Development and validation of prediction algorithm to identify tuberculosis in two large California health systems by Heidi Fischer, Lei Qian, Zhuoxin Li, Katia Bruxvoort, Jacek Skarbinski, Yuching Ni, Jennifer H. Ku, Bruno Lewin, Saadiq Garba, Parag Mahale, Sally F. Shaw, Brigitte Spence, Sara Y. Tartof

Get full text

Automatic screening for posttraumatic stress disorder in early adolescents following the Ya’an earthquake using text mining techniques by Yuzhuo Yuan, Yuzhuo Yuan, Zhiyuan Liu, Wei Miao, Xuetao Tian

“…Meanwhile, participants completed the PTSD Checklist for DSM-5 (PCL-5). Text classification models were constructed using three supervised learning algorithms (BERT, SVM, and KNN) to identify PTSD symptoms and their corresponding behavioral indicators in each sentence of the self-narratives.ResultsThe prediction accuracy for symptom-level classification reached 73.2%, and 67.2% for behavioral indicator classification, with the BERT performing the best.ConclusionsThese findings demonstrate that self-narratives combined with text mining techniques provide a promising approach for automated, rapid, and accurate PTSD screening. …”
Get full text

Automated Cervical Cancer Screening Using Single-Cell Segmentation and Deep Learning: Enhanced Performance with Liquid-Based Cytology by Mariangel Rodríguez, Claudio Córdova, Isabel Benjumeda, Sebastián San Martín

“…These findings demonstrate the potential of AI-powered cervical cell classification for improving CC screening, particularly with LBC. The high accuracy and efficiency of DL models combined with effective segmentation can contribute to earlier detection and more timely intervention. …”
Get full text

Diagnosis of bipolar disorder based on extracted significant biomarkers using bioinformatics and machine learning algorithms by Hamid Mohseni, Massoud Sokouti, Akram Nezhadi, Ali Sayadi

“…Conclusion. We presented two models to diagnose bipolar disorder. One model was developed using artificial neural network and tanh functions and the other model was developed using decision tree algorithm. …”
Get full text

Machine learning algorithm based on combined clinical indicators for the prediction of infertility and pregnancy loss by Rui Zhang, Yuanbing Guo, Xiaonan Zhai, Juan Wang, Xiaoyan Hao, Liu Yang, Lei Zhou, Jiawei Gao, Jiayun Liu

“…Three methods were used for screening 100+ clinical indicators, and five machine learning algorithms were used to develop and evaluate diagnostic models based on the most relevant indicators.ResultsMultivariate analysis revealed significant differences in several factors between the patients and the control group. 25-hydroxy vitamin D3 (25OHVD3) was the factor exhibiting the most prominent difference, and most patients presented deficiency in the levels of this vitamin. 25OHVD3 is associated with blood lipids, hormones, thyroid function, human papillomavirus infection, hepatitis B infection, sedimentation rate, renal function, coagulation function, and amino acids in patients with infertility. …”
Get full text

Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysisRes... by Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang, Xiaoer Wei, Shundong Hu, Yuwu Zhao, Li Cao, Jun Wang, Saman Nazarian, Ying Cao, Lan Zheng, Min Zhang, Jianliang Fu, Jingbo Li, Xiang Han, Dahong Qian, Dong Huang

“…More importantly, our proposed model identified all the AF patients that were diagnosed with Holter monitoring during index stroke admission. …”
Get full text

Corrosion Rate Prediction of Buried Oil and Gas Pipelines: A New Deep Learning Method Based on RF and IBWO-Optimized BiLSTM–GRU Combined Model by Jiong Wang, Zhi Kong, Jinrong Shan, Chuanjia Du, Chengjun Wang

“…The combined model, which incorporates an intelligent algorithm, is an effective means of enhancing the precision of buried pipeline corrosion rate prediction. …”
Get full text

Research on Investment Estimation of Prefabricated Buildings Based on Genetic Algorithm Optimization Neural Network by Jin Gao, Wanhua Zhao

“…Starting from the investment decision-making stage of construction projects, this paper analyses the characteristics of prefabricated investment estimation and the relevant literature on the characteristics of prefabricated construction projects, uses the rough set attribute reduction algorithm to screen the key engineering characteristic factors, and establishes a BP neural network model optimized by genetic algorithm to estimate and analyze the investment of completed prefabricated construction projects. …”
Get full text

Assessing the predictive value of time-in-range level for the risk of postoperative infection in patients with type 2 diabetes: a cohort study by Ying Wu, Rui Xv, Qinyun Chen, Ranran Zhang, Min Li, Chen Shao, Guoxi Jin, Guoxi Jin, Xiaolei Hu, Xiaolei Hu

“…LASSO regression and the Boruta algorithm were used to screen out the predictive factors related to postoperative infection in T2DM patients in the training set. …”
Get full text

Apply a Screensaver Template for Windows 98 by Zuhor Hasan, Ahmed Nori, Asmaa Hamo

