A5093787719- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare
- Sepsis Diagnosis and Treatment
- COVID-19 diagnosis using AI
- Explainable Artificial Intelligence (XAI)
- Reliability and Agreement in Measurement
- Artificial Intelligence in Healthcare and Education
- Bacterial Identification and Susceptibility Testing
Norwegian University of Science and Technology
2024
Medical histories of patients can predict a patient’s immediate future. While most studies propose to survival from vital signs and hospital tests within one episode care, we carried out selective feature engineering longitudinal medical records in this study develop dataset with derived features. We thereafter trained multiple machine learning models for the binary prediction whether an care will culminate death among suspected bloodstream infections. The classifier performance is evaluated...
This study aimed to investigate the predictive capabilities of historical patient records predict adverse outcomes such as mortality, readmission, and prolonged length stay (PLOS).
Abstract Bloodstream infections (BSIs) represent a critical public health concern, primarily due to their rapid progression and severe implications such as sepsis septic shock. This study introduces an innovative Explanable Artificial Intelligence (XAI) framework, leveraging historical electronic records (EHRs) enhance BSI prediction. Unlike traditional models that rely heavily on real-time clinical data, our XAI-based approach utilizes comprehensive dataset incorporating demographic...
Abstract Objective The aim of this study was to investigate predictive capabilities historical records patients maintained at hospitals towards predicting an impending adverse outcomes such as, mortality, readmission, and prolonged length stay (PLOS). Methods Leveraging a de-identified dataset from tertiary care university hospital, we developed eXplainable Artificial Intelligence (XAI) framework combining tree-based traditional ML models with interpretations, statistical analysis predictors...
Bloodstream infections (BSIs) are a severe public health threat due to their rapid progression into critical conditions like sepsis. This study presents novel eXplainable Artificial Intelligence (XAI) framework predict BSIs using historical electronic records (EHRs). Leveraging dataset from St. Olavs Hospital in Trondheim, Norway, encompassing 35,591 patients, the integrates demographic, laboratory, and comprehensive medical history data classify patients high-risk low-risk BSI groups. By...