Predictive machine-learning model for screening iron deficiency without anaemia: a retrospective cohort study by Girish N Nadkarni, Orly Efros, Eyal Klang, Shelly Soffer, Gili Kenet, Aya Mudrik, Renana Robinson

“…The primary hypothesis was that an ML model could achieve better accuracy in identifying low ferritin levels (<30 ng/mL) in non-anaemic patients compared with traditional methods.Design A retrospective cohort study.Setting Data were derived from secondary and tertiary care facilities within the eight-hospital Mount Sinai Health System, an urban academic health system.Participants The study included 211 486 adult patients (aged ≥18 years) with normal haemoglobin levels (≥130 g/L for men and ≥120 g/L for women) and recorded ferritin measurements.Primary and secondary outcome measures The primary outcome was the prediction of low ferritin levels (<30 ng/mL) using extreme gradient-boosted decision trees, an ML algorithm suited for structured clinical data. Secondary outcomes included subgroup analyses stratified by sex and age to evaluate model performance in different populations.Data from 211 486 Mount Sinai Health System patients with normal haemoglobin levels and ferritin testing were analysed. …”
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Impact of Medical Conditions and Area Deprivation on Fundraising Success in Online Crowdfunding: Cross-Sectional Study by Steven S Doerstling, Matthew M Engelhard, Dennis Akrobetu, Caroline E Sloan, Ada Campagna, Thuy-Vi Nguyen, Farrah Madanay, Felicia Chen, Peter A Ubel

“…Previous studies have usually focused on a single disease category at a time or a small number of mutually exclusive diseases, even though a given campaign may seek funding for multiple conditions. In addition, differences in fundraising exist according to socioeconomic status, but whether this association applies across different diseases is unclear. …”
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Minimum carbon dioxide is a key predictor of the respiratory health of pigs in climate-controlled housing systems by Eddiemar Baguio Lagua, Hong-Seok Mun, Keiven Mark Bigtasin Ampode, Hae-Rang Park, Md Sharifuzzaman, Md Kamrul Hasan, Young-Hwa Kim, Chul-Ju Yang

“…However, maintaining air quality is a limitation of current housing systems. This study evaluated the growth and health parameters of pigs raised under different environmental conditions and identified key environmental variables that determine respiratory health. …”
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Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach by Yang Chen, Xiaojie Su, Fei Yao, Yushan Liu, Hua Xing, Yubin Ju, Zhiran Kang, Wuquan Sun, Lijun Yao, Li Gong

“…The total reliability of the scale is 0.94 and the KMO (Kaiser-Meyer-Olkin measure of sampling adequacy) value of the scale validity was 0.949 (>0.7). The CatBoost algorithm-based machine-learning model achieved the best predictive performance in distinguishing practitioners with different degrees of knee pain after Tai Chi practice. …”
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Radiomics model building from multiparametric MRI to predict Ki-67 expression in patients with primary central nervous system lymphomas: a multicenter study by Yelong Shen, Siyu Wu, Yanan Wu, Chao Cui, Haiou Li, Shuang Yang, Xuejun Liu, Xingzhi Chen, Chencui Huang, Ximing Wang

“…The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. …”
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Interpersonal counselling for adolescent depression delivered by youth mental health workers without core professional training: the ICALM feasibility RCT by Jon Wilson, Viktoria Cestaro, Eirini Charami-Roupa, Timothy Clarke, Aoife Dunne, Brioney Gee, Sharon Jarrett, Thando Katangwe-Chigamba, Andrew Laphan, Susie McIvor, Richard Meiser-Stedman, Jamie Murdoch, Thomas Rhodes, Carys Seeley, Lee Shepstone, David Turner, Paul Wilkinson

“…Aims The aims of this feasibility study were to (1) assess the feasibility and acceptability of trial procedures, (2) explore the delivery of IPC-A and treatment as usual (TAU) and how and why intervention delivery varies across differing service contexts, (3) evaluate the extent of contamination of the control arm and if it should be mitigated against in a future trial and (4) investigate if the interval estimate of benefit of IPC over TAU in depression scores post treatment includes a clinically significant effect. …”
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Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury by Moa Pontén, Oskar Flygare, Martin Bellander, Moa Karemyr, Jannike Nilbrink, Clara Hellner, Olivia Ojala, Johan Bjureberg

“…The aim of this study was to explore clinician predictions of which adolescents would abstain from nonsuicidal self-injury after treatment as well as how these predictions match machine-learning algorithm predictions. Methods Data from a recent trial evaluating an internet-delivered emotion regulation therapy for adolescents with nonsuicidal self-injury was used. …”
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Research on leaf identification of table grape varieties based on deep learning by PAN Bowen, LIN Meiling, JU Yanlun, SU Baofeng, SUN Lei, FAN Xiucai, ZHANG Ying, ZHANG Yonghui, LIU Chonghuai, JIANG Jianfu, FANG Yulin

