TY - JOUR AU - Huang, Tracy AU - Ngan, Chun-Kit AU - Cheung, Ting Yin AU - Marcotte, Madelyn AU - Cabrera, Benjamin PY - 2025/3/13 TI - A Hybrid Deep Learning?Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study JO - JMIR Bioinform Biotech SP - e65001 VL - 6 KW - machine learning KW - data driven KW - clinical domain?guided framework KW - survivors of cancer KW - cancer KW - oncology KW - behavioral outcome predictions KW - behavioral study KW - behavioral outcomes KW - feature selection KW - deep learning KW - neural network KW - hybrid KW - prediction KW - predictive modeling KW - patients with cancer KW - deep learning models KW - leukemia KW - computational study KW - computational biology N2 - Background: The number of survivors of cancer is growing, and they often experience negative long-term behavioral outcomes due to cancer treatments. There is a need for better computational methods to handle and predict these outcomes so that physicians and health care providers can implement preventive treatments. Objective: This study aimed to create a new feature selection algorithm to improve the performance of machine learning classifiers to predict negative long-term behavioral outcomes in survivors of cancer. Methods: We devised a hybrid deep learning?based feature selection approach to support early detection of negative long-term behavioral outcomes in survivors of cancer. Within a data-driven, clinical domain?guided framework to select the best set of features among cancer treatments, chronic health conditions, and socioenvironmental factors, we developed a 2-stage feature selection algorithm, that is, a multimetric, majority-voting filter and a deep dropout neural network, to dynamically and automatically select the best set of features for each behavioral outcome. We also conducted an experimental case study on existing study data with 102 survivors of acute lymphoblastic leukemia (aged 15-39 years at evaluation and >5 years postcancer diagnosis) who were treated in a public hospital in Hong Kong. Finally, we designed and implemented radial charts to illustrate the significance of the selected features on each behavioral outcome to support clinical professionals? future treatment and diagnoses. Results: In this pilot study, we demonstrated that our approach outperforms the traditional statistical and computation methods, including linear and nonlinear feature selectors, for the addressed top-priority behavioral outcomes. Our approach holistically has higher F1, precision, and recall scores compared to existing feature selection methods. The models in this study select several significant clinical and socioenvironmental variables as risk factors associated with the development of behavioral problems in young survivors of acute lymphoblastic leukemia. Conclusions: Our novel feature selection algorithm has the potential to improve machine learning classifiers? capability to predict adverse long-term behavioral outcomes in survivors of cancer. UR - https://bioinform.jmir.org/2025/1/e65001 UR - http://dx.doi.org/10.2196/65001 UR - http://www.ncbi.nlm.nih.gov/pubmed/40080820 ID - info:doi/10.2196/65001 ER - TY - JOUR AU - Ji, Lu AU - Yao, Yifan AU - Yu, Dandan AU - Chen, Wen AU - Yin, Shanshan AU - Fu, Yun AU - Tang, Shangfeng AU - Yao, Lan PY - 2024/11/20 TI - Performance of a Full-Coverage Cervical Cancer Screening Program Using on an Artificial Intelligence? and Cloud-Based Diagnostic System: Observational Study of an Ultralarge Population JO - J Med Internet Res SP - e51477 VL - 26 KW - full coverage KW - cervical cancer screening KW - artificial intelligence KW - primary health institutions KW - accessibility KW - efficiency N2 - Background: The World Health Organization has set a global strategy to eliminate cervical cancer, emphasizing the need for cervical cancer screening coverage to reach 70%. In response, China has developed an action plan to accelerate the elimination of cervical cancer, with Hubei province implementing China?s first provincial full-coverage screening program using an artificial intelligence (AI) and cloud-based diagnostic system. Objective: This study aimed to evaluate the performance of AI technology in this full-coverage screening program. The evaluation indicators included accessibility, screening efficiency, diagnostic quality, and program cost. Methods: Characteristics of 1,704,461 individuals screened from July 2022 to January 2023 were used to analyze accessibility and AI screening efficiency. A random sample of 220 individuals was used for external diagnostic quality control. The costs of different participating screening institutions were assessed. Results: Cervical cancer screening services were extended to all administrative districts, especially in rural areas. Rural women had the highest participation rate at 67.54% (1,147,839/1,699,591). Approximately 1.7 million individuals were screened, achieving a cumulative coverage of 13.45% in about 6 months. Full-coverage programs could be achieved by AI technology in approximately 1 year, which was 87.5 times more efficient than the manual reading of slides. The sample compliance rate was as high as 99.1%, and compliance rates for positive, negative, and pathology biopsy reviews exceeded 96%. The cost of this program was CN ¥49 (the average exchange rate in 2022 is as follows: US $1=CN ¥6.7261) per person, with the primary screening institution and the third-party testing institute receiving CN ¥19 and ¥27, respectively. Conclusions: AI-assisted diagnosis has proven to be accessible, efficient, reliable, and low cost, which could support the implementation of full-coverage screening programs, especially in areas with insufficient health resources. AI technology served as a crucial tool for rapidly and effectively increasing screening coverage, which would accelerate the achievement of the World Health Organization?s goals of eliminating cervical cancer. UR - https://www.jmir.org/2024/1/e51477 UR - http://dx.doi.org/10.2196/51477 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/51477 ER - TY - JOUR AU - Chow, L. James C. AU - Li, Kay PY - 2024/11/6 TI - Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots Through Large Language Models JO - JMIR Bioinform Biotech SP - e64406 VL - 5 KW - artificial intelligence KW - humanistic AI KW - ethical AI KW - human-centered AI KW - machine learning KW - large language models KW - natural language processing KW - oncology chatbot KW - transformer-based model KW - ChatGPT KW - health care UR - https://bioinform.jmir.org/2024/1/e64406 UR - http://dx.doi.org/10.2196/64406 UR - http://www.ncbi.nlm.nih.gov/pubmed/39321336 ID - info:doi/10.2196/64406 ER -