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A Data-Driven Approach to Quantifying Immune States in Sepsis

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Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task.

Han Wu¹, Wenjie Ruan¹, Jiangtao Wang²

¹University of Exeter EX4 4PY Exeter U.K.

IEEE Transactions on Artificial Intelligence

|November 13, 2023

Summary

This summary is machine-generated.

Machine learning models can predict COVID-19 severity by interpreting biomarkers. Increased N-terminal pro-brain natriuretic peptide, C-reaction protein, lactic dehydrogenase, and decreased lymphocytes indicate severe infection and mortality risk.

Keywords:

Artificial intelligence in health artificial intelligence in medicine interpretable machine learning

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Area of Science:

Biomedical Informatics
Artificial Intelligence in Medicine
Computational Biology

Background:

The "black-box" nature of machine learning (ML) models impedes trust and deployment in critical medical applications like COVID-19 diagnosis.
Interpreting ML models can reveal crucial biomarkers, aiding clinicians overwhelmed during pandemics.
Identifying reliable biomarkers is essential for predicting COVID-19 severity and patient outcomes.

Purpose of the Study:

To interpret machine learning models for identifying biomarkers associated with COVID-19 infection severity.
To validate the identified biomarkers using independent datasets.
To enhance the clinical utility of high-accuracy ML diagnostic tools.

Main Methods:

Utilized four ML models: decision trees, random forests, gradient boosted trees, and neural networks.
Employed various interpretation techniques: permutation feature importance, partial dependence plots, individual conditional expectation, accumulated local effects, LIME, and SHAP.
Analyzed two datasets: 92 patients in Zhuhai, China, and 5644 patients from Kaggle (Hospital Israelita Albert Einstein).

Main Results:

Identified increased N-terminal pro-brain natriuretic peptide, C-reaction protein, and lactic dehydrogenase, alongside decreased lymphocytes, as indicators of severe COVID-19 and mortality risk.
Validated findings on a larger dataset, confirming the association with severe infection.
Discovered leukocytes, eosinophils, and platelets as additional indicative biomarkers for COVID-19.

Conclusions:

Model interpretation techniques effectively identify key biomarkers for COVID-19 severity prediction.
Biomarkers such as N-terminal pro-brain natriuretic peptide, C-reaction protein, lactic dehydrogenase, and lymphocytes are crucial for assessing COVID-19 prognosis.
Leukocytes, eosinophils, and platelets also serve as significant indicators for COVID-19, supporting the use of interpretable AI in clinical settings.