SECONDGRAM: Self-conditioned diffusion with gradient manipulation for longitudinal MRI imputation by Brandon Theodorou, Anant Dadu, Mike Nalls, Faraz Faghri, Jimeng Sun

“…SECONDGRAM builds on neural diffusion models and introduces two key contributions: self-conditioned learning to leverage much larger, unlinked datasets and gradient manipulation to combat instability and overfitting in a low-data setting. We evaluate SECONDGRAM on the UK Biobank dataset and show that it not only models MRI patterns better than existing baselines but also enhances training datasets to achieve better downstream results over naive approaches. …”
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A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database by Fei Wang, Xihao Wang, Zhigang Feng, Jun Li, Hailiang Xu, Hengming Lu, Lianqu Wang, Zhihui Li

“…Nomogram performance was comprehensively evaluated based on Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA). …”
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Using artificial neural networks to predict indoor particulate matter and TVOC concentration in an office building: Model selection and method development by Saren Gaowa, Zhen Zhang, Jianchun Nie, Linxiao Li, Han A-ru, Zhili Yu

“…In addition, the models’ generalization abilities were further evaluated by using some smaller datasets. For the MLNN model, when predicting indoor PM2.5 concentration, as the amount of training data decreased from 80 % to 20 %, its FB decreased from 0.41 to 0.03, its NMSE changed from 1.53 μg/m3 to 0.53 μg/m3, and its R2 decreased from 0.69 to 0.07. …”
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External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model by Simone Zappalà, PhD, Francesca Alfieri, MS, Andrea Ancona, PhD, Antonio M. Dell’Anna, MD, Kianoush B. Kashani, MD, MS

“…The performance of the PersEA model, a boosted tree algorithm fed by hourly patient data via electronic health records to provide real-time psAKI predictions, was evaluated using specific metrics that penalize late alarms. …”
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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

“…The effectiveness of these frameworks plays a crucial role in determining data processing speed, model training efficiency and predictive accuracy. As data become increasingly large, diverse and fast-moving, conventional processing systems often fall short of the performance required for modern analytics.Objective: This research seeks to thoroughly assess the performance of two prominent big data processing frameworks-Apache Spark (in-memory computing) and MapReduce (disk-based computing)-with a focus on applying random forest algorithms to predict stock prices. …”
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Prognosis and immune landscape of bladder cancer can be predicted using a novel miRNA signature associated with cuproptosis by Zhilei Zhang, Fang Liu, Yongbo Yu, Fei Xie, Tao Zhu

“…Additionally, we developed a nomogram incorporating clinical characteristics and the miRNA signature to further assess its prognostic value. We evaluated the tumor microenvironment (TME) of every patient using immune ESTIMATE, CIBERSORT, and ssGSEA algorithms. …”
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Optimization of Third Rail End Elbow Based on NURBS by RUAN Jie, ZHONG Zhuolin, WANG Bingqin, LI Hongmei, XU Hongyan, YAN Xiaohui

“…According to the collision simulation of the end elbow of the shoe-rail system under the working condition of 120 km/h train running speed, the evaluation indicators of the current collection quality of the shoe-rail system under high speed working conditions are determined. …”
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YOLOX-LS: Strong Gravitational Lenses Detection in the DECaLS with Deep Learning by Yangyang Liu, Liangping Tu, Jianxi Li, Jiawei Miao, Gengqi Lin, Chenying Zhao

“…This paper presents the results of the trained YOLOX-LS algorithm applied to 4.75 million cutout images. …”
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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

“…It was shown that artificial neural networks were the most common kind of trained model, while several other models were often used as benchmarks for performance. …”
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A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization by Mădălina Duceac (Covrig), Călin Gheorghe Buzea, Alina Pleșea-Condratovici, Lucian Eva, Letiția Doina Duceac, Marius Gabriel Dabija, Bogdan Costăchescu, Eva Maria Elkan, Cristian Guțu, Doina Carina Voinescu

“…<b>Methods:</b> A dataset of 977 patients from a Romanian neurosurgical center was used. We trained a deep neural network, random forest, and an autoencoder (trained only on non-recurrence cases) to model baseline and anomalous patterns. …”
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Enhanced Osteoporosis Detection Using Artificial Intelligence: A Deep Learning Approach to Panoramic Radiographs with an Emphasis on the Mental Foramen by Robert Gaudin, Wolfram Otto, Iman Ghanad, Stephan Kewenig, Carsten Rendenbach, Vasilios Alevizakos, Pascal Grün, Florian Kofler, Max Heiland, Constantin von See

“…This study presents a proof-of-concept algorithm, demonstrating the potential of deep learning to identify osteoporosis in dental radiographs. …”
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A statistical and machine learning approach for monthly precipitation forecasting in an Amazon city by Ewerton Cristhian Lima de Oliveira, Eduardo Costa de Carvalho, Edmir dos Santos Jesus, Rafael de Lima Rocha, Rafael de Lima Rocha, Helder Moreira Arruda, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Renata Gonçalves Tedeschi

