An Interpretable and Privacy-Aware Machine Intelligence Framework for Accurate Disease Prediction using Electronic Health Data

The development of EHDs has completely changed healthcare operations through simplification of the process of using patient data to diagnose and provide appropriate treatment. Some success has already been seen within this field of research, although it is challenging to implement MI models in practical applications. The limitations of current work illustrate the necessity of having a unified MI approach which would guarantee the clinical appropriateness, transparency, privacy, and usability of this system. The goal of the research paper under consideration is to propose a three-stage process to design an MI framework for disease prediction that will be understandable and preserve personal data. The first step is to obtain EHD from many different places, some of which are organized and others of which are not. After that, the data are preprocessed, features are extracted, and finally, the data are anonymized. The next step is to train and evaluate different Machine Learning (ML) models, like Convolutional Neural Networks (CNN), Long Short-Term Memories (LSTM), and others. An additional Explainable AI (XAI) layer is introduced into the analysis to help comprehend the findings. The next step of the research, which will happen in Phase 3 of the study, is cross-demographic validation and pilot test of the IPAMI Framework in the clinical environment. This research will bring improvements to the decision-making processes within the healthcare sector and result in the better well-being of patients due to potential application of MI systems in practice.