Clinical performance validation and four diagnostic strategy assessments of high-sensitivity troponin I assays by Junyi Wu, Yaotong Hua, Yilin Ge, Ke Chen, Siyu Chen, Jiashu Yang, Hui Yuan

“…However, there is no consensus on the optimal diagnostic strategy for early NSTEMI detection. …”
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Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques by Negin Ashrafi, Armin Abdollahi, Kamiar Alaei, Maryam Pishgar

“…Comprehensive evaluations were conducted based on multiple metrics, including Area Under the Curve (AUC), accuracy, F1 score, sensitivity, specificity, Positive Predictive Value, and Negative Predictive Value. XGBoost emerged as the top performing algorithm, achieving an AUC of 0.94 and an accuracy of 0.875 on the test set, marking substantial improvements over previously reported best results. …”
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Multimodal machine learning-based model for differentiating nontuberculous mycobacteria from mycobacterium tuberculosis by Hong-ling Li, Ri-zeng Zhi, Hua-sheng Liu, Mei Wang, Si-jie Yu

“…The multimodal model contained age, IL-6, and the 2 radiomics features, and the optimal model was from LightGBM algorithm. The optimal multimodal model had the highest AUC value, accuracy, sensitivity, and negative predictive value compared with the optimal clinical or radiomics models, and its’ favorable performance was also verified in the external test dataset (accuracy = 0.745, sensitivity = 0.900). …”
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The interpretable machine learning model for depression associated with heavy metals via EMR mining method by Site Xu, Mu Sun

“…The optimal model was selected after parameter tuning with a Genetic Algorithm (GA). …”
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Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…The diagnostic accuracy was 90.58%, with an overall positive predictive value of 89% and an overall negative predictive value of 86%. The algorithm effectively handled the CT images at the preprocessing stage, and the deep learning model performed well in detecting and classifying nodules. …”
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Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Stud... by Weiwei Hu, Yulong Liu, Jian Dong, Xuelian Peng, Chunyan Yang, Honglin Wang, Yong Chen, Shan Shi, Jin Li

“…The CatBoost (CATB) algorithm was optimized via 5-fold cross-validation and random grid search using 24 routine blood parameters. …”
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Reconstruction for Scanning LiDAR with Array GM-APD on Mobile Platform by Di Liu, Jianfeng Sun, Wei Lu, Sining Li, Xin Zhou

“…The position, attitude, and scanning angles provided by POS and angular encoders are used to reduce or eliminate the dynamic effects in multiple-laser-pulse detection. Then, an optimization equation is constructed based on the negative-binomial distribution detection model of GM-APD. …”
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Postimplementation Evaluation in Assisted Living Facilities of an eHealth Medical Device Developed to Predict and Avoid Unplanned Hospitalizations: Pragmatic Trial by Jacques-Henri Veyron, François Deparis, Marie Noel Al Zayat, Joël Belmin, Charlotte Havreng-Théry

“…System performance to predict hospitalization had a high specificity (96%) and negative predictive value (99.4%). ConclusionsThe Presage Care system has been implemented with success in assisted living facilities. …”
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Development and evaluation of a triplex real-time PCR assay for enhanced plague diagnostics in Madagascar. by Beza Ramasindrazana, Zoé A Rahalison, Philippe Gauthier, Guillain Mikaty, Zaina I Bodoarison, Lanto A Maminirina, Soloandry Rahajandraibe, Mamy G Randriamanantsoa, Gilbert Kayoko, Jean-Claude Manuguerra, Anne-Sophie Le Guern, Andriamiliharison J Rasamindrakotroka, Minoarisoa Rajerison

“…The aim of this study is to design and optimize a three-target real-time PCR assay (qPCR) for the detection of Y. pestis in clinical samples.…”
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UHVDC Transmission Line Fault Identification Method Based on Generalized Regression Neural Network by XIE Jia, LIU Feng, KE Yanguo, YIN Zhen, RUAN Wei, YAO Jinming

