Predicting Alzheimers Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort
This provides a tool for clinical investigation and risk prediction in Alzheimer's Disease, but it is incremental as it applies an existing method to a specific medical dataset.
The paper tackled predicting Alzheimer's Disease diagnosis risk over time using survival machine learning on the ADNI cohort, achieving a C-Index of 0.86 to forecast deterioration and time to deterioration.
The rise of Alzheimers Disease worldwide has prompted a search for efficient tools which can be used to predict deterioration in cognitive decline leading to dementia. In this paper, we explore the potential of survival machine learning as such a tool for building models capable of predicting not only deterioration but also the likely time to deterioration. We demonstrate good predictive ability (0.86 C-Index), lending support to its use in clinical investigation and prediction of Alzheimers Disease risk.