title: PubMed-supported clinical term weighting approach for improving inter-patient similarity measure in diagnosis prediction.
abstract: BACKGROUND
  Similarity-based retrieval of Electronic Health Records (EHRs) from large clinical information systems provides physicians the evidence support in making diagnoses or referring examinations for the suspected cases. Clinical Terms in EHRs represent high-level conceptual information and the similarity measure established based on these terms reflects the chance of inter-patient disease co-occurrence. The assumption that clinical terms are equally relevant to a disease is unrealistic, reducing the prediction accuracy. Here we propose a term weighting approach supported by PubMed search engine to address this issue.
  
  
  METHODS
  We collected and studied 112 abdominal computed tomography imaging examination reports from four hospitals in Hong Kong. Clinical terms, which are the image findings related to hepatocellular carcinoma (HCC), were extracted from the reports. Through two systematic PubMed search methods, the generic and specific term weightings were established by estimating the conditional probabilities of clinical terms given HCC. Each report was characterized by an ontological feature vector and there were totally 6216 vector pairs. We optimized the modified direction cosine (mDC) with respect to a regularization constant embedded into the feature vector. Equal, generic and specific term weighting approaches were applied to measure the similarity of each pair and their performances for predicting inter-patient co-occurrence of HCC diagnoses were compared by using Receiver Operating Characteristics (ROC) analysis.
  
  
  RESULTS
  The Areas under the curves (AUROCs) of similarity scores based on equal, generic and specific term weighting approaches were 0.735, 0.728 and 0.743 respectively (p < 0.01). In comparison with equal term weighting, the performance was significantly improved by specific term weighting (p < 0.01) but not by generic term weighting. The clinical terms "Dysplastic nodule", "nodule of liver" and "equal density (isodense) lesion" were found the top three image findings associated with HCC in PubMed.
  
  
  CONCLUSIONS
  Our findings suggest that the optimized similarity measure with specific term weighting to EHRs can improve significantly the accuracy for predicting the inter-patient co-occurrence of diagnosis when compared with equal and generic term weighting approaches.
authors.affiliation: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. wing.chi.chan@polyu.edu.hk.
authors.forename: Lawrence Wc
authors.initials: LW
authors.lastname: Chan
authors.affiliation: Institute of Mechanical and Manufacturing Engineering, School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK.
authors.forename: Ying
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Diagnostic Radiology, University of Hong Kong, Pokfulam, Hong Kong.
authors.forename: Tao
authors.initials: T
authors.lastname: Chan
authors.affiliation: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
authors.forename: Helen Kw
authors.initials: HK
authors.lastname: Law
authors.affiliation: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
authors.forename: S C Cesar
authors.initials: SCC
authors.lastname: Wong
authors.affiliation: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
authors.forename: Andy Ph
authors.initials: AP
authors.lastname: Yeung
authors.affiliation: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
authors.forename: K F
authors.initials: KF
authors.lastname: Lo
authors.affiliation: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
authors.forename: S W
authors.initials: SW
authors.lastname: Yeung
authors.affiliation: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
authors.forename: K Y
authors.initials: KY
authors.lastname: Kwok
authors.affiliation: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
authors.forename: William Yl
authors.initials: WY
authors.lastname: Chan
authors.affiliation: Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.
authors.forename: Thomas Yh
authors.initials: TY
authors.lastname: Lau
authors.affiliation: Informatics Institute and Department of Computer Science, University of Missouri, Columbia, MO, USA.
authors.forename: Chi-Ren
authors.initials: CR
authors.lastname: Shyu
mesh_terms: D003933:Diagnosis; D057286:Electronic Health Records; D006723:Hong Kong; D006801:Humans; D008491:Medical Informatics Applications; D039781:PubMed; D009626:Terminology as Topic