{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T10:01:52Z","timestamp":1789639312922,"version":"4.0.0"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Geriatrics"],"abstract":"<jats:p>Background: Hospitalizations among patients with Alzheimer\u2019s disease (AD) carry substantial mortality risk, but length of stay (LOS) is time-dependent and may reflect heterogeneous inpatient trajectories. We examined unadjusted and adjusted LOS\u2013mortality patterns and compared admission-only versus inpatient-course prediction using explainable machine learning. Methods: Using the full 2017 Nationwide Readmissions Database (NRD), we identified hospitalizations among adults aged \u226560 years with an ICD-10-CM G30.x AD code in any diagnosis position. Records with missing in-hospital mortality status were excluded. LOS was summarized in clinically interpretable bins and modeled using restricted cubic splines. Model A excluded explicit inpatient-course measures, whereas Model B added LOS, procedure count, and total charges. Performance was evaluated using patient-grouped 5-fold out-of-fold validation and summarized by AUROC and AUPRC; SHAP was used for interpretation. Results: Among 249,507 AD hospitalizations, 12,666 in-hospital deaths occurred (5.08%; weighted mortality 4.97%). Unadjusted mortality was highest at LOS 0\u20131 day (13.00%), lowest at 4\u20136 days (3.47%), and increased to 7.77% at \u226522 days. After multivariable adjustment, LOS remained strongly nonlinear, but adjusted predicted mortality declined across the modeled LOS range. Model A achieved AUROC\/AUPRC of 0.780\/0.180, whereas Model B improved to 0.828\/0.329. Sepsis, diagnostic burden, acute kidney injury, age, stroke, and pneumonia were stable predictors; LOS and procedure burden added prognostic information in Model B. Conclusions: The crude LOS\u2013mortality pattern was U-shaped, whereas the adjusted pattern suggests that the late-stay increase in unadjusted mortality is partly explained by patient complexity and evolving inpatient-course factors. Admission-only prediction provides meaningful early risk stratification, while inpatient-course information improves prognostic assessment as hospitalization evolves.<\/jats:p>","DOI":"10.3390\/geriatrics11050136","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T08:03:17Z","timestamp":1789632197000},"page":"136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Nonlinear Association of Length of Stay with In-Hospital Mortality in Alzheimer\u2019s Disease Hospitalizations: Admission-Only vs. Inpatient-Course Prediction Using Explainable Machine Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Tursun","family":"Alkam","sequence":"first","affiliation":[{"name":"Program of Applied Artificial Intelligence, University of San Diego, San Diego, CA 92110, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Ebrahim","family":"Tarshizi","sequence":"additional","affiliation":[{"name":"Program of Applied Artificial Intelligence, University of San Diego, San Diego, CA 92110, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Andrew H.","family":"Van Benschoten","sequence":"additional","affiliation":[{"name":"Program of Applied Artificial Intelligence, University of San Diego, San Diego, CA 92110, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1080\/13607863.2018.1468411","article-title":"Association between polypharmacy and dementia-A systematic review and metaanalysis","volume":"23","author":"Leelakanok","year":"2019","journal-title":"Aging Ment. 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