2021/01/26 by Jie Zhu, Zhu, Jie, Blanca Gallego +1
Computer Science · Mathematics · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Insurance, Mortality, Demography, Risk Management #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2101.10739
openalex publication_date 2021/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the ever-growing complexity of primary health care system, proactive patient failure management is an effective way to enhancing the availability of health care resource. One key enabler is the dynamic prediction of time-to-event outcomes. Conventional explanatory statistical approach lacks the capability of making precise individual level prediction, while the data adaptive binary predictors does not provide nominal survival curves for biologically plausible survival analysis. The purpose of this article is to elucidate that the knowledge of explanatory survival analysis can significantly enhance the current black-box data adaptive prediction models. We apply our recently developed counterfactual dynamic survival model (CDSM) to static and longitudinal observational data and testify that the inflection point of its estimated individual survival curves provides reliable prediction of the patient failure time.