Journal
BMC MEDICAL INFORMATICS AND DECISION MAKING
Volume 19, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s12911-019-0931-8
Keywords
Natural language processing; Lung cancer; Stage; Histology; Tumor grade; Treatments
Categories
Funding
- National Cancer Institute [1U24CA194215-01A1, U01TR02062, NIH-R01-84354, R01-115857]
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Background: Lung cancer is the second most common cancer for men and women; the wide adoption of electronic health records (EHRs) offers a potential to accelerate cohort-related epidemiological studies using informatics approaches. Since manual extraction from large volumes of text materials is time consuming and labor intensive, some efforts have emerged to automatically extract information from text for lung cancer patients using natural language processing (NLP), an artificial intelligence technique. Methods: In this study, using an existing cohort of 2311 lung cancer patients with information about stage, histology, tumor grade, and therapies (chemotherapy, radiotherapy and surgery) manually ascertained, we developed and evaluated an NLP system to extract information on these variables automatically for the same patients from clinical narratives including clinical notes, pathology reports and surgery reports. Results: Evaluation showed promising results with the recalls for stage, histology, tumor grade, and therapies achieving 89, 98, 78, and 100% respectively and the precisions were 70, 88, 90, and 100% respectively. Conclusion: This study demonstrated the feasibility and accuracy of automatically extracting pre-defined information from clinical narratives for lung cancer research.
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