Explainable Machine Learning Model Based on Routine Admission Laboratory Tests for Predicting New-Onset Hypoalbuminemia in Hospitalized Older Patients with Acute Exacerbation of COPD
Li Xiao, Yan Qin, Youli Wen, Xiaocong Guo, Yingqi Wang, Yinghui Wang, Zilin Wang · 2026
Read Explainable Machine Learning Model Based on Routine Admission Laboratory Tests for Predicting New-Onset Hypoalbuminemia in Hospitalized Older Patients with Acute Exacerbation of COPD by Li Xiao, Yan Qin, Youli Wen, Xiaocong Guo, Yingqi Wang, Yinghui Wang, Zilin Wang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13192498.txt.
Description
Li Xiao and co-authors build in 2026 a learning model that warns of new low albumin in older lung patients from routine entry lab tests. Low albumin flags poor nutrition and swelling load, and it ties to worse stays and outcomes. Entry warning tools for this risk were lacking.
Two hospital cohorts cover more than two thousand older patients with normal albumin at entry. One cohort trains and checks the model, while the second tests it cold. The outcome is a new low albumin mark by discharge. The model design stays explainable, so staff can see which lab values drive each warning.
Catalog record
| CREATOR | Li Xiao, Yan Qin, Youli Wen, Xiaocong Guo, Yingqi Wang, Yinghui Wang, Zilin Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |