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An abundance of information about cancer exists online, but categorizing andextracting useful information from it is difficult. Almost all research withinhealthcare data processing is concerned with formal clinical data, but there isvaluable information in non-clinical data too. The present study combinesmethods within distributed computing, text retrieval, clustering, andclassification into a coherent and computationally efficient system, that canclarify cancer patient trajectories based on non-clinical and freely availableinformation. We produce a fully-functional prototype that can retrieve, clusterand present information about cancer trajectories from non-clinical forumposts. We evaluate three clustering algorithms (MR-DBSCAN, DBSCAN, and HDBSCAN)and compare them in terms of Adjusted Rand Index and total run time as afunction of the number of posts retrieved and the neighborhood radius.Clustering results show that neighborhood radius has the most significantimpact on clustering performance. For small values, the data set is splitaccordingly, but high values produce a large number of possible partitions andsearching for the best partition is hereby time-consuming. With a properestimated radius, MR-DBSCAN can cluster 50000 forum posts in 46.1 seconds,compared to DBSCAN (143.4) and HDBSCAN (282.3). We conduct an interview withthe Danish Cancer Society and present our software prototype. The organizationsees a potential in software that can democratize online information aboutcancer and foresee that such systems will be required in the future.

Computationally Efficient Labeling of Cancer Related Forum Posts by Non-Clinical Text Information Retrieval