Results 231 to 240 of about 3,566,037 (267)

Artificial intelligence and machine learning in global cardiac surgery: A scoping review. [PDF]

JTCVS Open
Patel SK, Deng J, Elsherbini A, Bhatt N, Grewal A, Nedadur R, Chu MWA, Vervoort D.   +7 more
europepmc   +1 more source

Machine Learning‐Guided Design of Next‐Generation Battery Materials for Sustainable Energy Storage Systems

Electron, EarlyView.
Machine learning models were developed to predict material stability and sustainability of battery compounds using data‐driven descriptors. The random forest model demonstrated superior performance, enabling efficient screening and identification of environmentally sustainable and thermodynamically stable materials for next‐generation energy storage ...
Godfrey Perfectson Oise, Felix Oshiorenoya Uloko, Immunhierokene Clinton Obrorindo, Augustine Emakpor Otorokpo   +3 more
wiley   +1 more source

Explainable machine learning for breast cancer prediction in resource-constrained settings: A multi-algorithmic framework integrating shap-based transparency with clinical decision support. [PDF]

PLOS Digit Health
Bamodu OA, Nezam S, Chung CC.
europepmc   +1 more source

Identifying drug‐resistant epilepsy using Swedish National Patient Registry data

Epileptic Disorders, EarlyView.
Abstract Background Approximately 30% of individuals with epilepsy are drug‐resistant, suffering low quality of life and should be evaluated for other treatment modalities. Drug‐resistant epilepsy has no specific diagnostic code, making identification in national healthcare registers challenging and limiting epidemiological research based on register ...
Matilda Modigh, Elina Tayebi, Ronny Wickström, Cecilia Bartholdson, Arvind Kumar, Heléne Sundelin   +5 more
wiley   +1 more source

Predictive Approach to Mapping Angiostrongylus cantonensis Nematode Distribution, Canary Islands, Spain. [PDF]

Emerg Infect Dis
Anettová L, Divíšek J, Coufal R, Šipková A, Kačmaříková J, Horsák M, Baláž V, Izquierdo-Rodriguez E, Červená B, Foronda P, Modrý D.   +10 more
europepmc   +1 more source

Artificial intelligence for adaptive neuromodulation in drug‐resistant epilepsy

Epilepsia, EarlyView.
Abstract Drug‐resistant epilepsy (DRE) affects nearly one third of people with epilepsy and is associated with substantial cognitive, psychiatric, and mortality burdens. For patients who are not candidates for resection or laser interstitial thermal therapy, neuromodulation therapies such as vagus nerve stimulation, deep brain stimulation, and ...
Amir Hossein Daraie, Arianna Damiani, Mahsa Khoshkhou, Luis A. Sanchez, Rachel J. Smith, Shruti H. Agashe, Jorge A. Gonzalez‐Martinez, Jennifer Hopp, Adam S. Charles, Sridevi V. Sarma, Joon‐Yi Kang   +10 more
wiley   +1 more source

Fusion of MLP, XGBoost, and QAT-Optimized PointNet++ for Predicting Short-Term Dendrometer-Derived Stem Dynamics: An Edge-Oriented Computational Framework. [PDF]

Sensors (Basel)
Bolikulov F, Zohirov K, Mannonov G, Khudayorov U, Temirov Z, Mingboev U, Hafizov E, Abdusalomov A, Cho YI.   +8 more
europepmc   +1 more source

Long‐term prediction of epilepsy following traumatic brain injury among veterans using routine clinical data

Epilepsia, EarlyView.
Abstract Objective Despite elevated risk for epilepsy following traumatic brain injury (TBI), there are limited tools to assess epilepsy risk following TBI using routine clinical data. The objective of this study was to develop and validate a machine learning approach to predict the onset of posttraumatic epilepsy (PTE) over varying time horizons ...
Mustafa Ozmen, Shashank Vadlamani, James J. Gugger, Megan Amuan, Ramon Diaz‐Arrastia, Mary Jo Pugh, Eamonn Kennedy   +6 more
wiley   +1 more source

An Introduction to Machine Learning for the Pediatric Hospitalist. [PDF]

Hosp Pediatr
Meyer AG, Blasick S, Yang S, Santillana M.   +3 more
europepmc   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source