A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening by Emre Yalçın, Serpil Aslan, Mesut Toğaçar, Süleyman Cansun Demir

“…<b>Background/Objectives:</b> The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. The proposed method transforms one-dimensional (1D) patient data—including features such as nuchal translucency (NT), human chorionic gonadotropin (hCG), and pregnancy-associated plasma protein A (PAPP-A)—into two-dimensional (2D) Aztec barcode images, enabling advanced feature extraction using transformer-based deep learning models. …”
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Attachment beyond the screen: The influences of demographic factors and parasocial relationships on social media use in Qatar. by Ruining Jin, Tam-Tri Le

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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

“…A classification prediction model for the haemoglobin concentration after kidney transplantation was constructed. …”
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Optimisation Study of Investment Decision-Making in Distribution Networks of New Power Systems—Based on a Three-Level Decision-Making Model by Wanru Zhao, Ziteng Liu, Rui Zhang, Mai Lu, Wenhui Zhao

“…Next, the Pearson correlation coefficient is employed to screen key influencing factors, and in conjunction with the grey MG(1,1) model and the support vector machine algorithm, precise forecasting of the investment scale is achieved. …”
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Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India by Ramachandran Rajalakshmi, Thyparambil Aravindakshan PramodKumar, Ashis Kumar Dhara, Geetha Kumar, Naziya Gulnaaz, Shramana Dey, Sourav Basak, B Uma Shankar, Raka Goswami, Raja Mohammed, Suchetha Manikandan, Sushmita Mitra, Harsimran Thethi, Saravanan Jebarani, Sinnakaruppan Mathavan, Tamilselvi Sarveswaran, Ranjit Mohan Anjana, Viswanathan Mohan, Sambuddha Ghosh, Tushar Kanti Bera, Rajiv Raman

“…Our work is expected to mark a significant stride in DR detection and management, promising a more efficient and scalable solution for tackling this global health challenge.…”
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An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer by Lihuan Dai, Jinxue Yin, Xin Xin, Chun Yao, Yongfang Tang, Xiaohong Xia, Yuanlin Chen, Shuying Lai, Guoliang Lu, Jie Huang, Purong Zhang, Jiansheng Li, Xiangguang Chen, Xi Zhong

“…After feature reduction and selection, 11 ML algorithms were employed to develop predictive models, and their performance in predicting PD-L1 expression status was evaluated using areas under receiver operating characteristic curves (AUCs). …”
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Nomogram model using serum Club cell secretory protein 16 to predict prognosis and acute exacerbation in patients with idiopathic pulmonary fibrosis by Yaqiong Tian, Xuan Zhou, Mi Tian, Lijun Ren, Ruyi Zou, Hanyi Jiang, Miaomiao Xie, Mei Huang, Jingjing Ding, Yin Liu, Jingyu Chen, Min Cao, Hourong Cai

“…COX regression and LASSO algorithm were used to screen featured characteristics. …”
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Single-cell transcriptomics and machine learning unveil ferroptosis features in tumor-associated macrophages: Prognostic model and therapeutic strategies for lung adenocarcinoma by Ting Ji, Ting Ji, Juanli Jiang, Juanli Jiang, Xin Wang, Xin Wang, Kai Yang, Kai Yang, Shaojin Wang, Shaojin Wang, Bin Pan, Bin Pan

“…Using the GeneCards ferroptosis gene set (1515 genes), ferroptosis-related differentially expressed genes in macrophages were screened. Eight machine learning algorithms (LASSO, SVM, XGBoost, etc.) were leveraged to identify prognostic genes and build a Cox regression risk model. …”
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High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks by Haoyi Tan, Yukun Teng, Guangcun Shan

