Aiding Remote Diagnosis with Text Mining

Along with the increase of digital healthcare providers, the interest in diagnostic aids for remote diagnosis has increased as well. As patients write about their symptoms themselves, we have access to a type of data which previously was rarely recorded, and which has not been filtered by a healthcare professional. Knowledge of similar patients and similar symptoms is beneficial for doctors to arrive at a diagnosis. Therefore, the remote diagnostic process could be aided by presenting patient cases together with information about similar patients and their self-reported symptom descriptions. Apart from online diagnosis, such an aid could be beneficial in many healthcare settings, such as long-distance visits and knowledge gain from patient diaries. In this paper, we present the impact of aiding remote diagnosis by presenting clusters of similar symptoms, using symptom descriptions collected from a virtual visit application by the Swedish telemedicine provider KRY. Symptom descriptions were represented using the bag-of-words model and were then clustered using the k-means algorithm. An experiment was then conducted with 13 doctors, where patient cases were presented together with the most representative words of the associated cluster, to measure how their work was impacted. Results indicated that it was useful in more complicated cases, but also that future experiments will require further instructions on how the information is to be interpreted.