“…[Conclusion] The ResNet-101 model had the highest overall recognition accuracy, the lowest LOSS value, the higher average recognition accuracy and Recall rate of varieties, and could get a better model with fewer iterations, which would take less time. The Grad-CAM algorithm was used to evaluate the classification effect of four convolutional neural networks, and all of them could accurately identify the main features of the leaves. …”
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LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds by Veljka Kocic, Niko Lukac, Dzemail Rozajac, Stefan Schweng, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Javier del Ser, Andreas Holzinger

“…The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. …”
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A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer by Sushant Patkar, Stephanie Harmon, Isabell Sesterhenn, Rosina Lis, Maria Merino, Denise Young, G. Thomas Brown, Kimberly M. Greenfield, John D. McGeeney, Sally Elsamanoudi, Shyh-Han Tan, Cara Schafer, Jiji Jiang, Gyorgy Petrovics, Albert Dobi, Francisco J. Rentas, Peter A. Pinto, Gregory T. Chesnut, Peter Choyke, Baris Turkbey, Joel T. Moncur

“…This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. …”
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Toward Digital Twin of Off-Road Vehicles Using Robot Simulation Frameworks by Arianna Rana, Antonio Petitti, Angelo Ugenti, Rocco Galati, Giulio Reina, Annalisa Milella

“…Research presented in this paper deals with the development of the digital version of off-road vehicles. Two different robot simulation frameworks are investigated. …”
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Nitrogen content estimation of apple trees based on simulated satellite remote sensing data by Meixuan Li, Xicun Zhu, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…The support vector machine model constructed based on Sentinel-2 satellite simulated data was the optimal nitrogen content inversion model, with an average R² value of 0.81 and an average RMSE value of 0.15 for training sets across different phenological periods, and an average R² value of 0.61 and an average RMSE value of 0.23 for validation sets.DiscussionThis study systematically evaluated the applicability and accuracy differences of multi-source satellite data for estimating nitrogen content in apple trees, and clarified the variation patterns of nitrogen-sensitive spectral bands and optimal modeling strategies across key phenological stages. …”
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Epidemiology and clinical features of psoriasis in hard-to-treat body locations: a Chinese nationwide population-based study by Lingyi Lu, Lu Cao, Fan Jiang, Sihan Wang, Yingzhe Yu, Hua Huang, Bingjiang Lin

“…These findings support incorporating hard-to-treat area evaluation into psoriasis severity assessments and treatment algorithms.…”
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Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China by Ruizhe Yang, Wei Li, Qing Niu, WenTao Yang, Wei Gu, Xu Wang

“…Additionally, nine machine learning (ML) algorithms were utilized to construct prediction models. …”
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Identification of Postural Deviations and Trunk Asymmetries in Physical Therapy Students Using an AI-Based Mobile Application by Liviu Moraru, Adela Badau, Florin Marin Litoi, Andrei Madalin Petrescu, Catalin Octavian Manescu, Neluta Smidu

“…The study included 49 students, specializing in physical therapy, aged between 21-24 years. The evaluation was carried out with the mobile application AI Posture Evaluation and Correction System (APECS), which uses markers placed on the body photograph and photogrammetric algorithms for precise scanning of body symmetry. …”
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Supporting self-management with an internet intervention for low back pain in primary care: a RCT (SupportBack 2) by Adam W A Geraghty, Taeko Becque, Lisa C Roberts, Jonathan Hill, Nadine E Foster, Lucy Yardley, Beth Stuart, David A Turner, Gareth Griffiths, Frances Webley, Lorraine Durcan, Alannah Morgan, Stephanie Hughes, Sarah Bathers, Stephanie Butler-Walley, Simon Wathall, Gemma Mansell, Malcolm White, Firoza Davies, Paul Little

“…These differences were not statistically significant at the level of 0.025. …”
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Adaptive Binaural Cue-Based Amplitude Panning in Irregular Loudspeaker Configurations by Shang Zhao, Yunjia Zhou, Zhibin Lin

“…This paper presents an adaptive binaural cue-based amplitude panning algorithm based on binaural cues tailored for accurate azimuthal sound image localization in irregular loudspeaker configurations. …”
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Analysis of New RBF-FD Weights, Calculated Based on Inverse Quadratic Functions by Asghar Rahimi, C.A.Elyas Shivanian, Saeid Abbasbandy

“…We use an efficient algorithm to find the best value of the RBF parameter for the problem domain. …”
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Fault‐tolerant quantum implementation of conventional decoder logic with enable input by Laxmidhar Biswal, Bappaditya Mondal, Hafizur Rahaman

“…On the other hand, the conventional logic circuit is no more useful in quantum computing due to much difference from quantum logic. However, quantum computer has to perform classical tasks which can be addressed by translating to its equivalent quantum algorithm. …”
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INTEGRATION OF SVM AND SMOTE-NC FOR CLASSIFICATION OF HEART FAILURE PATIENTS by Dina Tri Utari

“…SMOTE (Synthetic Minority Over-sampling Technique) and SMOTE-NC (SMOTE for Nominal and Continuous features) are variations of the original SMOTE algorithm designed to handle imbalanced datasets with continuous and nominal features. …”
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