
##-> CORE
f_df_base_core = df_base_core[(df_base_core.subject_id == key_subject_id) & (df_base_core.hadm_id == key_hadm_id)]
f_df_admissions = df_admissions[(df_admissions.subject_id == key_subject_id) & (df_admissions.hadm_id == key_hadm_id)]
f_df_patients = df_patients[(df_patients.subject_id == key_subject_id)]
f_df_transfers = df_transfers[(df_transfers.subject_id == key_subject_id) & (df_transfers.hadm_id == key_hadm_id)]
###-> Merge data into single patient structure
f_df_core = f_df_base_core
f_df_core = f_df_core.merge(f_df_admissions, how='left')
f_df_core = f_df_core.merge(f_df_patients, how='left')
f_df_core = f_df_core.merge(f_df_transfers, how='left')
##-> HOSP
f_df_diagnoses_icd = df_diagnoses_icd[(df_diagnoses_icd.subject_id == key_subject_id)]
f_df_drgcodes = df_drgcodes[(df_drgcodes.subject_id == key_subject_id) & (df_drgcodes.hadm_id == key_hadm_id)]
f_df_emar = df_emar[(df_emar.subject_id == key_subject_id) & (df_emar.hadm_id == key_hadm_id)]
f_df_emar_detail = df_emar_detail[(df_emar_detail.subject_id == key_subject_id)]
f_df_hcpcsevents = df_hcpcsevents[(df_hcpcsevents.subject_id == key_subject_id) & (df_hcpcsevents.hadm_id == key_hadm_id)]
f_df_labevents = df_labevents[(df_labevents.subject_id == key_subject_id) & (df_labevents.hadm_id == key_hadm_id)]
f_df_microbiologyevents = df_microbiologyevents[(df_microbiologyevents.subject_id == key_subject_id) & (df_microbiologyevents.hadm_id == key_hadm_id)]
f_df_poe = df_poe[(df_poe.subject_id == key_subject_id) & (df_poe.hadm_id == key_hadm_id)]
f_df_poe_detail = df_poe_detail[(df_poe_detail.subject_id == key_subject_id)]
f_df_prescriptions = df_prescriptions[(df_prescriptions.subject_id == key_subject_id) & (df_prescriptions.hadm_id == key_hadm_id)]
f_df_procedures_icd = df_procedures_icd[(df_procedures_icd.subject_id == key_subject_id) & (df_procedures_icd.hadm_id == key_hadm_id)]
f_df_services = df_services[(df_services.subject_id == key_subject_id) & (df_services.hadm_id == key_hadm_id)]
###-> Merge content from dictionaries
f_df_diagnoses_icd = f_df_diagnoses_icd.merge(df_d_icd_diagnoses, how='left')
f_df_procedures_icd = f_df_procedures_icd.merge(df_d_icd_procedures, how='left')
f_df_hcpcsevents = f_df_hcpcsevents.merge(df_d_hcpcs, how='left')
f_df_labevents = f_df_labevents.merge(df_d_labitems, how='left')
##-> ICU
f_df_procedureevents = df_procedureevents[(df_procedureevents.subject_id == key_subject_id) & (df_procedureevents.hadm_id == key_hadm_id) & (df_procedureevents.stay_id == key_stay_id)]
f_df_outputevents = df_outputevents[(df_outputevents.subject_id == key_subject_id) & (df_outputevents.hadm_id == key_hadm_id) & (df_outputevents.stay_id == key_stay_id)]
f_df_inputevents = df_inputevents[(df_inputevents.subject_id == key_subject_id) & (df_inputevents.hadm_id == key_hadm_id) & (df_inputevents.stay_id == key_stay_id)]
f_df_icustays = df_icustays[(df_icustays.subject_id == key_subject_id) & (df_icustays.hadm_id == key_hadm_id) & (df_icustays.stay_id == key_stay_id)]
f_df_datetimeevents = df_datetimeevents[(df_datetimeevents.subject_id == key_subject_id) & (df_datetimeevents.hadm_id == key_hadm_id) & (df_datetimeevents.stay_id == key_stay_id)]
f_df_chartevents = df_chartevents[(df_chartevents.subject_id == key_subject_id) & (df_chartevents.hadm_id == key_hadm_id) & (df_chartevents.stay_id == key_stay_id)]
###-> Merge content from dictionaries
f_df_procedureevents = f_df_procedureevents.merge(df_d_items, how='left')
f_df_outputevents = f_df_outputevents.merge(df_d_items, how='left')
f_df_inputevents = f_df_inputevents.merge(df_d_items, how='left')
f_df_datetimeevents = f_df_datetimeevents.merge(df_d_items, how='left')
f_df_chartevents = f_df_chartevents.merge(df_d_items, how='left')
##-> CXR
f_df_mimic_cxr_split = df_mimic_cxr_split[(df_mimic_cxr_split.subject_id == key_subject_id)]
f_df_mimic_cxr_chexpert = df_mimic_cxr_chexpert[(df_mimic_cxr_chexpert.subject_id == key_subject_id)]
f_df_mimic_cxr_metadata = df_mimic_cxr_metadata[(df_mimic_cxr_metadata.subject_id == key_subject_id)]
f_df_mimic_cxr_negbio = df_mimic_cxr_negbio[(df_mimic_cxr_negbio.subject_id == key_subject_id)]
###-> Merge data into single patient structure
f_df_cxr = f_df_mimic_cxr_split
f_df_cxr = f_df_cxr.merge(f_df_mimic_cxr_chexpert, how='left')
f_df_cxr = f_df_cxr.merge(f_df_mimic_cxr_metadata, how='left')
f_df_cxr = f_df_cxr.merge(f_df_mimic_cxr_negbio, how='left')
###-> Get images of that timebound patient
f_df_imcxr = []
for img_idx, img_row in f_df_cxr.iterrows():
