A novel method for predicting the progression rate of ALS disease based on automatic generation of probabilistic causal chains
作者：
Highlights：
• A novel entropy-based method for discovering probabilistic causal chains from temporal dataset was proposed.
• Information theory-based analysis was used for determining the certainty of causal relationships.
• Most probable causal chains of ALS disease from PRO-ACT temporal dataset was discovered.
• Tracking causal chains from cause to effect is more realistic than tracking in the backward direction from effect to cause.
• The output causal chains were applied for prediction of ALS progression rate on a real set of patients.
摘要
•A novel entropy-based method for discovering probabilistic causal chains from temporal dataset was proposed.•Information theory-based analysis was used for determining the certainty of causal relationships.•Most probable causal chains of ALS disease from PRO-ACT temporal dataset was discovered.•Tracking causal chains from cause to effect is more realistic than tracking in the backward direction from effect to cause.•The output causal chains were applied for prediction of ALS progression rate on a real set of patients.
论文关键词：Causal chain,Amyotrophic Lateral Sclerosis,Entropy,Temporal dataset,Prediction
论文评审过程：Received 30 September 2019, Revised 17 April 2020, Accepted 12 May 2020, Available online 22 May 2020, Version of Record 16 July 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2020.101879
