Clinical Case-based Retrieval Using Latent Topic Analysis

Abstract

Clinical reporting is often performed with minimal consideration for secondary computational analysis of concepts. This fact makes the comparison of patients challenging as records lack a representation in a space where their similarity may be judged quantitatively. We present a method by which the entirety of a patient's clinical records may be compared using latent topics. To capture topics at a clinically relevant level, patient reports are partitioned based on their type, allowing for a more granular characterization of topics. The resulting probabilistic patient topic representations are directly comparable to one another using distance measures. To navigate a collection of patient records we have developed a workstation that allows users to weight different report types and displays succinct summarizations of why two patients are deemed similar, tailoring and expediting searches. Results show the system is able to capture clinically significant topics that can be used for case-based retrieval.

Figure 1.

Graphical model of latent Dirichlet allocation (LDA). Boxes denote replication.

Figure 2.

Test set log likelihood for LDA models.

Figure 3.

Topic case-based retrieval workstation. (a) Adjustable weights for reporting domains. (b) Query patient information. (c) Highlighted reports and topics for query patient. (d) Clickable shared topics between patients. (e) Highlighted result reports and topics. (f) Most similar patients listed in descending order for selected query patient.