img_path = core_mimiciv_imgcxr_path + str(img_row['Img_Folder']) + '/' + str(img_row['Img_Filename'])
img_cxr_shape = [224, 224]
img_cxr = cv2.resize(cv2.imread(img_path, cv2.IMREAD_GRAYSCALE), (img_cxr_shape[0], img_cxr_shape[1]))
f_df_imcxr.append(np.array(img_cxr))
##-> NOTES
f_df_noteevents = df_noteevents[(df_noteevents.subject_id == key_subject_id) & (df_noteevents.hadm_id == key_hadm_id)]
f_df_dsnotes = df_dsnotes[(df_dsnotes.subject_id == key_subject_id) & (df_dsnotes.hadm_id == key_hadm_id) & (df_dsnotes.stay_id == key_stay_id)]
f_df_ecgnotes = df_ecgnotes[(df_ecgnotes.subject_id == key_subject_id) & (df_ecgnotes.hadm_id == key_hadm_id) & (df_ecgnotes.stay_id == key_stay_id)]
f_df_echonotes = df_echonotes[(df_echonotes.subject_id == key_subject_id) & (df_echonotes.hadm_id == key_hadm_id) & (df_echonotes.stay_id == key_stay_id)]
f_df_radnotes = df_radnotes[(df_radnotes.subject_id == key_subject_id) & (df_radnotes.hadm_id == key_hadm_id) & (df_radnotes.stay_id == key_stay_id)]
###-> Merge data into single patient structure
#--None
# -> Create & Populate patient structure
## CORE
admissions = f_df_admissions
demographics = f_df_patients
transfers = f_df_transfers
core = f_df_core
## HOSP
diagnoses_icd = f_df_diagnoses_icd
drgcodes = f_df_diagnoses_icd
emar = f_df_emar
emar_detail = f_df_emar_detail
hcpcsevents = f_df_hcpcsevents
labevents = f_df_labevents
microbiologyevents = f_df_microbiologyevents
poe = f_df_poe
poe_detail = f_df_poe_detail
prescriptions = f_df_prescriptions
procedures_icd = f_df_procedures_icd
services = f_df_services
## ICU
procedureevents = f_df_procedureevents
outputevents = f_df_outputevents
inputevents = f_df_inputevents
icustays = f_df_icustays
datetimeevents = f_df_datetimeevents
chartevents = f_df_chartevents
## CXR
cxr = f_df_cxr
imcxr = f_df_imcxr
## NOTES
noteevents = f_df_noteevents
dsnotes = f_df_dsnotes
ecgnotes = f_df_ecgnotes
echonotes = f_df_echonotes
radnotes = f_df_radnotes
# Create patient object and return
Patient_ICUstay = Patient_ICU(admissions, demographics, transfers, core, \
diagnoses_icd, drgcodes, emar, emar_detail, hcpcsevents, \
labevents, microbiologyevents, poe, poe_detail, \
prescriptions, procedures_icd, services, procedureevents, \
outputevents, inputevents, icustays, datetimeevents, \
chartevents, cxr, imcxr, noteevents, dsnotes, ecgnotes, \
echonotes, radnotes)
return Patient_ICUstay
# DELTA TIME CALCULATOR FROM TWO TIMESTAMPS
def date_diff_hrs(t1, t0):
# Inputs:
# t1 -> Final timestamp in a patient hospital stay
# t0 -> Initial timestamp in a patient hospital stay
# Outputs:
# delta_t -> Patient stay structure bounded by allowed timestamps
try:
delta_t = (t1-t0).total_seconds()/3600 # Result in hrs
except:
delta_t = math.nan
return delta_t
# GET TIMEBOUND MIMIC-IV PATIENT RECORD BY DATABASE KEYS AND TIMESTAMPS
def get_timebound_patient_icustay(Patient_ICUstay, start_hr = None, end_hr = None):
# Inputs:
# Patient_ICUstay -> Patient ICU stay structure
# start_hr -> start_hr indicates the first valid time (in hours) from the admition time "admittime" for all retreived features, input "None" to avoid time bounding
# end_hr -> end_hr indicates the last valid time (in hours) from the admition time "admittime" for all retreived features, input "None" to avoid time bounding
#
# NOTES: Identifiers which specify the patient. More information about
# these identifiers is available at https://mimic-iv.mit.edu/basics/identifiers
# Outputs:
# Patient_ICUstay -> Timebound ICU patient stay structure filtered by max_time_stamp or min_time_stamp if any
# %% EXAMPLE OF USE
## Let's select a single patient
'''
key_subject_id = 10000032
key_hadm_id = 29079034
key_stay_id = 39553978
start_hr = 0
end_hr = 24
patient = get_patient_icustay(key_subject_id, key_hadm_id, key_stay_id)
dt_patient = get_timebound_patient_icustay(patient, start_hr , end_hr)
'''
# Create a deep copy so that it is not the same object
# Patient_ICUstay = copy.deepcopy(Patient_ICUstay)
## --> Process Event Structure Calculations
admittime = Patient_ICUstay.core['admittime'].values[0]
dischtime = Patient_ICUstay.core['dischtime'].values[0]
Patient_ICUstay.emar['deltacharttime'] = Patient_ICUstay.emar.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
Patient_ICUstay.labevents['deltacharttime'] = Patient_ICUstay.labevents.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
Patient_ICUstay.microbiologyevents['deltacharttime'] = Patient_ICUstay.microbiologyevents.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
Patient_ICUstay.outputevents['deltacharttime'] = Patient_ICUstay.outputevents.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
