Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm

ALS is a neurodegenerative disease where factors such as disease progression rate and pattern vary greatly among patients. Since patient functionality deteriorates over time, we model ALS temporally to mimic the physician's reasoning by incorporating old with new information using a long-short term memory (LSTM) network. We demonstrate that the LSTM achieves a higher accuracy than a random forest in disease state prediction, and improves accuracy with data from additional clinic visits. Being an anytime predictor, our model can help physicians and caregivers to adjust patients' treatment and living environment along the disease period, improving patients' life quality.