SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction
From JALM Talk Podcast by Association for Diagnostics and Laboratory Medicine
September 4, 2025 · 11 min
About this episode
This episode discusses the SBC-SHAP project aimed at improving the accessibility and interpretability of machine learning algorithms for predicting sepsis.
Topics covered
- machine learning
- sepsis prediction
- healthcare accessibility
- algorithm interpretability
- data science
Keywords
- machine learning
- sepsis
- healthcare
- algorithm
- interpretability
- data science
- accessibility
Mentioned in this episode
Organizations: Association for Diagnostics and Laboratory Medicine
More episodes of JALM Talk Podcast
- An Overview of Allowable Total Error in the Clinical Laboratory · March 13, 2026 · 12 min
- Toward Sustainable Clinical Chemistry: Waste Quantification and Reduction Strategies from Testing on 4 Commonly Used Automated Analyzers · March 10, 2026 · 8 min
- Design and Analytical Evaluation of an Immunoassay for Long Forms of Cardiac Troponin T · January 28, 2026 · 7 min
- Assessing and Resolving Findings of Sex Chromosome Discordance in Genetic Testing · January 13, 2026 · 11 min
- ADLM Guidance Document on Laboratory Testing for Drugs of Misuse to Support the Emergency Department · January 13, 2026 · 11 min
- Concurrent Anti-PR3 Immunoassay and cANCA Indirect Immunofluorescence Testing Provide Complementary Information for Clinical Laboratory Detection of Antineutrophil Cytoplasmic Antibodies · January 5, 2026 · 12 min
Explore listener stats, chart rankings, contacts and more on the JALM Talk Podcast podcast page.