Patient_ICUstay.datetimeevents['deltacharttime'] = Patient_ICUstay.datetimeevents.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
Patient_ICUstay.chartevents['deltacharttime'] = Patient_ICUstay.chartevents.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
# Patient_ICUstay.noteevents['deltacharttime'] = Patient_ICUstay.noteevents.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
# Patient_ICUstay.dsnotes['deltacharttime'] = Patient_ICUstay.dsnotes.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
# Patient_ICUstay.ecgnotes['deltacharttime'] = Patient_ICUstay.ecgnotes.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
# Patient_ICUstay.echonotes['deltacharttime'] = Patient_ICUstay.echonotes.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
# Patient_ICUstay.radnotes['deltacharttime'] = Patient_ICUstay.radnotes.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
# Re-calculate times of CXR database
Patient_ICUstay.cxr['StudyDateForm'] = pd.to_datetime(Patient_ICUstay.cxr['StudyDate'], format='%Y%m%d')
Patient_ICUstay.cxr['StudyTimeForm'] = Patient_ICUstay.cxr.apply(lambda x : '%#010.3f' % x['StudyTime'] ,1)
Patient_ICUstay.cxr['StudyTimeForm'] = pd.to_datetime(Patient_ICUstay.cxr['StudyTimeForm'], format='%H%M%S.%f').dt.time
Patient_ICUstay.cxr['charttime'] = Patient_ICUstay.cxr.apply(lambda r : dt.datetime.combine(r['StudyDateForm'],r['StudyTimeForm']),1)
Patient_ICUstay.cxr['charttime'] = Patient_ICUstay.cxr['charttime'].dt.floor('Min')
Patient_ICUstay.cxr['deltacharttime'] = Patient_ICUstay.cxr.apply(lambda x: date_diff_hrs(x['charttime'],admittime) if not x.empty else None, axis=1)
## --> Filter by allowable time stamps
if not (start_hr == None):
Patient_ICUstay.emar = Patient_ICUstay.emar[(Patient_ICUstay.emar.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.emar.deltacharttime)]
Patient_ICUstay.labevents = Patient_ICUstay.labevents[(Patient_ICUstay.labevents.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.labevents.deltacharttime)]
Patient_ICUstay.microbiologyevents = Patient_ICUstay.microbiologyevents[(Patient_ICUstay.microbiologyevents.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.microbiologyevents.deltacharttime)]
Patient_ICUstay.outputevents = Patient_ICUstay.outputevents[(Patient_ICUstay.outputevents.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.outputevents.deltacharttime)]
Patient_ICUstay.datetimeevents = Patient_ICUstay.datetimeevents[(Patient_ICUstay.datetimeevents.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.datetimeevents.deltacharttime)]
Patient_ICUstay.chartevents = Patient_ICUstay.chartevents[(Patient_ICUstay.chartevents.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.chartevents.deltacharttime)]
Patient_ICUstay.cxr = Patient_ICUstay.cxr[(Patient_ICUstay.cxr.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.cxr.deltacharttime)]
Patient_ICUstay.imcxr = [Patient_ICUstay.imcxr[i] for i, x in enumerate((Patient_ICUstay.cxr.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.cxr.deltacharttime)) if x]
#Notes
# Patient_ICUstay.noteevents = Patient_ICUstay.noteevents[(Patient_ICUstay.noteevents.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.noteevents.deltacharttime)]
# Patient_ICUstay.dsnotes = Patient_ICUstay.dsnotes[(Patient_ICUstay.dsnotes.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.dsnotes.deltacharttime)]
# Patient_ICUstay.ecgnotes = Patient_ICUstay.ecgnotes[(Patient_ICUstay.ecgnotes.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.ecgnotes.deltacharttime)]
# Patient_ICUstay.echonotes = Patient_ICUstay.echonotes[(Patient_ICUstay.echonotes.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.echonotes.deltacharttime)]
# Patient_ICUstay.radnotes = Patient_ICUstay.radnotes[(Patient_ICUstay.radnotes.deltacharttime >= start_hr) | pd.isnull(Patient_ICUstay.radnotes.deltacharttime)]
if not (end_hr == None):
Patient_ICUstay.emar = Patient_ICUstay.emar[(Patient_ICUstay.emar.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.emar.deltacharttime)]
Patient_ICUstay.labevents = Patient_ICUstay.labevents[(Patient_ICUstay.labevents.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.labevents.deltacharttime)]
Patient_ICUstay.microbiologyevents = Patient_ICUstay.microbiologyevents[(Patient_ICUstay.microbiologyevents.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.microbiologyevents.deltacharttime)]
Patient_ICUstay.outputevents = Patient_ICUstay.outputevents[(Patient_ICUstay.outputevents.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.outputevents.deltacharttime)]
