ABSTRACT
Early detection of lung cancer can improve effectiveness of treatment and raises the patient's chances of survival. This paper reviews a comprehensive survey on numerous learning methods to classify lung malignancies using CT images. The widespread use of electronic health records presents an opportunity to hasten cohort-related epidemiological studies utilizing informatics techniques because lung cancer is the second most frequent malignancy in both men and women. Natural language processing (NLP), an artificial intelligence approach, has been used in certain attempts to automatically extract information from text for lung cancer patients because manual extraction from vast volumes of text materials is labor- and time-intensive. In this study, we evaluated a natural language processing system to automatically extract information on these variables for the same patients from clinical narratives including clinical notes, pathology reports, and surgery reports. We used an existing cohort of 1,303 lung cancer studies.
