Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics by Maochun Zhang, Qing Zhang, Xueying Wang, Xiaoli Peng, Jiao Chen, Hanfeng Yang

“…Model performances were evaluated via AUC. Plot calibration curves and clinical decision curves were used to assess model efficacy. …”
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Building a machine learning-based risk prediction model for second-trimester miscarriage by Sangsang Qi, Shi Zheng, Mengdan Lu, Aner Chen, Yanbo Chen, Xianhu Fu

“…The imbalanced dataset from the training cohort was rectified by applying the SMOTE oversampling approach. …”
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Development and validation of survival prediction tools in early and late onset colorectal cancer patients by Wanling Li, Jinshan Liu, Yuntong Lan, Dongling Yu, Bingqiang Zhang

“…The models were evaluated using internal and external testing datasets based on AUC, accuracy, precision, recall, and F1 score. …”
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The Improved-EFI Score: A Multi-Omics-Based Novel Efficacy Predictive Tool for Predicting the Natural Fertility of Endometriosis Patients by He Q, Zhang C, Hu Y, Deng J, Zhang S

“…An improved endometriosis fertility index (EFI) predictive model was created based on ultrasound radiomics and urinary proteomics gathered during the patient’s initial admission, using two machine learning algorithms. The predictive model was evaluated for C-index, calibration, and clinical applicability through receiver working characteristic curve, decision curve analysis.Results: The improved EFI prediction model nomogram, based on five ultrasound radiomics parameters and three urine proteomics, had AUC values of 0.921 (95% CI: 0.864– 0.978) and 0.909 (95% CI: 0.852– 0.966) in the training and validation sets, respectively, while the traditional EFI prediction model had AUC values of 0.889 (95% CI: 0.832– 0.946) and 0.873 (95% CI: 0.816– 0.930) in the training and validation sets, respectively. …”
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A risk prediction model for gastric cancer based on endoscopic atrophy classification by Yadi Lan, Weijia Sun, Shen Zhong, Qianqian Xu, Yining Xue, Zhaoyu Liu, Lei Shi, Bing Han, Tianyu Zhai, Mingyue Liu, Yujing Sun, Hongwei Xu

“…However, we chose all the variables to construct the models for other machine learning algorithms. All models were evaluated using the receiver operating characteristic curve (ROC), predictive histograms, and decision curve analysis (DCA). …”
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Enhanced Domain Tuned Yolo-Driven Intelligent Fault Identification Method: Application in Selection and Construction of Gas Storage by BAI Xuefeng, ZHANG Fengyuan, ZOU Huanyu, HUANG Famu, LI Junlun, ZHAO Shijie, ZHANG Li, TANG Jizhou

“…Through the downstream task requirements-directed training and optimization algorithm, the optimal enhancement combination scheme of seismic volume images is achieved. …”
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Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach. by Wei-Hsuan Lo-Ciganic, Julie M Donohue, Eric G Hulsey, Susan Barnes, Yuan Li, Courtney C Kuza, Qingnan Yang, Jeanine Buchanich, James L Huang, Christina Mair, Debbie L Wilson, Walid F Gellad

“…This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. …”
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Application of Multi-Inflammatory Index to Predict Atrial Fibrillation Risk in Patients with Coronary Heart Disease: A Retrospective Machine Learning Study by Hou L, Su K, Zhao J, He T, Li Y

“…Thirteen variables most related to AF occurrence were selected using the Boruta algorithm. The LightGBM model outperformed others, showing the highest accuracy and calibration in both training and test sets. …”
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Establishment and validation of a combined diagnostic model for aldosterone-producing adenoma of the adrenal gland based on CT radiomics and clinical features by ZHANG Mingquan, LIU Jingjing, LIN Xin, FU Min, FENG Ying, CHEN Jingjing

“…The Pearson correlation coefficient and the least absolute shrinkage and selection ope-rator (LASSO) algorithm were used to identify the radiomic features on the plain CT and contrast-enhanced CT images of the adrenal gland, and a CT radiomic model was established. …”
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Radiomic Analysis of Contrast‐Enhanced CT Predicts Recompensation in Hepatitis B‐Related Decompensated Cirrhosis by Qiaofeng Chen, Yuanping Fan, Kaini Wu, Tianpan Cai, Chunyu Lan, Mingju Yu, Yunfeng Fu, Qi Zhu, Jianhao Qiu, Xiaodong Zhou