Patient_ICUstay.datetimeevents = Patient_ICUstay.datetimeevents[(Patient_ICUstay.datetimeevents.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.datetimeevents.deltacharttime)]
Patient_ICUstay.chartevents = Patient_ICUstay.chartevents[(Patient_ICUstay.chartevents.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.chartevents.deltacharttime)]
Patient_ICUstay.cxr = Patient_ICUstay.cxr[(Patient_ICUstay.cxr.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.cxr.deltacharttime)]
Patient_ICUstay.imcxr = [Patient_ICUstay.imcxr[i] for i, x in enumerate((Patient_ICUstay.cxr.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.cxr.deltacharttime)) if x]
#Notes
# Patient_ICUstay.noteevents = Patient_ICUstay.noteevents[(Patient_ICUstay.noteevents.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.noteevents.deltacharttime)]
# Patient_ICUstay.dsnotes = Patient_ICUstay.dsnotes[(Patient_ICUstay.dsnotes.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.dsnotes.deltacharttime)]
# Patient_ICUstay.ecgnotes = Patient_ICUstay.ecgnotes[(Patient_ICUstay.ecgnotes.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.ecgnotes.deltacharttime)]
# Patient_ICUstay.echonotes = Patient_ICUstay.echonotes[(Patient_ICUstay.echonotes.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.echonotes.deltacharttime)]
# Patient_ICUstay.radnotes = Patient_ICUstay.radnotes[(Patient_ICUstay.radnotes.deltacharttime <= end_hr) | pd.isnull(Patient_ICUstay.radnotes.deltacharttime)]
# Filter CXR to match allowable patient stay
Patient_ICUstay.cxr = Patient_ICUstay.cxr[(Patient_ICUstay.cxr.charttime <= dischtime)]
return Patient_ICUstay
# LOAD MASTER DICTIONARY OF MIMIC IV EVENTS
def load_haim_event_dictionaries(core_mimiciv_path):
# Inputs:
# df_d_items -> MIMIC chartevent items dictionary
# df_d_labitems -> MIMIC labevent items dictionary
# df_d_hcpcs -> MIMIC hcpcs items dictionary
#
# Outputs:
# df_patientevents_categorylabels_dict -> Dictionary with all possible event types
# Generate dictionary for chartevents, labevents and HCPCS
df_patientevents_categorylabels_dict = pd.DataFrame(columns = ['eventtype', 'category', 'label'])
# Load dictionaries
df_d_items = pd.read_csv(core_mimiciv_path + 'icu/d_items.csv')
df_d_labitems = pd.read_csv(core_mimiciv_path + 'hosp/d_labitems.csv')
df_d_hcpcs = pd.read_csv(core_mimiciv_path + 'hosp/d_hcpcs.csv')
# Get Chartevent items with labels & category
df = df_d_items
for category_idx, category in enumerate(sorted((df.category.astype(str).unique()))):
#print(category)
category_list = df[df['category']==category]
for item_idx, item in enumerate(sorted(category_list.label.astype(str).unique())):
df_patientevents_categorylabels_dict = df_patientevents_categorylabels_dict.append({'eventtype': 'chart', 'category': category, 'label': item}, ignore_index=True)
# Get Lab items with labels & category
df = df_d_labitems
for category_idx, category in enumerate(sorted((df.category.astype(str).unique()))):
#print(category)
category_list = df[df['category']==category]
for item_idx, item in enumerate(sorted(category_list.label.astype(str).unique())):
df_patientevents_categorylabels_dict = df_patientevents_categorylabels_dict.append({'eventtype': 'lab', 'category': category, 'label': item}, ignore_index=True)
# Get HCPCS items with labels & category
df = df_d_hcpcs
for category_idx, category in enumerate(sorted((df.category.astype(str).unique()))):
#print(category)
category_list = df[df['category']==category]
for item_idx, item in enumerate(sorted(category_list.long_description.astype(str).unique())):
df_patientevents_categorylabels_dict = df_patientevents_categorylabels_dict.append({'eventtype': 'hcpcs', 'category': category, 'label': item}, ignore_index=True)
return df_patientevents_categorylabels_dict
#-------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------
# Data filtering by condition and outcome |
# |
"""
Resources to identify tables and variables of interest can be found in the MIMIC-IV official API
(https://mimic-iv.mit.edu/docs/)
"""
# QUERY IN ALL SINGLE PATIENT ICU STAY RECORD FOR KEYWORD MATCHING
def is_haim_patient_keyword_match(patient, keywords, verbose = 0):
# Inputs:
# patient -> Timebound ICU patient stay structure filtered by max_time_stamp or min_time_stamp if any
# keywords -> List of string keywords to attempt to match in an "OR" basis
# verbose -> Flag to print found keyword outputs (0,1,2)
#
# Outputs:
# is_key -> Boolean flag indicating if any of the input Keywords are present
# keyword_mask -> Array indicating which of the input Keywords are present (0-Absent, 1-Present)
# Retrieve list of all the contents of patient datastructures
patient_dfs_list = [## CORE
