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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments.

Elena Casiraghi^1,2, Dario Malchiodi^1,2,3, Gabriella Trucco¹

¹Department of Computer Science "Giovanni degli Antoni,"Università degli Studi di Milano 20133 Milan Italy.

IEEE Access : Practical Innovations, Open Solutions

|November 23, 2021

Summary

A new machine learning system accurately predicts COVID-19 patient risk using radiological and clinical data. This tool aids emergency departments in rapid decision-making for patient care during the pandemic.

Keywords:

Associative tree Boruta feature selection COVID-19 clinical data analysis generalized linear models missing data imputation random forest classifier risk prediction

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Epidemiology

Background:

The COVID-19 pandemic caused over one million deaths globally by October 2020.
Emergency departments require rapid risk assessment for COVID-19 patients.
Chest radiographs (CXR) have low sensitivity for COVID-19 risk prediction, necessitating additional criteria.

Purpose of the Study:

To develop a computerized system for extracting key variables to improve COVID-19 patient risk prediction.
To create an explainable machine learning system offering simple decision criteria for clinicians.
To support emergency departments in assessing patient risk for COVID-19.

Main Methods:

Combined Boruta and Random Forest (RF) with 10-fold cross-validation for robust variable selection.
Trained an RF classifier using selected important variables.
Extracted, simplified, and pruned RF rules to build an associative tree for clinical decision support.

Main Results:

A neural network-derived radiological score strongly correlated with radiologist assessments.
Laboratory variables and comorbidity count significantly improved risk prediction.
The machine learning approach demonstrated effective and robust prediction performance compared to generalized linear models.

Conclusions:

The developed machine learning system accurately predicts COVID-19 patient risk.
The system integrates radiological, clinical, and laboratory data for comprehensive assessment.
This computational tool is deployable in emergency departments for rapid and accurate risk stratification of COVID-19 patients.