Time‐to‐event prediction using survival analysis methods for Alzheimer's disease progression. Issue 1 (31st December 2021)

• Record Type: Journal Article
• Authors: Sharma, Rahul; Anand, Harsh; Badr, Youakim; Qiu, Robin G.
• Is Part Of: Alzheimer's & dementia. Volume 7:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-31
• Subjects: Alzheimer's disease -- deep learning -- survival analysis -- time‐to‐event prediction; Dementia -- Periodicals; Dementia -- Treatment -- Periodicals; Alzheimer's disease -- Treatment -- Periodicals; Alzheimer's disease -- Periodicals; 616.831
• DOI: 10.1002/trc2.12229
• Languages: English
• ISSNs: 2352-8737

Abstract

Abstract: Introduction: Many research studies have well investigated Alzheimer's disease (AD) detection and progression. However, the continuous‐time survival prediction of AD is not yet fully explored to support medical practitioners with predictive analytics. In this study, we develop a survival analysis approach to examine interactions between patients' inherent temporal and medical patterns and predict the probability of the AD next stage progression during a time period. The likelihood of reaching the following AD stage is unique to a patient, helping the medical practitioner analyze the patient's condition and provide personalized treatment recommendations ahead of time. Methodologies: We simulate the disease progression based on patient profiles using non‐linear survival methods—non‐linear Cox proportional hazard model (Cox‐PH) and neural multi‐task logistic regression (N‐MTLR). In addition, we evaluate the concordance index (C‐index) and Integrated Brier Score (IBS) to describe the evolution to the next stage of AD. For personalized forecasting of disease, we also developed deep neural network models using the dataset provided by the National Alzheimer's Coordinating Center with their multiple‐visit details between 2005 and 2017. Results: The experiment results show that our N‐MTLR based survival models outperform the CoxPH models, the best of which gives Concordance‐Index of 0.79 and IBS of 0.09. We obtained 50 critical features out of 92 by applying recursive feature elimination and random forest techniques on the clinical data; the top ones include normal cognition and behavior, criteria for dementia, community affairs, etc. Our study demonstrates that selecting critical features can improve the effectiveness of probabilities at each time interval. Conclusions: The proposed deep learning‐based survival method and model can be used by medical practitioners to predict the patients' AD shift efficiently and recommend personalized treatment to mitigate or postpone the effects of AD. More generally, our proposed survival analysis approach for predicting disease stage shift can be used for other progressive diseases such as cancer, Huntington's disease, and scleroderma, just to mention a few, using the corresponding clinical data.