The impact of knowledge of hospitalization on mortality predictions.
Despite the rapid advancement of machine learning algorithms, the important problem of distinguishing patients based on the likelihood of their mortality remains a challenge. In this paper, we investigated the degree to which the incorporation of the time-varying factor, length of hospitalization co...
| Publicado en: | Health Services & Outcomes Research Methodology Vol. 26; no. 2; pp. 204 - 225 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193495166&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193495166 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13873741 OG0 jtl: Health Services & Outcomes Research Methodology issn: 13873741 maglogo: N pubinfo: dt: Jun2026 vid: 26 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 193495166 185898265 193495166 193495166 10.1007/s10742-025-00348-7 193495166 ppf: 204 ppct: 21 formats: tig: atl: The impact of knowledge of hospitalization on mortality predictions. aug: au: Qin, Chuanling Peterson, Curtis Weeks, William B. James O'Malley, A. affil: https://ror.org/049s0rh22 Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, 03756, Lebanon, NH, USA sug: subj: Hospitalization Cause of Death Risk Factors Risk Assessment Time Heart Failure Mortality Renal Insufficiency, Chronic Mortality Pulmonary Disease, Chronic Obstructive Mortality Length of Stay Prediction Models Human Male Female Boosting Machine Learning Algorithms Logistic Regression Funding Source Descriptive Statistics Data Analysis Software Confidence Intervals Odds Ratio Age Factors Sex Factors Race Factors Male Female ab: Despite the rapid advancement of machine learning algorithms, the important problem of distinguishing patients based on the likelihood of their mortality remains a challenge. In this paper, we investigated the degree to which the incorporation of the time-varying factor, length of hospitalization could contribute to modeling mortality. A two-part modeling approach was proposed to capture the potential heterogeneity over follow-up time and to evaluate the extent to which allowing a predictor based on a fixed-time event like hospitalization (as a time-varying coefficient) enhanced mortality prediction. A test was then conducted to assess whether the association between hospitalization and mortality diminished with continued survival of a patient. Leveraging logistic regression models and the XGBoost procedure, the findings supported the claim that the baseline hospitalization is a risk factor whose importance diminishes the longer the patient survives. While simulation studies and theoretical considerations indicate that the two-part model provides deeper insight into the evolving dynamics of regression coefficients and enhances the prediction accuracy of the marginal probability of mortality, its application to the empirical data that motivated this research yielded less compelling results, a finding that aligns with previous findings. Factors such as class imbalance and the magnitude of heterogeneous effects can significantly impact the performance of the two-part model in empirical datasets. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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