title: Toward better public health reporting using existing off the shelf approaches: A comparison of alternative cancer detection approaches using plaintext medical data and non-dictionary based feature selection.
abstract: OBJECTIVE
  Increased adoption of electronic health records has resulted in increased availability of free text clinical data for secondary use. A variety of approaches to obtain actionable information from unstructured free text data exist. These approaches are resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches. We sought to evaluate the potential to obtain actionable information from free text pathology reports using routinely available tools and approaches that do not depend on dictionary-based approaches.
  
  
  METHODS
  We obtained pathology reports from a large health information exchange and evaluated the capacity to detect cancer cases from these reports using 3 non-dictionary feature selection approaches, 4 feature subset sizes, and 5 clinical decision models: simple logistic regression, naïve bayes, k-nearest neighbor, random forest, and J48 decision tree. The performance of each decision model was evaluated using sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve.
  
  
  RESULTS
  Decision models parameterized using automated, informed, and manual feature selection approaches yielded similar results. Furthermore, non-dictionary classification approaches identified cancer cases present in free text reports with evaluation measures approaching and exceeding 80-90% for most metrics.
  
  
  CONCLUSIONS
  Our methods are feasible and practical approaches for extracting substantial information value from free text medical data, and the results suggest that these methods can perform on par, if not better, than existing dictionary-based approaches. Given that public health agencies are often under-resourced and lack the technical capacity for more complex methodologies, these results represent potentially significant value to the public health field.
authors.affiliation: Indiana University School of Informatics and Computing, Indianapolis, IN, USA. Electronic address: snkasthu@iupui.edu.
authors.forename: Suranga N
authors.initials: SN
authors.lastname: Kasthurirathne
authors.affiliation: Regenstrief Institute, Indianapolis, IN, USA; Indiana University Fairbanks School of Public Health, Indianapolis, IN, USA.
authors.forename: Brian E
authors.initials: BE
authors.lastname: Dixon
authors.affiliation: Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Judy
authors.initials: J
authors.lastname: Gichoya
authors.affiliation: Indiana University Fairbanks School of Public Health, Indianapolis, IN, USA.
authors.forename: Huiping
authors.initials: H
authors.lastname: Xu
authors.affiliation: Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Yuni
authors.initials: Y
authors.lastname: Xia
authors.affiliation: Regenstrief Institute, Indianapolis, IN, USA; Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Burke
authors.initials: B
authors.lastname: Mamlin
authors.affiliation: Regenstrief Institute, Indianapolis, IN, USA; Indiana University School of Medicine, Indianapolis, IN, USA.
authors.forename: Shaun J
authors.initials: SJ
authors.lastname: Grannis
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D003661:Decision Support Techniques; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval; D016015:Logistic Models; D008490:Medical Informatics; D009369:Neoplasms / Q000453:epidemiology*; D011237:Predictive Value of Tests; D011634:Public Health; D012372:ROC Curve; D012680:Sensitivity and Specificity
keywords: Cancer; Data preprocessing; Decision models; Feature selection; Pathology; Public health reporting