patient.core,
## HOSP
patient.diagnoses_icd,
patient.drgcodes,
patient.emar,
patient.emar_detail,
patient.hcpcsevents,
patient.labevents,
patient.microbiologyevents,
patient.poe,
patient.poe_detail,
patient.prescriptions,
patient.procedures_icd,
patient.services,
## ICU
patient.procedureevents,
patient.outputevents,
patient.inputevents,
patient.icustays,
patient.datetimeevents,
patient.chartevents,
## CXR
patient.cxr,
patient.imcxr,
## NOTES
patient.noteevents,
patient.dsnotes,
patient.ecgnotes,
patient.echonotes,
patient.radnotes
]
patient_dfs_dict = ['core', 'diagnoses_icd', 'drgcodes', 'emar', 'emar_detail', 'hcpcsevents', 'labevents', 'microbiologyevents', 'poe',
'poe_detail', 'prescriptions', 'procedures_icd', 'services', 'procedureevents', 'outputevents', 'inputevents', 'icustays',
'datetimeevents', 'chartevents', 'cxr', 'imcxr', 'noteevents', 'dsnotes', 'ecgnotes', 'echonotes', 'radnotes']
# Initialize query mask
keyword_mask = np.zeros([len(patient_dfs_list), len(keywords)])
for idx_df, patient_df in enumerate(patient_dfs_list):
for idx_keyword, keyword in enumerate(keywords):
try:
patient_df_text = patient_df.astype(str)
is_df_key = patient_df_text.sum(axis=1).str.contains(keyword, case=False).any()
if is_df_key:
keyword_mask[idx_df, idx_keyword]=1
if (verbose >= 2):
print('')
print('Keyword: ' + '"' + keyword + ' " ' + '(Found in "' + patient_dfs_dict[idx_df] + '" table )')
print(patient_df_text)
else:
keyword_mask[idx_df, idx_keyword]=0
except:
is_df_key = False
keyword_mask[idx_df, idx_keyword]=0
# Create final keyword mask
if keyword_mask.any():
is_key = True
else:
is_key = False
return is_key, keyword_mask
# QUERY IN ALL SINGLE PATIENT ICU STAY RECORD FOR INCLUSION CRITERIA MATCHING
def is_haim_patient_inclusion_criteria_match(patient, inclusion_criteria, verbose = 0):
# Inputs:
# patient -> Timebound ICU patient stay structure filtered by max_time_stamp or min_time_stamp if any
# inclusion_criteria -> Inclusion criteria in groups of keywords.
# Keywords in groups are follow and "OR" logic,
# while an "AND" logic is stablished among groups
# verbose -> Flag to print found keyword outputs (0,1,2)
#
# Outputs:
# is_included -> Boolean flag if inclusion criteria is found in patient
# inclusion_criteria_mask -> Binary mask of inclusion criteria found in patient
# Clean out process bar before starting
inclusion_criteria_mask = np.zeros(len(inclusion_criteria))
for idx_keywords, keywords in enumerate(inclusion_criteria):
is_included_flag, _ = is_haim_patient_keyword_match(patient, keywords, verbose)
inclusion_criteria_mask[idx_keywords] = is_included_flag
if inclusion_criteria_mask.all():
is_included = True
else:
is_included = False
# Print if patient has to be included
if (verbose >=2):
print('')
print('Inclusion Criteria: ' + str(inclusion_criteria))
print('Inclusion Vector: ' + str(inclusion_criteria_mask) + ' , To include: ' + str(is_included))
return is_included, inclusion_criteria_mask
# GENERATE ALL SINGLE PATIENT ICU STAY RECORDS FOR ENTIRE MIMIC-IV DATABASE
def search_key_mimiciv_patients(df_haim_ids, core_mimiciv_path, inclusion_criteria, verbose = 0):
# Inputs:
# df_haim_ids -> Dataframe with all unique available HAIM_MIMICIV records by key identifiers
# core_mimiciv_path -> Path to structured MIMIC IV databases in CSV files
#
# Outputs:
# nfiles -> Number of single patient HAIM files produced
# Clean out process bar before starting
sys.stdout.flush()
# List of key patients
key_haim_patient_ids = []
# Extract information for patient
nfiles = len(df_haim_ids)
with tqdm(total = nfiles) as pbar:
# Update process bar
nbase= 0
pbar.update(nbase)
#Iterate through all patients
for haim_patient_idx in range(nbase, nfiles):
#Load precomputed patient file
filename = f"{haim_patient_idx:08d}" + '.pkl'
patient = load_patient_object(core_mimiciv_path + 'pickle/' + filename)
#Check if patient fits keywords
is_key, _ = is_haim_patient_inclusion_criteria_match(patient, keywords, verbose)
if is_key:
key_haim_patient_ids.append(haim_patient_idx)
# Update process bar
pbar.update(1)
return key_haim_patient_ids
# GET MIMIC IV PATIENT LIST FILTERED BY DESIRED CONDITION
def get_haim_ids_only_by_condition(condition_tokens, core_mimiciv_path):
# Inputs:
# condition_tokens -> string identifier of the condition you want to isolate (condition_tokens= ['heart failure','chronic'])
