Explainable AI for Early Detection of Health Changes Via Streaming Clustering
The ability to explain the predictions of machine learning models has become increasingly important, especially in healthcare applications. Streaming clustering is an effective tool to recognize normal baseline patterns and to detect early signs of changes in data streams. However, many streaming cl...
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| Vydáno v: | IEEE International Fuzzy Systems conference proceedings s. 1 - 6 |
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IEEE
18.07.2022
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| ISSN: | 1558-4739 |
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| Abstract | The ability to explain the predictions of machine learning models has become increasingly important, especially in healthcare applications. Streaming clustering is an effective tool to recognize normal baseline patterns and to detect early signs of changes in data streams. However, many streaming clustering algorithms are not designed to explain to the users how predictions are made. In this paper, we extend a streaming clustering algorithm, the sequential possibilistic Gaussian mixture model (SPGMM) for early detection of health change to provide algorithm explainability for the results. Four approaches are discussed to explain either the cluster differences or the reason for the algorithm warnings: (i) linguistic summarization for warnings; (ii) annotation distribution of clusters; (iii) SHapley Additive exPlanations (SHAP); (iv) functional health score. The four approaches are validated on one older adult monitored with a collection of motion, bed, and depth sensors over three years. The results obtained on the older adult show that the four approaches aid understanding of how the clusters and warnings are generated, providing strong support for clinicians to take corresponding actions. |
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| AbstractList | The ability to explain the predictions of machine learning models has become increasingly important, especially in healthcare applications. Streaming clustering is an effective tool to recognize normal baseline patterns and to detect early signs of changes in data streams. However, many streaming clustering algorithms are not designed to explain to the users how predictions are made. In this paper, we extend a streaming clustering algorithm, the sequential possibilistic Gaussian mixture model (SPGMM) for early detection of health change to provide algorithm explainability for the results. Four approaches are discussed to explain either the cluster differences or the reason for the algorithm warnings: (i) linguistic summarization for warnings; (ii) annotation distribution of clusters; (iii) SHapley Additive exPlanations (SHAP); (iv) functional health score. The four approaches are validated on one older adult monitored with a collection of motion, bed, and depth sensors over three years. The results obtained on the older adult show that the four approaches aid understanding of how the clusters and warnings are generated, providing strong support for clinicians to take corresponding actions. |
| Author | Wu, Wenlong Popescu, Mihail Skubic, Marjorie Keller, James M. |
| Author_xml | – sequence: 1 givenname: Wenlong surname: Wu fullname: Wu, Wenlong email: ww6p9@mail.missouri.edu organization: University of Missouri-Columbia,Electrical Engineering and Computer Science Department,Columbia,MO,USA,65211 – sequence: 2 givenname: James M. surname: Keller fullname: Keller, James M. organization: University of Missouri-Columbia,Electrical Engineering and Computer Science Department,Columbia,MO,USA,65211 – sequence: 3 givenname: Marjorie surname: Skubic fullname: Skubic, Marjorie organization: University of Missouri-Columbia,Electrical Engineering and Computer Science Department,Columbia,MO,USA,65211 – sequence: 4 givenname: Mihail surname: Popescu fullname: Popescu, Mihail organization: University of Missouri-Columbia,Health Management and Informatics Department,Columbia,MO,USA,65211 |
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| Snippet | The ability to explain the predictions of machine learning models has become increasingly important, especially in healthcare applications. Streaming... |
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| SubjectTerms | Annotations Behavioral sciences Clustering algorithms explainable ai Linguistics Machine learning Prediction algorithms Predictive models streaming clustering |
| Title | Explainable AI for Early Detection of Health Changes Via Streaming Clustering |
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