“…In addition to 6 structural features, 25 molecular features (encompassing the types of metal and ligand atoms as well as bonding modes) and 8 chemical features (including heat of adsorption and Henry’s coefficient) were incorporated to enhance the prediction accuracy of the machine learning algorithms. …”
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Developing an HIV-specific falls risk prediction model with a novel clinical index: a systematic review and meta-analysis method by Sam Chidi Ibeneme, Eunice Odoh, Nweke Martins, Georgian Chiaka Ibeneme

“…Abstract Background Falls are a common problem experienced by people living with HIV yet predictive models specific to this population remain underdeveloped. …”
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Student knowledge tracking based multi-indicator exercise recommendation algorithm by Bin ZHUGE, Zhenghu YIN, Wenxue SI, Lei YAN, Ligang DONG, Xian JIANG

“…Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students, insufficient, could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation.Firstly, a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then, students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally, the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module, and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models.…”
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Student knowledge tracking based multi-indicator exercise recommendation algorithm by Bin ZHUGE, Zhenghu YIN, Wenxue SI, Lei YAN, Ligang DONG, Xian JIANG

“…Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students, insufficient, could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation.Firstly, a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then, students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally, the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module, and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models.…”
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Preoperative prediction of recurrence risk factors in operable cervical cancer based on clinical-radiomics features by Xue Du, Xue Du, Chunbao Chen, Lu Yang, Yu Cui, Min Li

“…Logistic regression algorithms were used to construct a fusion clinical-radiomics model to visualize nomograms. …”
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Two-test algorithms for infectious disease diagnosis: Implications for COVID-19. by Sunil Pokharel, Lisa J White, Jilian A Sacks, Camille Escadafal, Amy Toporowski, Sahra Isse Mohammed, Solomon Chane Abera, Kekeletso Kao, Marcela De Melo Freitas, Sabine Dittrich

“…A two-test algorithm comprising a rapid screening test followed by confirmatory laboratory testing can reduce false positive rate, produce rapid results and conserve laboratory resources, but can lead to large number of missed cases in high prevalence setting. …”
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Development of a machine learning prognostic model for early prediction of scrub typhus progression at hospital admission based on clinical and laboratory features by Youguang Lu, Zixu Wang, Junhu Wang, Yingqing Mao, Chuanshen Jiang, Jinpiao Wu, Haizhou Liu, Haiming Yi, Chao Chen, Wei Guo, Liguan Liu, Yong Qi

“…Eighteen objective clinical and laboratory features collected at admission were screened using various feature selection algorithms, and used to construct models based on six machine learning algorithms.Results The model based on Gradient Boosting Decision Tree using 14 features screened by Recursive Feature Elimination was evaluated as the optimal one. …”
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Pulmonary Nodules Detection Algorithm Combining Multi-view and Attention Mechanism by LIU Yu-bo, LIU Guo-zhu, SHI Cao, XU Can-hui

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Panel defect detection algorithm based on improved Faster R-CNN by Chen Wanqin, Tang Qingshan, Huang Tao

“…Experimental results show that the accuracy and recognition rate of the optimized network model have been greatly improved.…”
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DETECTION OF Β THALASSEMIA CARRIERS BY RED CELL PARAMETERS OBTAINED FROM AUTOMATIC COUNTERS USING MATHEMATICAL FORMULAS by Idit Lachover Roth, Boaz Lachover, Guy Koren, Carina Levin, Luci Zalman, Ariel Koren

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Analysis of immune characteristics and inflammatory mechanisms in COPD patients: a multi-layered study combining bulk and single-cell transcriptome analysis and machine learning by Changjin Wei, Yongfeng Zhu, Caiming Chen, Feipeng Li, Li Zheng

“…Inflammatory-related COPD feature genes were selected using Lasso regression and random forest algorithms, and a COPD risk prediction model was constructed. …”
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Socially Responsible Investment Portfolio Construction with a Double-Screening Mechanism considering Machine Learning Prediction by Jun Zhang, Xuedong Chen

“…The proposed models consist of two stages, i.e., stock screening and asset allocation. …”
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