title: Facilitating surveillance of pulmonary invasive mold diseases in patients with haematological malignancies by screening computed tomography reports using natural language processing.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Infectious Diseases Unit, Alfred Health, Melbourne, Victoria, Australia.
authors.forename: Michelle R
authors.initials: MR
authors.lastname: Ananda-Rajah
authors.affiliation: Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Martinez
authors.affiliation: Victorian Infectious Diseases Service, Peter Doherty Centre, Melbourne, Victoria, Australia; Infectious diseases department, Peter MacCallum Cancer Institute, Melbourne, Victoria, Australia.
authors.forename: Monica A
authors.initials: MA
authors.lastname: Slavin
authors.affiliation: School of Computer Science and IT, RMIT University, Melbourne, Victoria, Australia.
authors.forename: Lawrence
authors.initials: L
authors.lastname: Cavedon
authors.affiliation: Pharmacy Department, Alfred Health, Melbourne, Victoria, Australia; Faculty of Pharmacy & Pharmaceutical Science, Monash University, Melbourne, Victoria, Australia.
authors.forename: Michael
authors.initials: M
authors.lastname: Dooley
authors.affiliation: Infectious Diseases Unit, Alfred Health, Melbourne, Victoria, Australia; Department of Epidemiology and Preventative Medicine, Monash University, Melbourne, Victoria, Australia.
authors.forename: Allen
authors.initials: A
authors.lastname: Cheng
authors.affiliation: Victorian Infectious Diseases Service, Peter Doherty Centre, Melbourne, Victoria, Australia; Infectious diseases department, Peter MacCallum Cancer Institute, Melbourne, Victoria, Australia.
authors.forename: Karin A
authors.initials: KA
authors.lastname: Thursky
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016022:Case-Control Studies; D005260:Female; D019337:Hematologic Neoplasms / Q000150:complications*; D006801:Humans; D008171:Lung Diseases / Q000150:complications / Q000175:diagnosis* / Q000382:microbiology; D008297:Male; D008875:Middle Aged; D009181:Mycoses / Q000150:complications / Q000175:diagnosis* / Q000382:microbiology; D009323:Natural Language Processing; D011159:Population Surveillance; D012372:ROC Curve; D012189:Retrospective Studies; D014057:Tomography, X-Ray Computed / Q000379:methods*; D055815:Young Adult