Unstructured Electronic Health Records of Dysphagic Patients Analyzed by Large Language Models by Luisa Neubig, Deirdre Larsen, Melda Kunduk, Andreas M. Kist

“…We utilize clustering algorithms on the extracted diagnostic features to identify distinct groups of patients who share similar pathophysiological swallowing dysfunctions.Results: We found that basic NLP techniques often provide limited insights due to the high variability of the data. …”
Get full text

Using Digital Technology to Conduct COVID-19 Surveillance: A Systematic Review by Fitrisia Rahma, Fatmah Fatmah, Rachmadhi Purwana

“…Key among these are (1) the scarcity of high-quality, unbiased data and the difficulties of integrating disparate datasets; (2) limitations in technology design and the shortage of skilled human resources to develop, deploy, and maintain digital tools; (3) gaps in physical and network infrastructure, particularly in low- and middle-income settings; (4) cybersecurity vulnerabilities and complex ethical quandaries around privacy, consent, and algorithmic transparency; and (5) broader political, social, and environmental barriers, ranging from governance misalignment to inequitable access and ecological impacts. …”
Get full text

Spatial-Spectral Linear Extrapolation for Cross-Scene Hyperspectral Image Classification by Lianlei Lin, Hanqing Zhao, Sheng Gao, Junkai Wang, Zongwei Zhang

“…Extensive experiments were conducted on four datasets, an ablation study showed the effectiveness of the proposed modules, and a comparative analysis with advanced DG algorithms showed the superiority of our model in the face of various spectral and category shifts. …”
Get full text

Problems and perspectives of family doctors training on the undergraduate stage by Yu. M. Kolesnik, V. D. Syvolap, N. S. Mikhaylovskaya, T.O. Kulinich

“…In the educational process teaching materials consist of the work program on the subject, methodological recommendations for independent work and workshops, for practical skills and algorithms for their implementation, tests of initial, current and final knowledge control, samples of basic records of general practice - family medicine, education and qualification characteristics of family doctor, a list of basic regulatory documents, educational literature. …”
Get full text

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study by Andrew Johnson, Luke Oakden-Rayner, Catherine M Jones, Jarrel Seah, Cyril Tang, Quinlan D Buchlak, Nazanin Esmaili, Luke Danaher, Michael R Milne

“…Objectives Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. …”
Get full text

Method and Optimization of Key Parameters of Soil Organic Matter Detection Based on Pyrolysis Coupled with Artificial Olfaction by Mingwei Li, Xiao Li, Xuexun Li, Wenjun Wang, Yulong Chen, Long Zhou, Xiaomeng Xia

“…Finally, the Back-Propagation Neural Network (BPNN) and Partial Least-Squares Regression (PLSR) algorithms were used to establish an SOM prediction model after obtaining soil pyrolysis gases under the optimal combination of pyrolysis parameters. …”
Get full text

Integrative machine learning model of RNA modifications predict prognosis and treatment response in patients with breast cancer by Tao Wang, Shu Wang, Zhuolin Li, Jie Xie, Qi Jia, Jing Hou

“…In addition, immune infiltration levels were evaluated via six different algorithms, and immune checkpoint inhibitor responsiveness was predicted. …”
Get full text

Comparison of light gradient boosting and logistic regression for interactomic hub genes in Porphyromonas gingivalis and Fusobacterium nucleatum-induced periodontitis with Alzheime... by Pradeep Kumar Yadalam, Shubhangini Chatterjee, Prabhu Manickam Natarajan, Carlos M. Ardila, Carlos M. Ardila

“…Logistic regression and light gradient boosting were used to predict interactomic hub genes, with outliers removed and machine learning algorithms applied.ResultsThe data were cross-validated and divided into training and testing segments. …”
Get full text

Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China by Haijun Wang, Xiangdong Kong, Onanong Phewnil, Ji Luo, Pengju Li, Xiyong Chen, Tianhui Xie

“…Methods We employed a large sample and composite supervised classification algorithms to classify alpine wetlands and generate wetland maps, based on the Google Earth Engine cloud computing platform. …”
Get full text

Acoustic emission detection and defect identification method for micro-leakage in natural gas pipeline valves by Lizhen ZHANG, Wen'ao WANG

“…Conclusion The classified processing method for acoustic emission signals, based on acoustic emission technology, feature extraction, PCA, and clustering algorithms, can enhance the accuracy and stability of valve micro-leakage detection. …”
Get full text

Exploring the application of machine learning and SHAP explanations to predict health facility deliveries in Somalia by Jamilu Sani, Salad Halane, Mohamed Mustaf Ahmed, Abdiwali Mohamed Ahmed, Jamal Hassan Mohamoud

