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In this research, we design a wavelet transform based additive convolutional neural network (WT-A-CNN) that requires only patients\u2019 vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-the-art baselines on both ICU mortality and length-of-stay prediction tasks. Furthermore, the additive structure of the model can be used for model interpretation and analyzing which input signal is more useful for ICU outcome prediction for different patient cohorts. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to act promptly on patients at risk and reduce the negative impacts on patient outcomes.<\/jats:p>","DOI":"10.1145\/3727624","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T07:18:14Z","timestamp":1743578294000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Deep Learning Method"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8151-1563","authenticated-orcid":false,"given":"Yiqun","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Industrial and Manufacturing Systems Engineering, Iowa State University","place":["Ames, United States"]},{"name":"Mayo Clinic","place":["Ames, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2168-9594","authenticated-orcid":false,"given":"Shaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Industrial and Manufacturing Systems Engineering, Iowa State University","place":["Ames, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3069-3878","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Industrial and Manufacturing Systems Engineering, Iowa State University","place":["Ames, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5768-7702","authenticated-orcid":false,"given":"Wenli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Business Analytics, Iowa State University","place":["Ames, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/26\/5\/R01"},{"key":"e_1_3_2_3_2","first-page":"4699","volume-title":"Advances in Neural Information Processing Systems","author":"Agarwal Rishabh","year":"2021","unstructured":"Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, and Geoffrey E. 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