Development and validation of EWS systems 🚑

Contents

• preprocessing

  Contains info on initial pre-processing of Electronic Health Records consisting of blood tests, diagnoses, various procedures, and intensive care data for individuals residing in Denmark, with a general admission to the hospitals in the region of Zealand, Denmark, between 2018-2023.

  • Original_Preprocessing.ipynb contains python code with an initial pre-processing of all datasets with clinical information.

  • Intensive_Care.R contains R code with more thorough analysis of intensive care data.

  • Diagnoses.R contains R code performing categorization/grouping of various ICD-10 diagnoses of patients.

  • Blood_Tests.py contains python code on imputation of blood tests containing string values not suitable for analysis.

  • Procedures.R contains R code on various medical procedures for each individual. Categorization of procedures (SKS-Codes) has been performed + text mining/topic modelling for the characterization of them.

• Merging

  • EWS_Blood.py contains python code on merging of EWS (Early Warning Score) data of individuals with blood tests.

  • EWS_ITA.py contains python code on merging EWS + Blood Tests with Intensive Care data

  • EWS_Blood_ITA_Procedures.R contains R code on merging EWS + Blood Tests + Intensive Care with Procedures data

  • EWS_Final_Merging.py contains python code on the final merging of the datasets (diagnoses included)

• modelling

  • Evaluating_NEWS2_Only.r :
    • Contains R code for the validation of NEWS2 system in terms of predictive performance
    • 🔗 Internal-External Cross-Validation (IECV) based on hospitals
    • 🔗 AUC, Brier Score, Calibration, Net Benefit
    • 🔗 Thresholds added in the Decision Curve Analysis
  • IECV_NEWS2.r :
    • Contains R code on IECV with a meta-analysis approach
  • Development_Comparisons.r :
    • Contains R code comparing various models and algorithms with the current NEWS2 system
      • 🔗 NEWS2-Light: NEWS2 - Blood Pressure - Temperature
      • 🔗 IEWS: NEWS2 + Age + Sex
      • 🔗 TREE-EWS: XGBoost with Age,Sex,Vital Signs, Previous Hospitalization & Blood Tests
      • 🔗 Weighted performance metrics

• To do list:

  • Assessment of NEWS2 current system based on predictive performance metrics using data-splitting techniques ✅.

  • De-biasing the dataset with IPW based on intervention scenarios ✅

  • Development of alternative early warning score systems and model comparison ✅

  • Add calibration plots for the newly developed models ✅

  • Assess sustained recovery prediction of NEWS2 ✅

  • Assess performance on various strata of target population / Assess fairness 🔨

  • Try a DL architecture as an additional benchmark 🚩

  • Create a Table 1 ✅