Results 221 to 230 of about 95,729 (269)

Predicting Post-Induction Hypotension in Diverse Surgical Populations: A Multiclass Classification Universal Model Using Machine Learning Techniques. [PDF]

Yonsei Med J
Lee SW, Lee D, Kim SH.
europepmc   +1 more source

From Data to Decisions: How Machine Learning and Generative Artificial Intelligence Are Redefining Precision Medicine in Kidney Transplantation

Organ Medicine, EarlyView.
This review evaluates how machine learning, multimodal integration, and generative AI optimize kidney transplant outcomes. These tools enable superior prediction and personalized therapy but face hurdles in data volume, generalizability, and ethics. Future clinical adoption depends on continued innovation and multidisciplinary collaboration to overcome
Maoxin Liao, Cheng Yang
wiley   +1 more source

Machine Learning-Based Risk Prediction for Coronary Heart Disease Complicated by Hyperhomocysteinemia: Retrospective Study.

JMIR Med Inform
Du MY, Lyu MK, Liu HL, Li YZ, Yan HF, Li XH.   +5 more
europepmc   +1 more source

Key predictors of postpartum depression and anxiety symptoms among mothers in Kilifi, Kenya: a machine learning approach. [PDF]

Front Psychiatry
Benson FN, Odhiambo R, Brink W, Ngugi AK, Waljee AK, Weinheimer-Haus EM, Moyer CA, Zhu J, Abubakar A.   +8 more
europepmc   +1 more source

A Self‐Supervised Machine Learning Approach for the Estimation of Open‐Circuit Voltage Degradation in Photovoltaic Systems

Progress in Photovoltaics: Research and Applications, EarlyView.
This study introduces a self‐supervised machine learning approach integrating physics‐based principles to estimate open‐circuit voltage (voc$$ {v}_{oc} $$) degradation in photovoltaic systems using SCADA data. By combining clustering and regression algorithms, our method detects performance deviations without labelled datasets.
Sandra Riaño, Jose‐Domingo Santos, Miguel Esteras, Amaia Abanda, Javier Del Ser   +4 more
wiley   +1 more source

Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data. [PDF]

PLoS One
Zuo S, Liu N, Wang J, Li J, Zhu X, Jia Y.   +5 more
europepmc   +1 more source

Optimization of Flexural Strength of PLA Flexural Test Specimens Produced by FDM Using Taguchi L27 Orthogonal Array and Hybrid Regression Model

Journal of Polymer Science, EarlyView.
This study investigates the impact of FDM process parameters on the flexural strength of PLA components using a Taguchi L27 orthogonal array. Analysis reveals wall thickness and layer height as the primary determinants of mechanical performance. A highly accurate regression model is developed, providing a robust predictive framework to optimize 3D ...
Mehmet Şah Gültekin, Cüneyt Özdemir
wiley   +1 more source

Assessment of PlanetScope Spectral Data for Estimation of Peanut Leaf Area Index Using Machine Learning and Statistical Methods. [PDF]

Sensors (Basel)
Ekwe M, Fernando H, James G, Adeluyi O, Verrelst J, Kross A.   +5 more
europepmc   +1 more source

Machine learning‐based predictive models versus traditional risk scores in hemodialysis patients with comorbid urolithiasis

Precision Medical Sciences, EarlyView.
Machine learning‐based predictive models outperform traditional risk scores in hemodialysis patients with comorbid urolithiasis by capturing nonlinear, dialysis‐specific interactions. These approaches enable more accurate prediction of stone recurrence, sepsis, hospitalization, and mortality, supporting personalized risk stratification and precision ...
Dipal Chaulagain, Muhammad Riaz, Mirza Muhammad Hadeed Khawar, Jawad Hameed, Malik Sohail Riaz   +4 more
wiley   +1 more source