Abstract
PURPOSE: Prospective surveillance of invasive mold diseases (IMDs) in haematology patients should be standard of care but is hampered by the absence of a reliable laboratory prompt and the difficulty of manual surveillance. We used a high throughput technology, natural language processing (NLP), to develop a classifier based on machine learning techniques to screen computed tomography (CT) reports supportive for IMDs. PATIENTS AND METHODS: We conducted a retrospective case-control study of CT reports from the clinical encounter and up to 12-weeks after, from a random subset of 79 of 270 case patients with 33 probable/proven IMDs by international definitions, and 68 of 257 uninfected-control patients identified from 3 tertiary haematology centres. The classifier was trained and tested on a reference standard of 449 physician annotated reports including a development subset (n = 366), from a total of 1880 reports, using 10-fold cross validation, comparing binary and probabilistic predictions to the reference standard to generate sensitivity, specificity and area under the receiver-operating-curve (ROC). RESULTS: For the development subset, sensitivity/specificity was 91% (95%CI 86% to 94%)/79% (95%CI 71% to 84%) and ROC area was 0.92 (95%CI 89% to 94%). Of 25 (5.6%) missed notifications, only 4 (0.9%) reports were regarded as clinically significant. CONCLUSION: CT reports are a readily available and timely resource that may be exploited by NLP to facilitate continuous prospective IMD surveillance with translational benefits beyond surveillance alone.