Abstract
Lung tumors act as a severe cancer type across the globe, and it is common among both men and women. With increasing medical and healthcare facilities, the contribution to electronic health records (EHRs) has been increasing for decades. This further provides a bigger opportunity to increase the analytics via epidemiological studies that may adopt some of the intelligent informatics approaches. Among these, the artificial intelligence-supported natural language processing (NLP) is used for the automatic extraction of the data from the HER, that is, text datasets in relation to lung cancer. Further, the extraction of instances from larger datasets or larger volumes is considered to be laborious and reduces time. In this paper, we use deep learning architecture with NLP to extract and predict cancer in lungs from the input datasets. This text mining model enables automated prediction of instances from the input datasets for optimal prediction of cancer. The model is tested in a different environment to test the efficacy and robustness of DL with NLP with different sets of test datasets. The evaluation showed an improved prediction accuracy than the existing methods. The simulation shows that the model is robust for lung cancer research.
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Jabir, K., Thirumurthi Raja, A. (2023). Prediction of Lung Cancer from Electronic Health Records Using CNN Supported NLP. In: Joseph, F.J.J., Balas, V.E., Rajest, S.S., Regin, R. (eds) Computational Intelligence for Clinical Diagnosis. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-23683-9_40
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DOI: https://doi.org/10.1007/978-3-031-23683-9_40
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