Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adeno... by Song Lin, Song Lin, Yanli Niu, Yanli Niu, Lina Song, Yingjian Ye, Jinfang Yang, Junjie Liu, Xin Zhou, Xin Zhou, Peng An, Peng An

“…Ten machine learning algorithms (e.g., XGBoost, CatBoost, Random Forest) were trained on a stratified 7:3 split (training: n=245; testing: n=105) with five-fold cross-validation. …”
Get full text

Predicting postoperative malnutrition in patients with oral cancer: development of an XGBoost model with SHAP analysis and web-based application by Lixia Kuang, Lixia Kuang, Jingya Yu, Yunyu Zhou, Yu Zhang, Yu Zhang, Guangman Wang, Guangman Wang, Fangmin Zhang, Grace Paka Lubamba, Grace Paka Lubamba, Xiaoqin Bi, Xiaoqin Bi

Get full text

Climate-Based Prediction of Rice Blast Disease Using Count Time Series and Machine Learning Approaches by Meena Arumugam Gopalakrishnan, Gopalakrishnan Chellappan, Santhosh Ganapati Patil, Santosha Rathod, Kamalakannan Ayyanar, Jagadeeswaran Ramasamy, Sathyamoorthy Nagaranai Karuppasamy, Manonmani Swaminathan

“…This work shows how machine learning algorithms can improve the prediction of rice blast, offering vital information for early disease management. …”
Get full text

Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy by Young Kwang Chae, Emma Yu, Na Hyun Kim, Min Jeong Kim, Leeseul Kim, Hyung-Gyo Cho, Yeonggyeong Park, Yoonhee Choi, Seung Pyo Daniel Hong

“…Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. …”
Get full text

Diagnostics-linked Antimicrobial Surveillance: A Route to Patient-Centred Microbiology Diagnostics? by Dr Alex Howard, Dr Charlotte Brookfield, Dr Conor Rosato, Dr Anoop Velluva, Dr Alessandro Gerada, Professor William Hope

“…In this study we trialled a microbiology diagnostics-linked antimicrobial surveillance (DLAS) algorithmic approach on real-world urine antimicrobial susceptibility testing and antimicrobial prescribing data. …”
Get full text

Exploring the gut microbiota associated with peripheral nerve invasion in colorectal cancer patients and constructing predictive models by Chuanbin Chen, Qingmin Chen, Shenghai Liu, Guoxi Li, Jiawei Zhao, Jingting Huang, Tianyi Ye, Xinting Yang, Zigui Huang, Zhen Wang, Fuhai He, Mingjian Qin, Chenyan Long, Binzhe Tang, Yongqi Huang, Weizhong Tang, Jungang Liu, Xiaoliang Huang

Get full text

Differential risk of cardiovascular complications in patients with type-2 diabetes mellitus in Ghana: A hospital-based cross-sectional study. by Christian Obirikorang, Evans Asamoah Adu, Anthony Afum-Adjei Awuah, Samuel Nkansah Darko, Frank Naku Ghartey, Samuel Ametepe, Eric N Y Nyarko, Enoch Odame Anto, William Kwame Boakye Ansah Owiredu

“…To analyze the data, k-means clustering algorithms and regression analysis were used.<h4>Results</h4>The study identified three groups in female patients according to body mass index, relative fat mass, glycated hemoglobin, and triglyceride-glucose index. …”
Get full text

Development and Use of Biotechnological System Models in Applied Scientific Research by M. A. Kerimov

“…The paper examines a specific case of an «operator-machine-animal» biotechnological system employed in the context of machine milking operations. The system includes three subsystems: two of them have a biological nature and exhibit probabilistic behavior, while the milking machine, serving as a connecting element with inanimate characteristics, is to be regarded as a deterministic technical subsystem. …”
Get full text

Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional studyResearch in... by Cristina Saldivia-Siracusa, Eduardo Santos Carlos de Souza, Arnaldo Vitor Barros da Silva, Anna Luíza Damaceno Araújo, Caíque Mariano Pedroso, Tarcília Aparecida da Silva, Maria Sissa Pereira Sant'Ana, Felipe Paiva Fonseca, Hélder Antônio Rebelo Pontes, Marcos G. Quiles, Marcio Ajudarte Lopes, Pablo Agustin Vargas, Syed Ali Khurram, Alexander T. Pearson, Mark W. Lingen, Luiz Paulo Kowalski, Keith D. Hunter, André Carlos Ponce de Leon Ferreira de Carvalho, Alan Roger Santos-Silva

“…Obtained results suggest that the adoption of DL models in healthcare could aid in diagnostic assistance and decision-making during clinical practice. Funding: This work was supported by FAPESP (2022/13069-8, 2022/07276-0, 2021/14585-7 and 2024/20694-1), CAPES, CNPq (307604/2023-3) and FAPEMIG.…”
Get full text

A hybrid deep learning framework for regional reference crop evapotranspiration estimation in the Hetao Irrigation District using limited meteorological data by Xiao Zhang, Yuxin Tao, Chenglong Zhang

“…In response to this concern, this study proposed two integrated deep learning models, i.e., CNN-Transformer and CNN-Informer, to estimate ETo based on three meteorological factor input combinations (temperature-based, radiation-based, and mass transfer-based). …”
Get full text

Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…To address these issues, this study proposes CLPFT (Comprehensive Uncertainty Assessment Framework for Typhoon), an innovative assessment framework integrating prototype learning and uncertainty quantification through a UProtoMLP neural network. Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. …”
Get full text

Optimal management of the acquiring and evaluating the competencies process of university students by G. A. Dorrer, A. G. Dorrer, G. M. Rudakova

“…The methodology is based on matrix mathematics, so increasing the dimension of the problem does not cause a change of the calculation algorithms.…”
Get full text

Aneuploidy screening in women of advanced age in the public healthcare setting of a low- to middle- income country – an observational cohort study by L Geerts, N Du Toit, M Schoeman

“…During the study period, 1 196 women of AMA were seen. Ninety-three received pre-screen counselling, and 44 of these declined DS screening (47.3% (95% confidence interval (CI) 36.9 - 57.9)). …”
Get full text

Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence by Manohar Pavanya, Krishnaraj Chadaga, Vennila J, Akhila Vasudeva, Bhamini Krishna Rao, Srikanth Prabhu, Shashikala K Bhat

“…Statistical analyses were conducted using Jamovi (version: 2.6.26) and JASP team (2024) JASP (version: 0.18.3). Multiple ML classifiers were employed. We developed a stacked ensemble model that integrated various algorithms, including a custom-stacked ensemble approach and three XAI methodologies: Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchor. …”
Get full text

Modified Сomplex Treatment of the Cirhotic Patients with the Hepatopulmonary Syndrome of the Different Severity Degrees: Pathogenetic Reasoning and Efficiency by Abrahamovych M., Abrahamovych O., Tolopko S., Ferko M.

“…Pentoxifyllini 0.05% ­ 100.0 ml intravenously once daily, valsartan ­ 1 tablet (40.0 mg) once daily, spironolactone ­ 1 caps. (50.0 mg) twice a day; with the III degree HPS ­ 4.2% sodium bicarbonate ­ 100.0 ml intravenously once a day, sol. …”
Get full text

On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review by Flavio Leandro de Morais, Raysa Carla Leal da Silva, Anna Beatriz Silva, Estefani Pontes Simao, Maria Eduarda Ferro de Mello, Stephany Paula da Silva Canejo, Katia Maria Mendes, Waldemar Brandao Neto, Jackson Raniel Florencio da Silva, Maicon Herverton Lino Ferreira da Silva Barros, Patricia Takako Endo

Get full text

Navigating Ethical Dilemmas Of Generative AI In Medical Writing by Qurrat Ulain Hamdan, Waleed Umar, Mahnoor Hasan

“…Generative AI in Medical Writing Generative AI tools or “chatbots” combine the adaptive learning capabilities of deep learning algorithms and natural language processing, resulting in a virtual assistant or aide that is capable of answering queries, following commands, and improving its responses according to the vast data available on the Internet in addition to user responses.3 This has allowed the accomplishment of various complex tasks within seconds that would otherwise require hours of trial and error. …”
Get full text

Ethical framework for responsible foundational models in medical imaging by Debesh Jha, Gorkem Durak, Abhijit Das, Jasmer Sanjotra, Onkar Susladkar, Suramyaa Sarkar, Ashish Rauniyar, Nikhil Kumar Tomar, Linkai Peng, Sirui Li, Koushik Biswas, Ertugrul Aktas, Elif Keles, Matthew Antalek, Zheyuan Zhang, Bin Wang, Xin Zhu, Xin Zhu, Hongyi Pan, Deniz Seyithanoglu, Alpay Medetalibeyoglu, Vanshali Sharma, Vedat Cicek, Amir A. Rahsepar, Rutger Hendrix, Rutger Hendrix, A. Enis Cetin, Bulent Aydogan, Mohamed Abazeed, Frank H. Miller, Rajesh N. Keswani, Rajesh N. Keswani, Hatice Savas, Sachin Jambawalikar, Daniela P. Ladner, Amir A. Borhani, Concetto Spampinato, Concetto Spampinato, Michael B. Wallace, Michael B. Wallace, Ulas Bagci

Get full text

Use of artificial intelligence to support prehospital traumatic injury care: A scoping review by Jake Toy, Jonathan Warren, Kelsey Wilhelm, Brant Putnam, Denise Whitfield, Marianne Gausche‐Hill, Nichole Bosson, Ross Donaldson, Shira Schlesinger, Tabitha Cheng, Craig Goolsby

“…The majority used machine learning (88%) alone or in conjunction with DL or NLP, and the top three algorithms used were support vector machine, logistic regression, and random forest. …”
Get full text

Insights from UKCTOCS for design, conduct and analyses of large randomised controlled trials by Usha Menon, Aleksandra Gentry-Maharaj, Matthew Burnell, Sophia Apostolidou, Andy Ryan, Jatinderpal K Kalsi, Naveena Singh, Lesley Fallowfield, Alistair J McGuire, Stuart Campbell, Steven J Skates, Anne Dawnay, Mahesh Parmar, Ian J Jacobs

“…Future work There is a pressing need to increase the evidence base to support decision making about all aspects of trial methodology. …”
Get full text