A Systematic Literature Review of Machine Learning Applications for Community-Acquired Pneumonia

Lozano-Rojas, Daniel; Free, Robert C; McEwan, Alistair A.; Woltmann, Gerrit

doi:10.1007/978-981-16-3880-0_30

Lecture Notes in Electrical Engineering

2021

DOI: 10.1007/978-981-16-3880-0_30

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A Systematic Literature Review of Machine Learning Applications for Community-Acquired Pneumonia

Daniel Lozano-Rojas

Robert C Free

Alistair A. McEwan

et al.

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2023

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Explainable artificial intelligence for omics data: a systematic mapping study

Toussaint,

Leiser,

Thiebes

et al. 2023

Briefings in Bioinformatics

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Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies’ AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners.

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Explainable artificial intelligence for omics data: a systematic mapping study

Toussaint,

Leiser,

Thiebes

et al. 2023

Briefings in Bioinformatics

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A Multi-Center Clinical Trial for Wireless Stethoscope-Based Diagnosis and Prognosis of Children Community-Acquired Pneumonia

Huang

Wang

2023

IEEE Trans. Biomed. Eng.

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A Systematic Literature Review of Machine Learning Applications for Community-Acquired Pneumonia

Cited by 2 publications

References 28 publications

Explainable artificial intelligence for omics data: a systematic mapping study

Explainable artificial intelligence for omics data: a systematic mapping study

A Multi-Center Clinical Trial for Wireless Stethoscope-Based Diagnosis and Prognosis of Children Community-Acquired Pneumonia

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