Computer based extraction of phenoptypic features of human congenital anomalies from the digital literature with natural language processing techniques

Stud Health Technol Inform. 2014:205:570-4.


The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, ,it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies.

MeSH terms

  • Artificial Intelligence
  • Congenital Abnormalities / classification*
  • Congenital Abnormalities / diagnosis*
  • Data Mining / methods*
  • Decision Support Systems, Clinical / organization & administration*
  • Humans
  • Medical Subject Headings*
  • Natural Language Processing*
  • Periodicals as Topic / classification
  • Periodicals as Topic / statistics & numerical data
  • Phenotype
  • PubMed / classification
  • PubMed / statistics & numerical data*