# outcome_tokens -> string identifier of the outcome you want to isolate
# core_mimiciv_path -> path to folder where the base MIMIC IV dataset files are located
# Outputs:
# condition_outcome_df -> Dataframe including patients IDs with desired Condition, indicating the Outcome.
# Load necessary ICD diagnostic lists and general patient information
d_icd_diagnoses = pd.read_csv(core_mimiciv_path + 'hosp/d_icd_diagnoses.csv')
d_icd_diagnoses['long_title'] = d_icd_diagnoses['long_title'].str.lower()
diagnoses_icd['icd_code'] = diagnoses_icd['icd_code'].str.replace(' ', '')
admissions = pd.read_csv(core_mimiciv_path + 'core/admissions.csv')
patients = pd.read_csv(core_mimiciv_path + 'core/patients.csv')
admissions = pd.merge(admissions, patients, on = 'subject_id')
#list of unique hadm id with conditions specified
condition_list = []
condition_list = d_icd_diagnoses[d_icd_diagnoses['long_title'].str.contains(condition_keywords[0])]
for i in condition_keywords[1:]:
condition_list = condition_list[condition_list['long_title'].str.contains('chronic')]
icd_list = condition_list['icd_code'].unique().tolist()
hid_list_chf = diagnoses_icd[diagnoses_icd['icd_code'].isin(icd_list) &
(diagnoses_icd['seq_num']<=3)]['hadm_id'].unique().tolist()
pkl_id = pd.read_csv(core_mimiciv_path + 'pickle/haim_mimiciv_key_ids.csv')
id_hf = pkl_id[pkl_id['hadm_id'].isin(hid_list_chf)].drop_duplicates(subset='hadm_id')
# delete all pkl files with only less than 1 day recorded
pkl_list_adm = admissions[admissions['hadm_id'].isin(id_hf['hadm_id'])]
pkl_list_adm['dischtime'] = pd.to_datetime(pkl_list_adm['dischtime'])
pkl_list_adm['admittime'] = pd.to_datetime(pkl_list_adm['admittime'])
pkl_list_adm['deltatime'] = (pkl_list_adm['dischtime'] - pkl_list_adm['admittime']).astype('timedelta64[D]').values
pkl_no_zero = pkl_list_adm[pkl_list_adm['deltatime'] != 0]['hadm_id']
no_zero_id = pkl_id[pkl_id['hadm_id'].isin(pkl_no_zero)].drop_duplicates(subset='hadm_id')
haim_ids_list = no_zero_id['haim_id_pickle'].values
return haim_ids_list
#-------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------
# Core embeddings for MIMIC-IV Deep Fusion |
#
'''
'''
# LOAD CORE INFO OF MIMIC IV PATIENTS
def load_core_mimic_haim_info(core_mimiciv_path, df_haim_ids):
# Inputs:
# core_mimiciv_path -> Base path of mimiciv
# df_haim_ids -> Table of HAIM ids and corresponding keys
#
# Outputs:
# df_haim_ids_core_info -> Updated dataframe with integer representations of core data
# %% EXAMPLE OF USE
# df_haim_ids_core_info = load_core_mimic_haim_info(core_mimiciv_path)
# Load core table
df_mimiciv_core = pd.read_csv(core_mimiciv_path + 'core/core.csv')
# Generate integer representations of categorical variables in core
core_var_select_list = ['gender', 'ethnicity', 'marital_status', 'language','insurance']
core_var_select_int_list = ['gender_int', 'ethnicity_int', 'marital_status_int', 'language_int','insurance_int']
df_mimiciv_core[core_var_select_list] = df_mimiciv_core[core_var_select_list].astype('category')
df_mimiciv_core[core_var_select_int_list] = df_mimiciv_core[core_var_select_list].apply(lambda x: x.cat.codes)
# Combine HAIM IDs with core data
df_haim_ids_core_info = pd.merge(df_haim_ids, df_mimiciv_core, on=["subject_id", "hadm_id"])
return df_haim_ids_core_info
# GET DEMOGRAPHICS EMBEDDINGS OF MIMIC IV PATIENT
def get_demographic_embeddings(dt_patient, verbose=0):
# Inputs:
# dt_patient -> Timebound mimic patient structure
# verbose -> Flag to print found keyword outputs (0,1,2)
#
# Outputs:
# base_embeddings -> Core base embeddings for the selected patient
# %% EXAMPLE OF USE
# base_embeddings = get_demographic_embeddings(dt_patient, df_haim_ids_core_info, verbose=2)
# Retrieve dt_patient and get embeddings
demo_embeddings = dt_patient.core.loc[0, ['anchor_age', 'gender_int', 'ethnicity_int', 'marital_status_int', 'language_int', 'insurance_int']]
if verbose >= 1:
print(demo_embeddings)
demo_embeddings = demo_embeddings.values
return demo_embeddings
#-------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------
# TSFresh embeddings for MIMIC-IV Deep Fusion |
#
'''
## -> DEEP FUSION REPRESENTATION OF MIMIC-IV EHR USING TSFRESH
"tsfresh" is a python package. It automatically calculates a large number of time series
characteristics, the so called features. Further the package contains methods to evaluate
