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To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary, which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world\n            <jats:italic>in vivo<\/jats:italic>\n            dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients.\n          <\/jats:p>","DOI":"10.1145\/3563779","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T11:22:39Z","timestamp":1663845759000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2768-7450","authenticated-orcid":false,"given":"Pascal","family":"Fechner","sequence":"first","affiliation":[{"name":"inContAlert GmbH, Research Center Finance &amp; Information Management, University of Bayreuth"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7879-0239","authenticated-orcid":false,"given":"Fabian","family":"K\u00f6nig","sequence":"additional","affiliation":[{"name":"Research Center Finance &amp; Information Management, University of Applied Sciences Augsburg, Branch Business &amp; Information Systems Engineering of the Fraunhofer FIT"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9815-0653","authenticated-orcid":false,"given":"Wolfgang","family":"Kratsch","sequence":"additional","affiliation":[{"name":"Research Center Finance &amp; Information Management, University of Applied Sciences Augsburg, Branch Business &amp; Information Systems Engineering of the Fraunhofer FIT"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0077-8579","authenticated-orcid":false,"given":"Jannik","family":"Lockl","sequence":"additional","affiliation":[{"name":"inContAlert GmbH, University of Bayreuth, University College London"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4743-4511","authenticated-orcid":false,"given":"Maximilian","family":"R\u00f6glinger","sequence":"additional","affiliation":[{"name":"Research Center Finance &amp; Information Management, University of Bayreuth, Branch Business &amp; Information Systems Engineering of the Fraunhofer FIT"}]}],"member":"320","published-online":{"date-parts":[[2023,1,25]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. 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