A novel method for predicting the progression rate of ALS disease based on automatic generation of probabilistic causal chains

• Published in: Artificial intelligence in medicine 2020-07, Vol.107, p.101879-101879, Article 101879
• Main Authors: Ahangaran, M., Jahed-Motlagh, M.R., Minaei-Bidgoli, B.
• Format: Article
• Language: English
• Subjects: Amyotrophic Lateral Sclerosis; Causal chain; Entropy; Prediction; Temporal dataset
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2020.101879

[Display omitted] •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. Causal discovery is considered as a major concept in biomedical informatics contributing to diagnosis, therapy, and prognosis of diseases. Probabilistic causality approaches in epidemiology and medicine is a common method for finding relationships between pathogen and disease, environment and disease, and adverse events and drugs. Bayesian Network (BN) is one of the common approaches for probabilistic causality, which is widely used in health-care and biomedical science. Since in many biomedical applications we deal with temporal dataset, the temporal extension of BNs called Dynamic Bayesian network (DBN) is used for such applications. DBNs define probabilistic relationships between parameters in consecutive time points in the form of a graph and have been successfully used in many biomedical applications. In this paper, a novel method was introduced for finding probabilistic causal chains from a temporal dataset with the help of entropy and causal tendency measures. In this method, first, Causal Features Dependency (CFD) matrix is created on the basis of parameters changes in consecutive events of a phenomenon, and then the probabilistic causal graph is constructed from this matrix based on entropy criteria. At the next step, a set of probabilistic causal chains of the corresponding causal graph is constructed by a novel polynomial-time heuristic. Finally, the causal chains are used for predicting the future trend of the phenomenon. The proposed model was applied to the Pooled Resource Open-Access Clinical Trials (PRO-ACT) dataset related to Amyotrophic Lateral Sclerosis (ALS) disease, in order to predict the progression rate of this disease. The results of comparison with Bayesian tree, random forest, support vector regression, linear regression, and multivariate regression show that the proposed algorithm can compete with these methods and in some cases outperforms other algorithms. This study revealed that probabilistic