the explaining power and importance of such characteristics for regression or classification
tasks. https://tsfresh.readthedocs.io/en/latest/
'''
# TSFRESH FEATURE EXTRACTOR OF MIMICIV CHARTEVENTS
def get_chartevent_tsfresh_timeseries_embeddings(dt_patient, df_patientevents_categorylabels_dict, verbose=0):
# Inputs:
# dt_patient -> Timebound Patient ICU stay structure
# df_patientevents_categorylabels_dict -> MIMIC IV Event dictionary
# verbose -> Flag to print generated outputs (0,1,2)
#
# Outputs:
# evs_features -> TSfresh generated chart event features for each timeseries
# %% EXAMPLE OF USE
# evs_features = extract_chartevent_tsfresh_timeseries_embeddings(dt_patient, df_patientevents_categorylabels_dict, event_type, verbose=1)
# Stablish dynamics of progressbar
if verbose <= 1: disable_progressbar=True
else: disable_progressbar=False
# Prep features of empty timeseries features from TSFresh in the context of clinical data
fc_parameters = {"length": None,
"absolute_sum_of_changes": None,
"maximum": None,
"mean": None,
"mean_abs_change": None,
"mean_change": None,
"median": None,
"minimum": None,
"standard_deviation": None,
"variance": None,
"large_standard_deviation": [{"r": r * 0.2} for r in range(1, 5)],
# Comment by Yu: don't think we need the 1 for quntile?
"quantile": [{"q": q} for q in [.25, .5, .75, 1]],
"linear_trend": [{"attr": "pvalue"}, {"attr": "rvalue"}, {"attr": "intercept"},{"attr": "slope"}, {"attr": "stderr"}]}
x_hr =[0]
y_hr = np.arange(len(x_hr))
# Extract Features with TSFresh
timeseries = pd.DataFrame({'id': np.zeros_like(y_hr), 'valnum': y_hr, 'time': x_hr}, columns=['id', 'valnum', 'time'])
features_empty_timeseries = extract_features(timeseries, column_id="id", column_sort="time", disable_progressbar=True, default_fc_parameters=fc_parameters)
#Get patient events by event type
evs = dt_patient.chartevents
#List all types of chart events (Charts, Labs and signals)
for eventtype_idx, eventtype in enumerate(sorted((df_patientevents_categorylabels_dict.eventtype.unique()))):
if verbose >= 3: print('* ' + eventtype)
event_list = df_patientevents_categorylabels_dict[df_patientevents_categorylabels_dict['eventtype']==eventtype]
for category_idx, category in enumerate(sorted((df_patientevents_categorylabels_dict.category.unique()))):
if verbose >= 3: print('-> ' + category)
category_list = df_patientevents_categorylabels_dict[df_patientevents_categorylabels_dict['category']==category]
for item_idx, item in enumerate(sorted(category_list.label.unique())):
if verbose >= 3: print('---> ' + item)
# POPULATE FEATURE SPACE FOR PATIENT
item_chart = evs[evs['label']==item]
empty_timeseries = False
# Set x equal to the times
x_hr = item_chart.deltacharttime[item_chart.label==item]
if len(x_hr)==0:
x_hr =[0]
empty_timeseries = True
y_hr = item_chart.valuenum[item_chart.label==item]
y_hr = y_hr[~(np.isnan(y_hr))]
x_hr = x_hr[0:len(y_hr)]
if y_hr.empty:
y_hr = np.arange(len(x_hr))
extracted_features = features_empty_timeseries
else:
# Extract Features with TSFresh
timeseries = pd.DataFrame({'id': np.zeros_like(y_hr), 'valnum': y_hr, 'time': x_hr}, columns=['id', 'valnum', 'time'])
extracted_features = impute(extract_features(timeseries, column_id="id", column_sort="time", disable_progressbar=disable_progressbar, default_fc_parameters = fc_parameters))
if (eventtype_idx ==0) & (category_idx ==0) & (item_idx == 0):
evs_features = extracted_features
else:
evs_features = evs_features.append(extracted_features)
# Transform extracted features from 0-1
transformer = QuantileTransformer().fit(evs_features)
norm_evs_features = transformer.transform(evs_features)
norm_evs_features = np.asarray(norm_evs_features)
if verbose >= 1:
# Plot feature representation
plt.figure(figsize = (20,5))
plt.imshow(X, cmap='hot', interpolation='nearest', aspect='auto')
plt.colorbar(label="Patient Timeseries Features", orientation="vertical")
plt.show()
return norm_evs_features, evs_features
def pivot_chartevent(df, event_list):
# create a new table with additional columns with label list
df1 = df[['subject_id', 'hadm_id', 'stay_id', 'charttime']]
for event in event_list:
df1[event] = np.nan
#search in the abbreviations column
df1.loc[(df['label']==event), event] = df['valuenum'].astype(float)
df_out = df1.dropna(axis=0, how='all', subset=event_list)
