Interpretability of machine learning‐based prediction models in healthcare
By Gregor Stiglic, Primož Kocbek, Nino Fijacko, Marinka Zitnik, Katrien Verbert, Leona Cilar. WIREs Data Mining Knowl. Discov., 2020
523 citations recorded by the source corpus.
There is a need of ensuring that learning (ML) models are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end‐users. Further, interpretable ML models allow healthcare experts to make reasonable and data‐driven decisions to provide personalized decisions that can ultimately lead to higher quality of service in healthcare. Generally, we can classify interpretability approaches in two groups where the first focuses on personalized interpretation (local interpretability) while the second summarizes prediction models on a population level (global interpretability). Alternatively, we can group interpretability methods into model‐specific techniques, which are designed to interpret predictions generated by a specific model, such as a neural network, and model‐agnostic approaches, which provide easy‐to‐understand explanations of predictions made by any ML model. Here, we give an overview of interpretability approaches using structured data and provide examples of practical interpretability of ML in different areas of healthcare, including prediction of health‐related outcomes, optimizing treatments, or improving the efficiency of screening for specific conditions. Further, we outline future directions for interpretable ML and highlight the importance of developing algorithmic solutions that can enable ML driven decision making in high‐stakes healthcare problems.
Subfields: Artificial Intelligence, Health Informatics
View Paper: https://www.semanticscholar.org/paper/9b435ea80b6f90800339b3bffeafab6c990caa88
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In Artificial Intelligence
- Amitojdeep Singh
- Francesco Piccialli
- Imran Ahmed
- Gwanggil Jeon
- Vasudevan Lakshminarayanan
- Sourya Sengupta
Citations in the corpus
- From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where (2022, background): “Several techniques have been introduced in the literature to analyze explainability models [46], [47].”
- Trust in AI: progress, challenges, and future directions (2024, unknown): “Intrinsic interpretability refers to techniques that are intrinsically inter-pretable due to their structure (Ai and Narayanan, 2021; Pintelas et al., 2020; Stiglic et al., 2020).”
- Explainable Deep Learning Models in Medical Image Analysis (2020, background): “Different kinds of taxonomies and classification methods are discussed briefly here and a detailed analysis of the taxonomies can be found in [8,9] and a flow chart for them is shown in 1.”