A Medical Decision Support Tool Using Text-mining Techniques with Electronic Medical Records

Free-text clinical notes represent a vast amount of information which in the past has been un-analyzed data. In this paper we apply text-mining methods on the free-text in electronic medical records (EMRs) to define treatment options for patients with lower back pain. The goal of the project is to develop a generalized text-mining framework that can be used not only in the treatment of lower back pain, but any medical condition. The framework takes advantage of open-source algorithms for anonymization and the clinical NLP tool Apache Clinical Text Analysis and Knowledge Extraction System (cTAKES) to form structured data from clinical notes. The machine learning algorithm uses seven years of extracted clinical notes from the primary care physician to classify 20 patients’ pattern of back pain. With the small dataset provided, the algorithm managed to achieve diagnosis accuracy of up to 100%. The twenty-patient dataset was simply too homogenous and small to make statistical claims for sensitivity and specificity. However, the system shows indicators of satisfactory performance, and we are trying to extract more data of patients who do not have back pain to be able to validate our system better.