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Comput. Healthcare"],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>Machine Learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, their black-box character. This article investigates the usefulness of<jats:italic>Explainable Artificial Intelligence<\/jats:italic>(XAI) methods to increase transparency in automated<jats:italic>clinical gait classification<\/jats:italic>based on time series. For this purpose, predictions of state-of-the-art classification methods are explained with a XAI method called Layer-wise Relevance Propagation (LRP). Our main contribution is an approach that explains class-specific characteristics learned by ML models that are trained for gait classification. We investigate several gait classification tasks and employ different classification methods, i.e.,\u00a0Convolutional Neural Network, Support Vector Machine, and Multi-layer Perceptron. We propose to evaluate the obtained explanations with two complementary approaches: a statistical analysis of the underlying data using Statistical Parametric Mapping and a qualitative evaluation by two clinical experts. A gait dataset comprising ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls is utilized. Our experiments show that explanations obtained by LRP exhibit promising statistical properties concerning inter-class discriminativity and are also in line with clinically relevant biomechanical gait characteristics.<\/jats:p>","DOI":"10.1145\/3474121","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T16:29:43Z","timestamp":1640017783000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":59,"title":["Explaining Machine Learning Models for Clinical Gait Analysis"],"prefix":"10.1145","volume":"3","author":[{"given":"Djordje","family":"Slijepcevic","sequence":"first","affiliation":[{"name":"Institute of Creative Media Technologies, Department of Media &amp; DigitalTechnologies, St. P\u00f6lten University of Applied Sciences, St. P\u00f6lten, Austria"}]},{"given":"Fabian","family":"Horst","sequence":"additional","affiliation":[{"name":"Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Mainz, Germany"}]},{"given":"Sebastian","family":"Lapuschkin","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany"}]},{"given":"Brian","family":"Horsak","sequence":"additional","affiliation":[{"name":"Institute of Health Sciences, Department of Health Sciences, St. P\u00f6lten University of Applied Sciences, Austria and Center for Digital Health and Social Innovation, St. P\u00f6lten University of Applied Sciences, St. P\u00f6lten, Austria"}]},{"given":"Anna-Maria","family":"Raberger","sequence":"additional","affiliation":[{"name":"Institute of Health Sciences, Department of Health Sciences, St. P\u00f6lten University of Applied Sciences, St. P\u00f6lten, Austria"}]},{"given":"Andreas","family":"Kranzl","sequence":"additional","affiliation":[{"name":"Laboratory for Gait and Movement Analysis, Orthopaedic Hospital Vienna-Speising, Vienna, Austria"}]},{"given":"Wojciech","family":"Samek","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany"}]},{"given":"Christian","family":"Breiteneder","sequence":"additional","affiliation":[{"name":"Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria"}]},{"given":"Wolfgang Immanuel","family":"Sch\u00f6llhorn","sequence":"additional","affiliation":[{"name":"Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Germany"}]},{"given":"Matthias","family":"Zeppelzauer","sequence":"additional","affiliation":[{"name":"Institute of Creative Media Technologies, Department of Media &amp; Digital Technologies, St. P\u00f6lten University of Applied Sciences, Austria"}]}],"member":"320","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.5555\/3327546.3327621"},{"key":"e_1_3_3_4_2","first-page":"453","volume-title":"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS)","author":"Alaqtash Murad","year":"2011","unstructured":"Murad Alaqtash, Thompson Sarkodie-Gyan, Huiying Yu, Olac Fuentes, Richard Brower, and Amr Abdelgawad. 2011. 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