{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:45:14Z","timestamp":1769636714997,"version":"3.49.0"},"reference-count":23,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T00:00:00Z","timestamp":1635292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Axencia Galega de Innovacion","award":["IN845D-2020\/29"],"award-info":[{"award-number":["IN845D-2020\/29"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the continuous monitoring process of critical patients, Intensive Care Units (ICU) generate large amounts of data, which are difficult for healthcare personnel to analyze manually, especially in overloaded situations such as those present during the COVID-19 pandemic. Therefore, the automatic analysis of these data has many practical applications in patient monitoring, including the optimization of alarm systems for alerting healthcare personnel. In this paper, explainable machine learning techniques are used for this purpose, with a methodology based on age-stratification, boosting classifiers, and Shapley Additive Explanations (SHAP) proposed. The methodology is evaluated using MIMIC-III, an ICU patient research database. The results show that the proposed model can predict mortality within the ICU with AUROC values of 0.961, 0.936, 0.898, and 0.883 for age groups 18\u201345, 45\u201365, 65\u201385 and 85+, respectively. By using SHAP, the features with the highest impact in predicting mortality for different age groups and the threshold from which the value of a clinical feature has a negative impact on the patient\u2019s health can be identified. This allows ICU alarms to be improved by identifying the most important variables to be sensed and the threshold values at which the health personnel must be warned.<\/jats:p>","DOI":"10.3390\/s21217125","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T23:24:42Z","timestamp":1635377082000},"page":"7125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2334-1556","authenticated-orcid":false,"given":"Jos\u00e9 A.","family":"Gonz\u00e1lez-N\u00f3voa","sequence":"first","affiliation":[{"name":"Cardiovascular Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), 36213 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1464-2616","authenticated-orcid":false,"given":"Laura","family":"Busto","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), 36213 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0919-1793","authenticated-orcid":false,"given":"Juan J.","family":"Rodr\u00edguez-Andina","sequence":"additional","affiliation":[{"name":"Department of Electronic Technology, University of Vigo, 36310 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7425-7541","authenticated-orcid":false,"given":"Jos\u00e9","family":"Fari\u00f1a","sequence":"additional","affiliation":[{"name":"Department of Electronic Technology, University of Vigo, 36310 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marta","family":"Segura","sequence":"additional","affiliation":[{"name":"Intensive Care Unit Department, Hospital \u00c1lvaro Cunqueiro (SERGAS), 36213 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7793-531X","authenticated-orcid":false,"given":"Vanesa","family":"G\u00f3mez","sequence":"additional","affiliation":[{"name":"Intensive Care Unit Department, Hospital \u00c1lvaro Cunqueiro (SERGAS), 36213 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dolores","family":"Vila","sequence":"additional","affiliation":[{"name":"Intensive Care Unit Department, Hospital \u00c1lvaro Cunqueiro (SERGAS), 36213 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e9sar","family":"Veiga","sequence":"additional","affiliation":[{"name":"Cardiovascular Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), 36213 Vigo, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1097\/CCM.0b013e3182413bb2","article-title":"Guidelines for intensive care unit design","volume":"40","author":"Thompson","year":"2012","journal-title":"Crit. 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