return df_out
def pivot_labevent(df, event_list):
# create a new table with additional columns with label list
df1 = df[['subject_id', 'hadm_id', 'charttime']]
for event in event_list:
df1[event] = np.nan
#search in the label column
df1.loc[(df['label']==event), event] = df['valuenum'].astype(float)
df_out = df1.dropna(axis=0, how='all', subset=event_list)
return df_out
def pivot_procedureevent(df, event_list):
# create a new table with additional columns with label list
df1 = df[['subject_id', 'hadm_id', 'storetime']]
for event in event_list:
df1[event] = np.nan
#search in the label column
df1.loc[(df['label']==event), event] = df['value'].astype(float) #Yu: maybe if not label use abbreviation
df_out = df1.dropna(axis=0, how='all', subset=event_list)
return df_out
#FUNCTION TO COMPUTE A LIST OF TIME SERIES FEATURES
def get_ts_emb(df_pivot, event_list):
# Inputs:
# df_pivot -> Pivoted table
# event_list -> MIMIC IV Type of Event
#
# Outputs:
# df_out -> Embeddings
# %% EXAMPLE OF USE
# df_out = get_ts_emb(df_pivot, event_list)
# Initialize table
try:
df_out = df_pivot[['subject_id', 'hadm_id']].iloc[0]
except:
# print(df_pivot)
df_out = pd.DataFrame(columns = ['subject_id', 'hadm_id'])
# df_out = df_pivot[['subject_id', 'hadm_id']]
#Adding a row of zeros to df_pivot in case there is no value
df_pivot = df_pivot.append(pd.Series(0, index=df_pivot.columns), ignore_index=True)
#Compute the following features
for event in event_list:
series = df_pivot[event].dropna() #dropna rows
if len(series) >0: #if there is any event
df_out[event+'_max'] = series.max()
df_out[event+'_min'] = series.min()
df_out[event+'_mean'] = series.mean(skipna=True)
df_out[event+'_variance'] = series.var(skipna=True)
df_out[event+'_meandiff'] = series.diff().mean() #average change
df_out[event+'_meanabsdiff'] =series.diff().abs().mean()
df_out[event+'_maxdiff'] = series.diff().abs().max()
df_out[event+'_sumabsdiff'] =series.diff().abs().sum()
df_out[event+'_diff'] = series.iloc[-1]-series.iloc[0]
#Compute the n_peaks
peaks,_ = find_peaks(series) #, threshold=series.median()
df_out[event+'_npeaks'] = len(peaks)
#Compute the trend (linear slope)
if len(series)>1:
df_out[event+'_trend']= np.polyfit(np.arange(len(series)), series, 1)[0] #fit deg-1 poly
else:
df_out[event+'_trend'] = 0
return df_out
def get_ts_embeddings(dt_patient, event_type):
# Inputs:
# dt_patient -> Timebound Patient ICU stay structure
#
# Outputs:
# ts_emb -> TSfresh-like generated Lab event features for each timeseries
#
# %% EXAMPLE OF USE
# ts_emb = get_labevent_ts_embeddings(dt_patient)
#Get chartevents
if(event_type == 'procedure'):
df = dt_patient.procedureevents
#Define chart events of interest
event_list = ['Foley Catheter', 'PICC Line', 'Intubation', 'Peritoneal Dialysis',
'Bronchoscopy', 'EEG', 'Dialysis - CRRT', 'Dialysis Catheter',
'Chest Tube Removed', 'Hemodialysis']
df_pivot = pivot_procedureevent(df, event_list)
elif(event_type == 'lab'):
df = dt_patient.labevents
#Define chart events of interest
event_list = ['Glucose', 'Potassium', 'Sodium', 'Chloride', 'Creatinine',
'Urea Nitrogen', 'Bicarbonate', 'Anion Gap', 'Hemoglobin', 'Hematocrit',
'Magnesium', 'Platelet Count', 'Phosphate', 'White Blood Cells',
'Calcium, Total', 'MCH', 'Red Blood Cells', 'MCHC', 'MCV', 'RDW',
'Platelet Count', 'Neutrophils', 'Vancomycin']
df_pivot = pivot_labevent(df, event_list)
elif(event_type == 'chart'):
df = dt_patient.chartevents
#Define chart events of interest
event_list = ['Heart Rate','Non Invasive Blood Pressure systolic',
'Non Invasive Blood Pressure diastolic', 'Non Invasive Blood Pressure mean',
'Respiratory Rate','O2 saturation pulseoxymetry',
'GCS - Verbal Response', 'GCS - Eye Opening', 'GCS - Motor Response']
df_pivot = pivot_chartevent(df, event_list)
#Pivote df to record these values
ts_emb = get_ts_emb(df_pivot, event_list)
try:
ts_emb = ts_emb.drop(['subject_id', 'hadm_id']).fillna(value=0)
except:
ts_emb = pd.Series(0, index=ts_emb.columns).drop(['subject_id', 'hadm_id']).fillna(value=0)
return ts_emb
#-------------------------------------------------------------------------------------------------
#-------------------------------------------------------------------------------------------------
# Biobert Chart Event embeddings for MIMIC-IV Deep Fusion |
#