{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T19:02:01Z","timestamp":1785438121488,"version":"3.56.0"},"reference-count":39,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Introduction: This study aims to predict mortality using chest radiographs obtained on the first day of intensive care admission, thereby contributing to better planning of doctors\u2019 treatment strategies and more efficient use of limited resources through early and accurate predictions. Methods: We retrospectively analyzed 510 ICU patients. After data augmentation, a total of 3019 chest radiographs were used for model training and validation, while an independent, non-augmented test set of 100 patients (100 images) was reserved for final evaluation. Seventy-four (74) radiomic features were extracted from the images and analyzed using machine learning algorithms. Model performances were evaluated using the area under the ROC curve (AUC), sensitivity, and specificity metrics. Results: A total of 3019 data samples were included in the study. Through feature selection methods, the initial 74 features were gradually reduced to 10. The Subspace KNN algorithm demonstrated the highest prediction accuracy, achieving AUC 0.88, sensitivity 0.80, and specificity 0.87. Conclusions: Machine learning algorithms such as Subspace KNN and features obtained from PAAC radiographs, such as GLCM Contrast, Kurtosis, Cobb angle, Haralick, Bilateral Infiltrates, Cardiomegaly, Skewness, Unilateral Effusion, Median Intensity, and Intensity Range, are promising tools for mortality prediction in patients hospitalized in the internal medicine intensive care unit. These tools can be integrated into clinical decision support systems to provide benefits in patient management.<\/jats:p>","DOI":"10.3390\/diagnostics15243138","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T14:22:40Z","timestamp":1765376560000},"page":"3138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mortality Prediction from Patient\u2019s First Day PAAC Radiograph in Internal Medicine Intensive Care Unit Using Artificial Intelligence Methods"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1430-0541","authenticated-orcid":false,"given":"Orhan","family":"Gok","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Sakarya 54050, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1399-8044","authenticated-orcid":false,"given":"T\u00fcrker Fedai","family":"Cavus","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Faculty of Engineering, Sakarya University, Sakarya 54050, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7725-707X","authenticated-orcid":false,"given":"Ahmed Cihad","family":"Genc","sequence":"additional","affiliation":[{"name":"Ahmed Cihad Genc Private Clinic, Istanbul 34480, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6768-7973","authenticated-orcid":false,"given":"Selcuk","family":"Yaylaci","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Faculty of Medicine, Sakarya University, Sakarya 54050, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4211-7958","authenticated-orcid":false,"given":"Lacin Tatli","family":"Ayhan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine, Sakarya University, Sakarya 54050, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1177\/10815589251335327","article-title":"Comprehensive Analyses Using Machine Learning Models for Mortality Prediction in the Intensive Care Unit of Internal Medicine","volume":"73","author":"Genc","year":"2025","journal-title":"J. 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