Results 211 to 220 of about 66,452 (290)

Explainable machine learning for early diagnosis of esophageal cancer: A feature-enriched Light Gradient Boosting Machine framework with Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretations. [PDF]

J Int Med Res
Ridwan AM, Mohi Uddin KM.
europepmc   +1 more source

Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy

Journal of Ultrasound in Medicine, EarlyView.
Objectives Based on ultrasound technology and clinical indicators, this study intends to develop multiple risk prediction models for diabetic peripheral neuropathy (DPN), conduct comparative analyses of these models, and further evaluate and validate the diagnostic efficacy of the optimal model for DPN as well as its potential in clinical application ...
Bo‐yu She, Meng‐lu Song, Wen‐bin Chen, Kun‐bin Wu, Zhen‐han Lai   +4 more
wiley   +1 more source

Survival Prediction in Patients With Bladder Cancer Undergoing Radical Cystectomy Using a Machine Learning Algorithm: Retrospective Single-Center Study. [PDF]

JMIR Perioper Med
Causio FA, De Vita V, Nappi A, Sawaya M, Rocco B, Foschi N, Maioriello G, Russo P.   +7 more
europepmc   +1 more source

The Role of Artificial Intelligence in Modern Allergology: A Review of Applications in Diagnosis, Prediction, and Management

JEADV Clinical Practice, EarlyView.
ABSTRACT Artificial Intelligence is rapidly transforming allergology by enhancing diagnosis, risk prediction, automation, patient communication, education, and therapy development. Machine learning approaches, including convolutional neural networks, recurrent architectures, and transformer‐based models, enable analysis of complex datasets from ...
Sebastian Seurig, Stephan Traidl, Sonja Mathes   +2 more
wiley   +1 more source

Interpretable radiomics model based on magnetic resonance imaging to predict responses to transarterial chemoembolization for hepatocellular carcinoma. [PDF]

World J Radiol
Mao Q, Zhang P, Zhou MT, Shi Y, Min XL, Xu H, Yang L, Zhang XM.   +7 more
europepmc   +1 more source

Protective Effects of Riociguat Against Contrast‐Induced Nephropathy: An Experimental and Machine Learning‐Based Study in Rats

The Kaohsiung Journal of Medical Sciences, EarlyView.
ABSTRACT Contrast‐induced nephropathy (CIN) is an important cause of acute kidney injury following exposure to iodinated contrast media, and effective preventive strategies remain limited. This study investigated the renoprotective effects of riociguat, a soluble guanylate cyclase stimulator, in an experimental rat model of CIN and explored machine ...
Mustafa Begenc Tascanov, Kenan Toprak, Sibel Turedi, İsmail Koyuncu, Zulkif Tanrıverdi, Necip Fazıl Dedeoglu, Halil Fedai, Asuman Bicer, İbrahim Halil Altıparmak, Recep Demirbag, Gencay Sarıısık   +10 more
wiley   +1 more source

Machine learning model identifies tibial anatomical variables as potential risk factors for anterior cruciate ligament injury

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose The tibial slope is a well‐known risk factor for anterior cruciate ligament (ACL) injury. As machine learning continues to progress, it has become an increasingly explored tool for clinical screening and risk factor analysis. This study aims to develop and validate a prognostic machine learning model to predict the outcome of ACL ...
Cheng‐Hao Kao, Javad Hashemi, James Slauterbeck, Naveen Chandrashekar   +3 more
wiley   +1 more source

Reliable and efficient solar radiation estimation with the insights of XAI. [PDF]

Sci Rep
Nallakaruppan MK, Johnson J, Mapari S, Dasgupta A, Udgir S, Narmatha C.   +5 more
europepmc   +1 more source

Data‐Driven Design and Discovery of Metal–Organic Framework/Polymer Mixed Matrix Membranes

Macromolecular Materials and Engineering, EarlyView.
Integration of machine learning (ML) to current experimental and computational studies will be central to unlocking the potential of metal–organic framework (MOF)/polymer mixed matrix membranes (MMMs) by guiding materials selection, predicting membrane performance, and even synthesis conditions.
Seda Keskin
wiley   +1 more source