Artificial Intelligence-Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach

• Автори: Marvin Chia-Han Yeh (Автор), Yu-Hsiang Wang (Автор), Hsuan-Chia Yang (Автор), Kuan-Jen Bai (Автор), Hsiao-Han Wang (Автор), Yu-Chuan Jack Li (Автор)
• Формат: Книга
• Опубліковано: JMIR Publications, 2021-08-01T00:00:00Z.
• Предмети: article
• Онлайн доступ: Connect to this object online.
• Notes: 1438-8871; 10.2196/26256
• Keywords: Computer applications to medicine. Medical informatics; R858-859.7; Public aspects of medicine; RA1-1270
• Source: Journal of Medical Internet Research, Vol 23, Iss 8, p e26256 (2021)

BackgroundArtificial intelligence approaches can integrate complex features and can be used to predict a patient's risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. ObjectiveThe aim of this study was to use electronic medical records to prescreen patients who are at risk of developing lung cancer. MethodsWe randomly selected 2 million participants from the Taiwan National Health Insurance Research Database who received care between 1999 and 2013. We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data, and we tested the model prospectively on post-2012 data. An age- and gender-matched subgroup that was 10 times larger than the original lung cancer group was used to assess the predictive power of the electronic medical record. Discrimination (area under the receiver operating characteristic curve [AUC]) and calibration analyses were performed. ResultsThe analysis included 11,617 patients with lung cancer and 1,423,154 control patients. The model achieved AUCs of 0.90 for the overall population and 0.87 in patients ≥55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among people aged ≥55 years with a pre-existing history of lung disease. ConclusionsOur model achieved excellent performance in predicting lung cancer within 1 year and has potential to be deployed for digital patient screening. Convolution neural networks facilitate the effective use of EMRs to identify individuals at high risk for developing lung cancer.