An Explainable Machine Learning Model and Bedside Nomogram Support Hemodialysis Decision-Making in Lithium Poisoning

A Systematic Review of Machine Learning, Deep Learning, and Explainable AI Approaches for Cardiac Disease Prediction

Sunanda Budihal, Sheetalrani Kawale, Abhishek Angadi

The cardiovascular (Cardiac) disease (CVD) is another factor that causes death among the global population most, and this is the reason why there is a high necessity to implement proper, effective, and interpretive diagnostic systems. The usage of machine learning (ML), deep lear…Read more

Also available via: European Organization for Nuclear Research

Cloud-Native Clinical Decision Support: Deploying Serverless Machine Learning Middleware for Real-Time Hospital Flow Optimization and Surgical Delay Prediction

YINKA ADERIBIGBE

The application of machine learning in healthcare presents unprecedented opportunities for optimizing hospital flow and mitigating surgical delays. However, the deployment of clinical decision support systems is frequently bottlenecked by the fragmented, unstructured nature of El…Read more

Before the Model: Why Datasets and Data Representation Define What Machine Learning Can Learn

Jean Franck Loa Rojas

Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…Read more

Human in the Loop: Alignment and Control in safety-critical and life-or-death decision-making by autonomous machines

Harris Georgiou

ABSTRACT: As Artificial Intelligence (AI) becomes “smarter”, it brings forward more fundamental rather than technical problems to solve, entailing legal, ethical or even philosophical questions. In this chapter, the core issue of AI “alignment” is investigated under the scope of…Read more

Reproducibility Package for Explainable and Leakage-Conscious Machine Learning for Athlete Injury Risk Modeling Across Heterogeneous Datasets

Abdülkadir Enes GÖRGÜLÜ, Eray Dursun, Serdar SOLAK

This reproducibility package supports the manuscript “Explainable and Leakage-Conscious Machine Learning for Athlete Injury Risk Modeling Across Heterogeneous Datasets.” It contains the executed and clean analysis notebooks, the corresponding Python script, exact software-version…Read more

Village-Level Suitability Assessment For Litchi Cultivation In The Malwa Region Using Explainable Machine Learning And Geospatial Data

Dr. Pankaj Malik, Mishthi Patodia, Vedant soni, Deepika Kumari, Pragati Agrawal, Jaiswal Tanmay

Litchi is a high-value fruit crop traditionally cultivated in regions with favorable climatic and soil conditions. Expanding litchi cultivation into non-traditional areas such as the Malwa region of Madhya Pradesh requires accurate identification of suitable locations to minimize…Read more