“…This paper involves designing graphics model for displaying and working under Windows98 operating system called Screen Saver, which is considered as one of the most significant desktop settings. …”
Get full text

Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening by Keisuke Uchikawa, Kairi Furui, Masahito Ohue

“…In contrast, with limited active compound data, a random search strategy proves more effective. Moreover, our approach is particularly promising for targets that yield poor screening results when using experimentally determined structures from the PDB. …”
Get full text

Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques by Juswin Sajan John, Boppuru Rudra Prathap, Gyanesh Gupta, Jaivanth Melanaturu

“…Building on existing literature, discussions with psychologists and other mental health practitioners, we developed a taxonomy of 27 distinctive markers spread across 4 label categories; aiming to create a preliminary screening tool leveraging textual data.The core objective is to identify the most suitable model for this complex task, encompassing comprehensive evaluation of various machine learning and deep learning algorithms. we experimented with support vector machines (SVM), random forest (RF) and long short-term memory (LSTM) algorithms incorporating various feature combinations involving Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA). …”
Get full text

Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study [version 2; peer review: 2 approved, 1 approved with reservations] by Emily Kendall, Lucia Cilloni, Nimalan Arinaminpathy, David Dowdy

“…Concentrating on urban areas in low- and middle-income countries, the aim of this analysis was to estimate the degree to which ‘dynamic’ screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing. …”
Get full text

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data by Masahiro Hata, Yuki Miyazaki, Kohji Mori, Kenji Yoshiyama, Shoshin Akamine, Hideki Kanemoto, Shiho Gotoh, Hisaki Omori, Atsuya Hirashima, Yuto Satake, Takashi Suehiro, Shun Takahashi, Manabu Ikeda

“…A total of 102 patients, both with and without AD-related biomarker changes (amyloid beta and phosphorylated tau), were recorded using a 2-minute resting-state portable EEG. A machine-learning algorithm then analyzed the EEG data to identify these biomarker changes. …”
Get full text

Using Life’s Essential 8 and heavy metal exposure to determine infertility risk in American women: a machine learning prediction model based on the SHAP method by Xiaoqing Gu, Qianbing Li, Xiangfei Wang

“…The association between LE8 and heavy metal exposure and risk of infertility was assessed using logistic regression analysis and six machine learning models (Decision Tree, GBDT, AdaBoost, LGBM, Logistic Regression, Random Forest), and the SHAP algorithm was used to explain the model’s decision process.ResultsOf the six machine learning models, the LGBM model has the best predictive performance, with an AUROC of 0.964 on the test set. …”
Get full text

AI-Assisted Detection for Early Screening of Acute Myeloid Leukemia Using Infrared Spectra and Clinical Biochemical Reports of Blood by Chuan Zhang, Jialun Li, Wenda Luo, Sailing He

“…Acute myeloid leukemia (AML) accounts for most cases of adult leukemia, and our goal is to screen out some AML from adults. In this work, we introduce an AI-enhanced system designed to facilitate early screening and diagnosis of AML among adults. …”
Get full text

Two-Step Screening for Depression and Anxiety in Patients with Cancer: A Retrospective Validation Study Using Real-World Data by Bryan Gascon, Joel Elman, Alyssa Macedo, Yvonne Leung, Gary Rodin, Madeline Li

“…<b>Conclusions:</b> The present study is among the first to demonstrate that a two-step screening algorithm for depression may improve depression screening in cancer using real-world data. …”
Get full text

Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer by Fengqiang Cui, Changjiao Yan, Jiang Wu, Yuqing Yang, Jixin Yang, Jialing Luo, Nanlin Li

“…Next, 101 combinations of 10 machine learning algorithms and univariate Cox analysis were utilized to screen for prognostic genes. …”
Get full text

Potential use of saliva infrared spectra and machine learning for a minimally invasive screening test for congenital syphilis in infants by Deise Cristina Dal’Ongaro, Cicero Cena, Bruno Spolon Marangoni, Daniele A. Soares-Marangoni

“…When applying Quadratic Standard Normal Variate preprocessing with LOOCV, the model achieved 90% accuracy, 100% sensitivity, and 80% specificity. …”
Get full text

Prospective external validation of the automated PIPRA multivariable prediction model for postoperative delirium on real-world data from a consecutive cohort of non-cardiac surgery... by Mary-Anne Kedda, Kelly A Reeve, Nayeli Schmutz Gelsomino, Michela Venturini, Felix Buddeberg, Martin Zozman, Reto Stocker, Philipp Meier, Marius Möller, Simone Pascale Wildhaber, Benjamin T Dodsworth

“…The study highlighted the model’s applicability across diverse clinical environments, despite differences in patient populations and screening protocols.Conclusions The PIPRA algorithm is a reliable tool for identifying surgical patients at risk of POD, supporting early intervention strategies to improve patient outcomes. …”
Get full text