A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…Six machine learning algorithms were employed to develop a screening model for PCa using the training dataset. …”
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ALGORITHM OF PSYCHOLOGICAL AND PSYCHOTHERAPEUTIC SUPPORT OF PATIENTS WITH CHRONIC CEREBRAL ISCHEMIA by I. V. Khianikiainen, V. A. Mikhailov

“…The objective of the study was to develop the algorithm for identifying psychosocial characteristics of patients with ES CCI and providing them with psychotherapeutic care. …”
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Development of prediction models for screening depression and anxiety using smartphone and wearable-based digital phenotyping: protocol for the Smartphone and Wearable Assessment f... by Sujin Kim, Ah Young Kim, Yu-Bin Shin, Seonmin Kim, Min-Sup Shin, Jinhwa Choi, Kyung Lyun Lee, Jisu Lee, Sangwon Byun, Heon-Jeong Lee, Chul-Hyun Cho

“…The Smartphone and Wearable Assessment for Real-Time Screening of Depression and Anxiety study aims to develop prediction algorithms to identify individuals at risk for depressive and anxiety disorders, as well as those with mild-to-severe levels of either condition or both. …”
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Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI by Mélanie Karlsen, Sonia Khier, David Fabre, David Marchionni, Jérôme Azé, Sandra Bringay, Pascal Poncelet, Elisa Calvier

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Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images by M. Shanmuga Eswari, S. Balamurali, Lakshmana Kumar Ramasamy

“…Objective We developed an optimized decision support system for retinal fundus image-based glaucoma screening. Methods We combined computer vision algorithms with a convolutional network for fundus images and applied a faster region-based convolutional neural network (FRCNN) and artificial algae algorithm with support vector machine (AAASVM) classifiers. …”
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An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders by Xuening Lyu, Rimsa Goperma, Dandan Wang, Chunling Wan, Liang Zhao

“…The core of our methodology involves a novel algorithm featuring an Efficient-Unet based Deep Learning model for the precise segmentation of NSR areas. …”
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A machine learning based prediction model for short term efficacy of nasopharyngeal carcinoma by Qiulu Zhong, Xiangde Li, Qinghua Du, Qianfu Liang, Danjing Luo, Jiaying Wen, Haiying Yue, Wenqi Liu, Xiaodong Zhu, Jian Li

“…Three machine learning algorithms were used to construct predictive models for the short-term efficacy of LANPC. …”
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A proposed algorithm for early autism screening in Polish primary care settings – a pilot study by Patryk Domarecki, Katarzyna Plata-Nazar, Wojciech Nazar

“…Abstract Background The rising rate of autism spectrum disorder (ASD) prevalence worldwide demands new screening algorithms to make the process of diagnosis more effective. …”
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Research on the optimization model of anti-breast cancer candidate drugs based on machine learning by Zhou Dong, Hong Chen, Yuchen Yang, Hairong Hao

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Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple machine learning algorithms by Hongjun Tao, Yang Wen, Rongfang Yu, Yining Xu, Fangliang Yu

“…Multiple machine learning algorithms are applied to construct distinct risk prediction models, with the most effective model selected through comparative analysis. …”
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Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study by Chuang Li, Youbei Lin, Xuyang Xiao, Xinru Guo, Jinrui Fei, Yanyan Lu, Junling Zhao, Lan Zhang

“…This study demonstrates that machine learning models—particularly the RF algorithm—hold substantial promise for predicting kinesiophobia in postoperative lung cancer patients. …”
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Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. …”
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A diagnostic prediction model for anti-neutrophil cytoplasmic antibody associated vasculitis combined with glomerulonephritis based on machine learning algorithm by Wang Jian-mei, Zhu Ge-li, Cao Chen-lin, Peng Qing-quan

“…<italic>EHHADH</italic>， <italic>CCL2</italic>， <italic>FN1</italic>， <italic>IL1B</italic>， <italic>VAV1</italic>， <italic>CXCR4</italic>， <italic>CCL5</italic>， and <italic>CD44</italic>were core genes in the PPI network. The RF algorithm screened out 15 characteristic genes， and the artificial neural network algorithm calculated the weight of each characteristic gene and successfully constructed a diagnostic model. …”
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A diagnostic prediction model for anti-neutrophil cytoplasmic antibody associated vasculitis combined with glomerulonephritis based on machine learning algorithm by Wang Jian-mei, Zhu Ge-li, Cao Chen-lin, Peng Qing-quan

“…EHHADH， CCL2， FN1， IL1B， VAV1， CXCR4， CCL5， and CD44were core genes in the PPI network. The RF algorithm screened out 15 characteristic genes， and the artificial neural network algorithm calculated the weight of each characteristic gene and successfully constructed a diagnostic model. …”
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Construction of a predictive model for cognitive impairment among older adults in Northwest China by Yu Wang, Ni Wang, Yanjie Zhao, Xiaoyan Wang, Yuqin Nie, Liping Ding

“…Model performance was evaluated on the basis of the area under the curve, sensitivity, specificity, accuracy, F1 score, precision, and recall.ResultsA total of 12,332 older adults were recruited and screened with the Mini-Mental State Examination Scale. …”
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Machine learning algorithms as early diagnostic tools for prolonged operative time in patients with fluorescent laparoscopic cholecystectomy: a retrospective cohort study by Chu Wang, Chu Wang, JunYe Wen, ZiYi Su, HanXiang Yu

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Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies. by Cox Lwaka Tamba, Yuan-Li Ni, Yuan-Ming Zhang

“…This method is referred to as ISIS EM-BLASSO algorithm. Monte Carlo simulation studies validated the new method, which has the highest empirical power in QTN detection and the highest accuracy in QTN effect estimation, and it is the fastest, as compared with efficient mixed-model association (EMMA), smoothly clipped absolute deviation (SCAD), fixed and random model circulating probability unification (FarmCPU), and multi-locus random-SNP-effect mixed linear model (mrMLM). …”
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A novel lightweight multi-scale feature fusion segmentation algorithm for real-time cervical lesion screening by Jiahui Yang, Ying Zhang, Wenlong Fan, Jie Wang, Xinhe Zhang, Chunhui Liu, Shuang Liu, Linyan Xue

“…Therefore, a lightweight algorithm segmentation for cervical lesion real-time screening system is urgently needed. …”
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Increasing clinicians’ suspicion of ATTR amyloidosis using a retrospective algorithm by Jessica Ammon, John Alexander, Woodson Petit-Frere, Deya Alkhatib, Aranyak Rawal, Grace Newman, Oguz Akbiligic, Brian Borkowski, John Jefferies, Isaac B. Rhea

“…Abstract Background This study aimed to increase the index of suspicion for transthyretin amyloidosis (ATTR) among cardiologists leading to increased screening for amyloidosis. Methods A retrospective algorithm was created to identify patients at risk for ATTR. …”
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The short video platform recommendation mechanism based on the improved neural network algorithm to the mainstream media by Mengruo Qi

“…Therefore, in order to address the data sparsity and high-dimensional feature extraction, this study proposes a novel short video platform recommendation model. The proposed method utilizes the term frequency inverse document frequency algorithm for text mining, and combines error back propagation neural network for learning to explore the potential connection between users and videos. …”
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