Learning Diagnosis from Electronic Health Records

Abstract

In the attempt to build a complete solution for a medical assistive decision support system we proposed a complex flow that integrates a sequence of modules which target the different data engineering tasks. This solution can analyse any type of unstructured medical documents which are processed by applying specific NLP steps followed by semantic analysis which leads to the medical concepts identification, thus imposing a structure on the input documents. The data collection, document pre-processing, concept extraction, and correlation are modules that have been researched by us in our previous works and for which we proposed original solutions. Using the collected and structured representation of the medical records, informed decisions regarding the health status of the patients can be made. The current paper focuses on the prediction module that joins all the components in a logical flow and is completed with the suggested diagnosis classification for the patient. The accuracy rate of 81.25%, obtained on the medical documents supports the strength of our proposed strategy.