“…Secondly, the chaos quantum particle swarm optimization (CQPSO) algorithm is used to optimize the parameters of the generalized regression neural network, form an ideal network model based on the principle of the lowest fitness function, and better learn the fault characteristics of the ultra-high voltage DC transmission line. …”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…<b>Results:</b> This study assessed the performance of dimensionality reduction and classification algorithms in distinguishing COVID-19-negative and COVID-19-positive cases using radiomics data from brain MR scans. …”
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The Utility of Follow-up Transthoracic Echocardiogram to Screen for Severe Portopulmonary Hypertension (POPH) in Patients Granted POPH Model for End-stage Liver Disease (MELD) Exce... by Kathryn T. del Valle, MD, Dana Kay, MD, Michael J. Krowka, MD, James R. Runo, MD, Corey Sadd, MD, Julie K. Heimbach, MD, Rodrigo Cartin-Ceba, MD, Hector R. Cajigas, MD, Charles D. Burger, MD, John E. Moss, MD, Hilary M. DuBrock, MD

“…We examined varied echocardiographic parameters to optimize sensitivity for the detection of hemodynamically severe POPH. …”
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Causes of resistance to PARP inhibitors and ways to overcome it. Case report of aggressive <i>BRCA</i>-related breast cancer by A. I. Stukan, A. Yu. Goryainova, S. V. Sharov, O. A. Goncharova, Z. K. Khachmamuk, V. V. Durov

“…However, in clinical practice, despite the proven antitumor efficacy of drugs, acquired resistance to PARP inhibitors leads to difficulties in selecting further therapy due unknown resistance mechanisms and absence of algorithm of action. Despite the various mechanisms of resistance to PARP inhibitors, the choice of subsequent combination therapy after the detection of resistance to PARP inhibitors should be based on an understanding of these mechanisms and the existence of heterogeneous metastatic process. …”
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The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights by Majdi Maabreh, Arwa Maabreh, Basheer Qolomany, Ala Al-Fuqaha

“…The word “robustness” refers to a machine learning algorithm’s ability to cope with hostile situations. …”
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HyenaCircle: a HyenaDNA-based pretrained large language model for long eccDNA prediction by Fuyu Li, Wenxiang Lu, Yunfei Bai

“…We propose HyenaCircle, a novel deep learning model leveraging large language model and third-generation sequencing data to predict long eccDNA formation.MethodsFull-length eccDNAs within 1–5 kb were identified by FLED algorithm for Nanopore sequencing data, extended by 100-bp flanking sequences, and paired with 20,000 length-matched negative controls from eccDNA-depleted genomic regions. …”
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Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma. by José Fernando García Molina, Lei Zheng, Metin Sertdemir, Dietmar J Dinter, Stefan Schönberg, Matthias Rädle

“…Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. …”
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Scalable Hyperspectral Enhancement via Patch-Wise Sparse Residual Learning: Insights from Super-Resolved EnMAP Data by Parth Naik, Rupsa Chakraborty, Sam Thiele, Richard Gloaguen

“…The spectral and spatial characteristics of the scene encoded in the dictionary enable reconstruction through a first-order optimization algorithm to ensure an efficient sparse representation. …”
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Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region by S. I. Panin, V. A. Suvorov, A. V. Zubkov, S. A. Bezborodov, A. A. Panina, N. V. Kovalenko, A. R. Donsckaia, I. G. Shushkova, A. V. Bykov, Ya. A. Marenkov

“…In 28 (20.1 %) cases, at the time of tumor detection, patients did not have clinical symptoms. …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS) with machine learning (ML) algorithms. …”
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The Media Spatial Diffusion Effect and Distribution Characteristics of AI in Education: An Empirical Analysis of Public Sentiments Across Provincial Regions in China by Bowen Chen, Jinqiao Zhou, Hongfeng Zhang

“…Python was used to scrape relevant online comments from various provinces in China. Using the SnowNLP algorithm, sentiments were classified into three categories: positive, neutral, and negative. …”
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