A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach by Yuli Wang, Na Mei, Ziyi Zhou, Yuan Fang, Jiacheng Lin, Fanchen Zhao, Zhihong Fang, Yan Li

“…Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application. …”
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Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting by Tamrat Endebu, Girma Taye, Wakgari Deressa

“…Algorithm performance was compared via the corrected resampled t test (p < 0.05), and decision curve analysis (DCA) was used to assess the model’s clinical utility. …”
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Development of a prediction model for hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke: a retrospective analysis by Yidan Chen, Wendie Lv, Xuhui Liu, Mingmin Yan, Jing Zheng, Dan Yan, Dan Wang, Yulin Yao, Bingxi Liu, Yahui Li, Yue Wan

“…The area under the ROC curve (AUC) of the line graph was 0.885 (95%CI = 0.816 ~ 0.953), and the calibration curve showed that the probability predicted by the line graph was in good agreement with the actual observed values. The ROC curve and decision curve analysis (DCA), which assesses clinical usefulness, showed that the nomogram provided greater net benefit than the three individual predictors. …”
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Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome by Pedro Pons-Suñer, François Signol, Noemi Alvarez, Claudia Sargas, Sara Dorado, Jose Vicente Gil Ortí, Juan A. Delgado Sanchis, Marta Llop, Laura Arnal, Rafael Llobet, Juan-Carlos Perez-Cortes, Rosa Ayala, Eva Barragán

“…Predictions are made at three time points: 90 days, six months, and one year post-diagnosis. …”
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Monitoring patient pathways at a secondary healthcare services through process mining via Fuzzy Miner by Güzin Özdağoğlu, Muhammet Damar, Fatih Safa Erenay, Hale Turhan Damar, Osman Himmetoğlu, Andrew David Pinto

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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

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Machine learning via DARTS-Optimized MobileViT models for pancreatic Cancer diagnosis with graph-based deep learning by Yusuf Alaca

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Using self-generated identification codes to match anonymous longitudinal data in a sexual health study of secondary school students: a cohort study by Edmond Pui Hang Choi, Ellie Bostwick Andres, Heidi Sze Lok Fan, Lai Ming Ho, Alice Wai Chi Fung, Kevin Wing Chung Lau, Neda Hei Tung Ng, Monique Yeung, Janice Mary Johnston

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Presenting a prediction model for HELLP syndrome through data mining by Boshra Farajollahi, Mohammadjavad Sayadi, Mostafa Langarizadeh, Ladan Ajori

“…Results A total of 21 variables were included in this study after the first stage. Among all the ML algorithms, multi-layer perceptron and deep learning performed the best, with an F1 score of more than 99%.In all three evaluation scenarios of 5fold and 10fold cross-validation, the K-nearest neighbors (KNN), random forest (RF), AdaBoost, XGBoost, and logistic regression (LR) had an F1 score of over 0.95, while this value was around 0.90 for support vector machine (SVM), and the lowest values were below 0.90 for decision tree (DT). …”
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Indirect determination of hemoglobin A2 reference intervals in Pakistani infants using data mining by Muhammad Shariq Shaikh, Sibtain Ahmed, Saba Farrukh, Shahnawaz Bayunus

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Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

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A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images by Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza, Jamel Baili, Dina Abdulaziz AlHammadi, Hee-Chan Cho, Byoungchol Chang

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Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests by Min Fang, Chengjie Pan, Xiaoyi Yu, Wenjuan Li, Ben Wang, Huajian Zhou, Zhenying Xu, Genyuan Yang

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Risk factors for tuberculosis treatment outcomes: a statistical learning-based exploration using the SINAN database with incomplete observations by Nguyen Ky Phat, Yoonah Lee, Dinh Hoa Vu, Nguyen Phuoc Long, Seongoh Park

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Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study by Andrew Houston, Sophie Williams, William Ricketts, Charles Gutteridge, Chris Tackaberry, John Conibear

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Deep learning image reconstruction and adaptive statistical iterative reconstruction on coronary artery calcium scoring in high risk population for coronary heart disease by Lijuan Zhu, Xiaomeng Shi, Lusong Tang, Haruhiko Machida, Lili Yang, Meixiang Ma, Ruoshui Ha, Yun Shen, Fang Wang, Dazhi Chen

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Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025 by Kelemua Aschale Yeneakal, Gizaw Hailiye Teferi, Temesgen T. Mihret, Abraham Keffale Mengistu, Sefefe Birhanu Tizie, Maru Meseret Tadele

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Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network by Wenlong Zou, Haipeng Zhao, Ming Ren, Chaoxiong Cui, Guobin Yuan, Boyi Yuan, Zeyu Ji, Chao Wu, Bin Cai, Tingting Yang, Jinjun Zou, Guangzhi Liu

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Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning by Yusuke Kobayashi, Naoki Fujiwara, Yuki Murakami, Shoichi Ishida, Sho Kinguchi, Tatsuya Haze, Kengo Azushima, Akira Fujiwara, Hiromichi Wakui, Masayoshi Sakakura, Kei Terayama, Nobuhito Hirawa, Tetsuo Isozaki, Hiroaki Yasuzaki, Hajime Takase, Yuichiro Yano, Kouichi Tamura

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A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models by Yawei Yang, Junjie Tang, Liping Ma, Feng Wu, Xiaoqing Guan

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