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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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Towards an Explainable AI-Based Tool to Predict Preterm Birth.

Ilias Kyparissidis Kokkinidis¹, Evangelos Logaras¹, Emmanouil S Rigas¹

¹Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece.

Studies in Health Technology and Informatics

|May 19, 2023

Summary

This summary is machine-generated.

Artificial Intelligence models can predict preterm birth (PTB) risk using patient data. An ensemble voting model achieved high accuracy, offering potential clinical decision support for expectant mothers.

Keywords:

Artificial Intelligence (AI)Clinical Decision Support Systems Explainable Artificial Intelligence (XAI)Machine Learning (ML)Obstetrics and Gynecology (specialty)Premature Birth

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Preterm EEG: A Multimodal Neurophysiological Protocol

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Published on: February 18, 2012

Area of Science:

Medical Informatics
Computational Biology
Maternal Health

Background:

Preterm birth (PTB) before 37 weeks of gestation poses significant health risks.
Accurate prediction of PTB is crucial for timely intervention and improved outcomes.
Existing predictive methods may lack sufficient accuracy or interpretability.

Purpose of the Study:

To develop and evaluate Artificial Intelligence (AI)-based predictive models for estimating PTB probability.
To utilize a comprehensive dataset including clinical, demographic, and historical information.
To enhance model trustworthiness through explainability features.

Main Methods:

Applied various Machine Learning (ML) algorithms to a dataset of 375 pregnant women.
Utilized objective results, screening variables, demographics, and medical/social history.
Employed an ensemble voting model as the primary predictive approach.

Main Results:

The ensemble voting model demonstrated strong predictive performance.
Achieved an area under the receiver operating characteristic curve (ROC-AUC) of approximately 0.84.
Obtained an area under the precision-recall curve (PR-AUC) of approximately 0.73.

Conclusions:

AI-driven models show significant promise for accurate PTB prediction.
The ensemble voting model offers a robust and reliable approach.
Explainability efforts aim to increase clinical adoption and trust in AI predictions for PTB.