“…Three machine‐learning algorithms were used to develop radiomic signatures. …”
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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. …”
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Identification of osteoarthritis-associated chondrocyte subpopulations and key gene-regulating drugs based on multi-omics analysis by Ting Hao, Zhiwei Pei, Sile Hu, Zhenqun Zhao, Wanxiong He, Jing Wang, Liuchang Jiang, jirigala Ariben, Lina Wu, Xiaolong Yang, Leipeng Wang, Yonggang Wu, Xiaofeng Chen, Qiang Li, Haobo Yang, Siqin Li, Xing Wang, Mingqi Sun, Baoxin Zhang

“…Additionally, the immune and pathway scores of the training dataset samples were evaluated using the ESTIMATE, MCP-counter, and ssGSEA algorithms to establish the relationship between the hub genes and immune and pathways. …”
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High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE. by Stefano Castellana, Caterina Fusilli, Gianluigi Mazzoccoli, Tommaso Biagini, Daniele Capocefalo, Massimo Carella, Angelo Luigi Vescovi, Tommaso Mazza

“…We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability.…”
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Artificial Intelligence Precision Recognition and Auxiliary Diagnosis of Dental X-ray Panoramic Images Based on Deep Learning by Liu Riming, Gao Zhenshan

“…Methods: Multiple classic medical image segmentation network models (including Unet, PSPNet, FPN, Unet++, and DeepLabV3+) were trained and tested on the ParaDentCaries dataset to evaluate their performance in dental X-ray panoramic images. …”
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Domain general noise reduction for time series signals with Noisereduce by Tim Sainburg, Asaf Zorea

“…We provide a detailed overview of Noisereduce and evaluate its performance on a variety of time-domain signals.…”
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Watershed Health Prediction based on Surface Water Quality Variables (Case Study: Taleghan Watershed) by Payam Ebrahimi, Ali Salajegheh, Mohsen Mohseni Saravi, Arash Malekian, Amir Sadoddin

“…In this research, using the Galinak hydrometric station data and the values of 10 parameters of water quality K, Na, mg, Ca, So4, Cl, Hco3, PH, Ec, TDS in the years (1990-2016), the health status of this area was evaluated using gene expression planning model. Data for years (1990-2006), (2007-2014), (2015-2016) were considered as training, test and error data respectively, and at least one year (from September 2016 until September 2017) as predictive data fitted using an algorithm with R2 0.87 and RMSE 3.003. …”
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Automated Quality Control of Candle Jars via Anomaly Detection Using OCSVM and CNN-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…Both strategies are trained primarily on non-defective samples, with only a limited number of anomalous examples used for evaluation. …”
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Early Warning of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Multi-Omics Signature: A Machine Learning-Based Retrospective Study by Ke Z, Shen L, Shao J

“…Logistic regression (ie generalized linear regression model [GLRM]) and random forest model (RFM) were used to construct an ALN prediction model in the training queue, and the discriminant power of the model was evaluated using area under curve (AUC) and decision curve analysis (DCA). …”
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Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression by Kun Guo, Kun Guo, Bo Zhu, Lei Zha, Yuan Shao, Zhiqin Liu, Naibing Gu, Kongbo Chen

“…Model performance was evaluated using the Area Under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, predictive values, and F1 score, with five-fold cross-validation to ensure robustness.ResultsThe training set, identified key variables associated with stroke prognosis, including hypertension, diabetes, and smoking history. …”
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PRESENTATION OF THE TEXT INFORMATION FOR THE ANALYSIS OF TEXT TONALITY BY ARTIFICIAL NEURAL NETWORK. THE IMPLEMENTATION OF PRIVATE DICTONARY METHOD by Nikolay Ivanovich Chervyakov, Evgenia Igorevna Lifanova

“…This paper proposes a method for isolating and obtaining numerical characteristics of the text for tonality evaluation. The resulting characteristics in vector form can be transmitted to machine learning algorithm, for determining the classification of texts and tonality. …”
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