A Survey on Medical Health Records and AI

Abstract

As the population increases, so do the number of patients getting admitted in hospitals. This generates an overwhelming amount of data within the electronic health records (EHRs) that is impossible to manage manually. This is where machine learning concepts come in handy. The ML algorithms for regression-classification have become increasingly popular within the healthcare sector. Especially for cardiovascular diseases (CVD). Estimation states that by 2030, over 23 million people will die from CVD each year. But, it is estimated that 90% of CVD is preventable. The on time recognition and diagnosis of heart failure from the pre-existing medical records is a way to do so. However, the EHRs are not particularly reliable when it comes to the comparison of structured and unstructured data. This is due to the use of colloquial language and possible existence of sparse content. To tackle this issue, the proposed system uses the KTI-RNN model for recognition of unstructured data and removal of sparse content, TF-IWF model to extract the keyword set, the LDA model to extract the topic word set, and finally, the GA-BiRNN model to identify heart failure from extensive medical texts. The GA-BiRNN model is made up of a bidirectional recurrent neural network model and its output layer embedded with global attention mechanism and gating mechanism.