“…Besides the use of algorithms, another evaluation was conducted on Feature Composition based on statistical methods to investigate the impact of variables on the prediction.ResultsThe results obtained in our investigation indicate that the vector autoregressive moving average with exogenous regressors (VARMAX) model achieved the best performance in rainfall forecasting, with an average root mean square error (RMSE) of 9.1833 in time series cross-validation, outperforming the other models.DiscussionThe climate-driven patterns directly influenced the performance of the rainfall forecasting models evaluated in this study. …”
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Graph-Based COVID-19 Detection Using Conditional Generative Adversarial Network by Imran Ihsan, Azhar Imran, Tahir Sher, Mahmood Basil A. Al-Rawi, Mohammed A. Elmeligy, Muhammad Salman Pathan

“…The proposed methodology encompasses four distinct phases: initial segmentation of raw chest radiographs employing Conditional Generative Adversarial Networks (CGAN), followed by feature extraction through a tailored pipeline integrating both manual computer vision algorithms and pre-trained Deep Neural Network (DNN) models. …”
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Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network by Yujie Lu, Qingbin Du, Ruijia Zhang, Bo Wang, Zigeng Liu, Qizhe Tang, Pan Dai, Xiangxiang Fan, Chun Huang

“…The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. …”
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Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics by Zhenze Zhang, Zhenze Zhang, Caiqing Zhao, Caiqing Zhao, Yijun Zhou, Ling Yao, Peng Liu, Ziling Fang, Nian Fang, Nian Fang

“…This multicenter study aimed to develop and validate ML-based models for mortality risk stratification in critically ill IBD patients using large-scale ICU databases.MethodsData from 551 IBD patients in the MIMIC-IV database (2008–2019) were analyzed, with external validation using the eICU dataset. Nine ML algorithms (XGBoost, logistic regression, LightGBM, random forest, decision tree, elastic net, MLP, KNN, RSVM) were trained to predict 1-year mortality. …”
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Assessment of synthetic post-therapeutic OCT images using the generative adversarial network in patients with macular edema secondary to retinal vein occlusion by Shi Feng, Jingyuan Yang, Xinyu Zhao, Jianchun Zhao, Yunfeng Du, Weihong Yu, Dayong Ding, Xirong Li, Youxin Chen

“…The model is constructed based on the pix2pixHD algorithm, and synthetic OCT images are evaluated in terms of the picture quality, authenticity, the central retinal thickness (CRT), the maximal retinal thickness, the area of intraretinal cystoid fluid (IRC), and the area of subretinal fluid (SRF). …”
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Risk Factors for Gout in Taiwan Biobank: A Machine Learning Approach by Liu YR, Nfor ON, Zhong JH, Lin CY, Liaw YP

“…The predictive performance was evaluated using a split dataset (80% training set and 20% test set).Results: Variable importance analysis was performed to identify key variables, with uric acid and gender emerging as the most influential risk factors. …”
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A machine learning model for predicting obesity risk in patients with diabetes mellitus: analysis of NHANES 2007–2018 by Wenqiang Wang, Ruiqing Mo, Xingyu Chen, Sijie Yang

“…Model performance was evaluated based on area under the ROC curve (AUC), calibration curves, Brier score, and decision curve analysis (DCA). …”
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Preliminary application of a cervical vertebra segmentation method based on Transformer and diffusion model for lateral cephalometric radiographs in orthodontic clinical practice by LIU Yang, WU Mengyi, HU Yao, QI Kun, WANG Yubin, ZHAO Yue, SONG Jinlin

“…Objective·To construct a cervical vertebra image segmentation model by using a diffusion model with the Transformer deep learning algorithm, and evaluate its segmentation performance, to address the clinical challenge of accurately assessing complex changes in skeletal morphology during the growth and developmental peaks of malocclusion.Methods·Accurate cervical vertebra segmentation was performed on cephalometric radiographs from 185 orthodontic patients (44 cases from the Stomatological Hospital of Chongqing Medical University and 141 cases from the Stomatological Hospital of Xi'an Jiaotong University) by using a method combining Transformer and diffusion models. …”
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Derivation and external validation of prediction model for hypertensive disorders of pregnancy in twin pregnancies: a retrospective cohort study in southeastern China by Yuting Gao, Na Lin, Shuisen Zheng, Yujuan Chen, Xiaoling Chen

“…Objective We aimed to develop and validate an effective prediction model for hypertensive disorder of pregnancy (HDP) in twin pregnancies after 28 weeks of gestation.Design Retrospective cohort study.Setting Maternity hospital.Participants We recruited twin pregnancies who delivered in Fujian Maternity and Child Health Hospital from January 2014 to December 2019 as a training cohort. Besides, we included twin pregnancies delivered at Fujian Maternity and Child Health Hospital; Women and Children’s Hospital of Xiamen University from January 2020 to December 2021 as temporal validation set and geographical validation set, respectively.Main outcome measures We performed univariate analysis, the least absolute shrinkage and selection operator regression and Boruta algorithm to screen variables. …”
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