Explainable Artificial Intelligence-Based Diseases Diagnosis From Unstructured Clinical Data and Decision Making Using Blockchain Technologies

Abstract

In the digital era, health information is stored in digital form for easy maintenance, analysis and transfer. The proficiency of manual illness diagnosis and drug prediction in the medical field depends on the expertise availability, and experience of the specialists. In emergency and abnormal situation, the patient’s life completely depends on expert’s availability. Therefore, a different approach is needed to get around the difficulties in managing emergency cases. Artificial intelligence helps to take decisions in an accurate manner but does not provide the details of the decisions. The ability to treat emergency patients entirely depends on the particular hospitals. The clinical data includes numerical results, text prescriptions, scanned images, etc. Therefore, managing unstructured data with care is necessary for making clinical decisions. An explainable artificial intelligence-based disease diagnosis and blockchain-based decision-making system are presented in this work to address these challenges and improve patient care. A natural language processing system analyzes the unstructured data to identify different types of data and explainable AI diagnosis disease with justification and reason for the prediction. An ant colony optimization-based recommender system examines the predicted decision and identifies the specific drug for the disease. The disease decision and drug information are kept in a permissioned blockchain for confirmation. Decisions are validated by more than 50% of the experts present in the permissioned blockchain network, which consists of experts from various regions. As a result, the quickest and most accurate decisions possible are taken to handle emergency situations.