“…Methods This study analyzed data from the 2020 Somalia Demographic and Health Survey (SDHS) involving 8,951 women aged 15–49 years. Seven ML algorithms, Random Forest, XGBoost, Gradient Boosting, Logistic Regression, Support Vector Machine, Decision Tree, and K-Nearest Neighbors, were evaluated for their ability to predict health facility deliveries. …”
Get full text

Neural Networks vs. Regression: A Comparative Analysis in Medical Data Processing by Minodora ANDOR, Gheorghe Ioan MIHALAŞ

“…The regression model followed standard statistical guidelines, while the FFNN required optimization of hyperparameters, including the number of layers, activation functions, learning rate, and  optimization algorithms. We extended the study using simulated data to further compare logistic regression (a data set of 300 patients) with neural networks. …”
Get full text

Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review by Wilson W S Tam, Kang Sim, Jing Ling Tay, Yun Ling Ang

“…Logistic regression and random forest were the best performing algorithms. Factors influencing outcomes included demographic (age, ethnicity), illness (duration of untreated illness, types of symptoms), functioning (baseline functioning, interpersonal relationships and activity engagement), treatment variables (use of higher doses of antipsychotics, electroconvulsive therapy), data from passive sensor (call log, distance travelled, time spent in certain locations) and online activities (time of use, use of certain words, changes in search frequencies and length of queries).Conclusion Machine learning methods show promise in the prediction of prognosis (specifically functioning, relapse and remission) of mental disorders based on relevant collected variables. …”
Get full text

Network pharmacology and AI in cancer research uncovering biomarkers and therapeutic targets for RALGDS mutations by S. Mohammed Zaidh, Hariharan Thirumalai Vengateswaran, Mohammad Habeeb, Kiran Balasaheb Aher, Girija Balasaheb Bhavar, N. Irfan, K N V Chenchu Lakshmi

“…Structured E-pharmacophore model aided in capturing the binding cavity using eraser algorithms and fabricating a new selective lead compound for the KRSA. …”
Get full text

Research on machine vision online monitoring system for egg production and quality in cage environment by Zhenlong Wu, Hengyuan Zhang, Cheng Fang

“…To diverse operational needs, two distinct post-processing algorithms were developed: one for counting eggs and detecting abnormalities during robotic patrols, and another for assessing egg quality through fixed video streams, which measured crucial parameters such as egg dimensions and shape indexes. …”
Get full text

Analysis and reduction of topographic effect induced errors in land surface temperature retrieval over the Tibetan Plateau by Yuejie Zhang, Qinghong Sheng, Kerui Li, Bo Wang, Jun Li, Xiao Ling, Fan Gao

“…However, most retrieval algorithms assume a flat-surface thermal infrared radiation transfer equation (TIRTE), which introduces inevitable topographic induced in the TP’s complex terrain. …”
Get full text

An evaluation methodology for machine learning-based tandem mass spectra similarity prediction by Michael Strobel, Alberto Gil-de-la-Fuente, Mohammad Reza Zare Shahneh, Yasin El Abiead, Roman Bushuiev, Anton Bushuiev, Tomáš Pluskal, Mingxun Wang

“…Machine learning (ML) approaches have emerged as a promising technique to predict structural similarity from MS/MS that may surpass the current state-of-the-art algorithmic methods. However, the comparison between these different ML methods remains a challenge because there is a lack of standardization to benchmark, evaluate, and compare MS/MS similarity methods, and there are no methods that address data leakage between training and test data in order to analyze model generalizability. …”
Get full text

A Survey on Adversarial Attacks for Malware Analysis by Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Pradip Kunwar, Bhavani Thuraisingham

“…We systematically analyze adversarial generation algorithms from broader domains adapted to malware evasion attacks, proposing a taxonomy of adversarial evasion attacks within malware detection based on target domains(Windows, Android and PDF). …”
Get full text

Investigation of the Regularities of the Influence of Meteorological Factors on Avalanches in Eastern Kazakhstan by Marzhan Rakhymberdina, Natalya Denissova, Yerkebulan Bekishev, Gulzhan Daumova, Milan Konečný, Zhanna Assylkhanova, Azamat Kapasov

“…Descriptive statistics, correlation analysis, principal component analysis (PCA), as well as K-means clustering and DBSCAN algorithms, were used for the analysis. During the analysis of meteorological conditions preceding avalanches at nine avalanche-prone areas in Eastern Kazakhstan, using PCA (Principal Component Analysis), the main weather factors affecting avalanche formation were determined. …”
Get full text

Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning by Hui Xu, Xingwang Peng, Ziyu Peng, Rui Wang, Rui Zhou, Lianguo Fu

“…Second, ML models were constructed based on the logistic regression (LR), decision tree (DT), support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms, after which random sampling and tenfold cross-validation were separately used to evaluate and compare these models and identify the optimal model. …”
Get full text