Aiding Remote Diagnosis with Text Mining

Abstract: The topic of this thesis is on how text mining could be used on patient-reported symptom descriptions, and how it could be used to aid doctors in their diagnostic process. Healthcare delivery today is struggling to provide care to remote settings, and costs are increasing together with the aging population. The aid provided to doctors from text mining on patient descriptions is unknown.Investigating if text mining can aid doctors by presenting additional information, based on what patients who write similar things to what their current patient is writing about, could be relevant to many settings in healthcare. It has the potential to improve the quality of care to remote settings and increase the number of patients treated on the limited resources available. In this work, patient texts were represented using the Bag-of-Words model and clustered using the k-means algorithm. The final clustering model used 41 clusters, and the ten most important words for the cluster centroids were used as representative words for the cluster. An experiment was then performed to gauge how the doctors were aided in their diagnostic process when patient texts were paired with these additional words. The results were that the words aided doctors in cases where the patient case was difficult and that the clustering algorithm can be used to provide the current patient with specific follow-up questions.