Detecting and Explaining Postpartum Depression in Real-Time with Generative Artificial Intelligence by Silvia García-Méndez, Francisco de Arriba-Pérez

“…Moreover, it addresses the black box problem since the predictions are described to the end users thanks to the combination of LLMS with interpretable ML models (i.e. tree-based algorithms) using feature importance and natural language. …”
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Will Artificial Intelligence Replace Physicians or Augment Their Capabilities? by Sara Rahmati Roodsari, Alireza Zali, Mohammad Rahmati-Roodsari, Behina Forouzanmehr

“…In medical imaging, deep learning algorithms, especially those trained with genetic algorithms, have demonstrated immense promise to improve the accuracy of pneumonia and COVID-19 diagnosis from chest X-rays. …”
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The Future of Minimally Invasive GI and Capsule Diagnostics (REFLECT), October 2024 by Lea Østergaard Hansen, Alexandra Agache, Anastasios Koulaouzidis

“…The symposium also highlighted the significance of predictive models for patient selection and developments in panenteric CE. …”
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Plasma metabolite biomarker identification study for the early detection of gastric cancer by Juan Zhu, Yida Huang, Bin Liu, Xue Li, Li Yuan, Le Wang, Kun Qian, Yingying Mao, Lingbin Du, Xiangdong Cheng

“…Ultra-performance liquid chromatography–mass spectrometry–based metabolomics methods were used to characterize the subjects’ plasma metabolic profiles and to screen and validate the GC biomarkers. Five machine learning algorithms (neural network, support vector machine, ridge regression, lasso regression and Naïve Bayes) were used to build a diagnostic model. …”
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Recent Trends and Advances in Utilizing Digital Image Processing for Crop Nitrogen Management by Bhashitha Konara, Manokararajah Krishnapillai, Lakshman Galagedara

“…A total of 95 articles remained after the screening and selection process. Interest in integrating machine learning and deep learning algorithms with DIP has increased, with the frequently used algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boost, and Convolutional Neural Networks—achieving higher prediction accuracy levels. …”
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Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review by Suli Li, Yihang Chu, Ying Wang, Yantong Wang, Shipeng Hu, Xiangye Wu, Xinwei Qi

“…This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. …”
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Optimized Allocation of Flood Control Emergency Materials Based on Loss Quantification by Wei Wang, Yunqing Wang, Li Huang, Yue Song

“…The center of gravity method is used to address demand when constructing the quantitative function of out‐of‐stock loss. The NSGA‐II algorithm was selected to generate the results after the method comparison to ultimately determine the Pareto solution of the model. …”
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Machine Learning and Deep Learning Techniques for Prediction and Diagnosis of Leptospirosis: Systematic Literature Review by Suhila Sawesi, Arya Jadhav, Bushra Rashrash

“…MethodsUsing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction model Risk of Bias Assessment Tool (PROBAST) tools, we conducted a comprehensive review of studies applying ML and DL models for leptospirosis detection and prediction, examining algorithm performance, data sources, and validation approaches. …”
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Optimizing the dynamic treatment regime of outpatient rehabilitation in patients with knee osteoarthritis using reinforcement learning by Sijia Liu, Jiawei Luo, Chengqi He

“…Then, based on the key features screened out, a dynamic treatment recommendation system was constructed by using deep reinforcement learning algorithms, including Deep Deterministic Policy Gradien(DDPG), Deep Q-Network(DQN) and Batch-Constrained Q-learning(BCQ). …”
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Role of arachidonic acid metabolism in osteosarcoma prognosis by integrating WGCNA and bioinformatics analysis by Yaling Wang, Peichun HSU, Haiyan Hu, Feng Lin, Xiaokang Wei

“…An AA metabolism predictive model of the five AAMRGs were established by Cox regression and the LASSO algorithm. …”
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Prospective Validation and Usability Evaluation of a Mobile Diagnostic App for Obstructive Sleep Apnea by Pedro Amorim, Daniela Ferreira-Santos, Marta Drummond, Pedro Pereira Rodrigues

“…Current guidelines recommend the development of clinical prediction algorithms in screening prior to PSG. A recent intuitive and user-friendly tool (OSABayes), based on a Bayesian network model using six clinical variables, has been proposed to quantify the probability of OSA. …”
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The signature based on interleukin family and receptors identified IL19 and IL20RA in promoting nephroblastoma progression through STAT3 pathway by Chen Ding, Hongjie Gao, Liting Zhang, Zhiyi Lu, Bowen Zhang, Ding Li, Fengyin Sun

“…A prognostic model was constructed based on five selected IL(R)s using the LASSO Cox regression algorithm. …”
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CNN-Based Medical Ultrasound Image Quality Assessment by Siyuan Zhang, Yifan Wang, Jiayao Jiang, Jingxian Dong, Weiwei Yi, Wenguang Hou

“…At last, some tests are taken to evaluate the IQA models. They show that the CNN-based IQA is feasible and effective.…”
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Advances in the application of machine learning technology in the field of environmental health by ZHENG Yu, LI Cheng, HU Guiping, JIA Guang

“…However, the application of ML technology in the field of environmental health still faces challenges such as data quality, model interpretability, and interdisciplinary cooperation. …”
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A hybrid super learner ensemble for phishing detection on mobile devices by Routhu Srinivasa Rao, Cheemaladinne Kondaiah, Alwyn Roshan Pais, Bumshik Lee

“…Furthermore, many of these techniques are unsuitable for mobile devices, which face additional constraints, such as limited RAM, smaller screen sizes, and lower computational power. To address these limitations, this paper proposes a novel hybrid super learner ensemble model named Phish-Jam, a mobile application specifically designed for phishing detection on mobile devices. …”
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Prediction of EGFR mutations in non-small cell lung cancer: a nomogram based on 18F-FDG PET and thin-section CT radiomics with machine learning by Jianbo Li, Qin Shi, Yi Yang, Jikui Xie, Qiang Xie, Ming Ni, Xuemei Wang, Xuemei Wang

“…After selecting optimal radiomic features, four machine learning algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were used to develop and validate radiomics models. …”
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Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis by Hamed Hajishah, Danial Kazemi, Ehsan Safaee, Mohammad Javad Amini, Maral Peisepar, Mohammad Mahdi Tanhapour, Arian Tavasol

“…In total, 346 machine learning models were evaluated, with the most common algorithms being random forest, logistic regression, and gradient boosting. …”
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Critical review of OCT in clinical practice for the assessment of oral lesions by Fariba Esperouz, Domenico Ciavarella, Mauro Lorusso, Andrea Santarelli, Lorenzo Lo Muzio, Giuseppina Campisi, Lucio Lo Russo

“…This is further complicated by the introduction and increased trend to adopt artificial intelligence (AI) algorithms in imaging evaluation. Machine learning and deep learning algorithms have shown superior diagnostic sensitivity and accuracy compared to clinician judgment. …”
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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

“…In addition, seven key genes, including PDK4, S100A8, HTRA1, CHI3L1, WT1, CDKN1B, and EGF, were screened by machine learning algorithm. Most of these genes exhibited low expression in renal tissue of DN patients and positive correlation with neutrophils, and their expressions were verified in an external dataset cell model. …”
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GPR65 is a novel immune biomarker and regulates the immune microenvironment in lung adenocarcinoma by Hanxu Zhou, Zhi Chen, Shuang Gao, Chaoqun Lian, Junjie Hu, Jin Lu, Lei Zhang

“…We screened differential genes (DEGs) in the immune and stromal components, and then screened modular genes by the WGCNA algorithm, which were intersected with DEGs and incorporated into the LASSO-COX regression model. …”
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