Results 241 to 250 of about 776,810 (287)

Machine Learning Model for Predicting Hepatocellular Carcinoma Development in Patients With Hepatitis B Virus‐Related Cirrhosis Receiving Antiviral Therapy

The Kaohsiung Journal of Medical Sciences, EarlyView.
ABSTRACT Few prediction models have been specifically designed to evaluate the risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B and cirrhosis. This study aimed to develop a machine learning‐based prediction model to assess the risk of developing HCC in patients with hepatitis B virus (HBV)‐related cirrhosis undergoing nucleos(
Pao‐Yuan Huang, Heng‐Syu Lin, Cheng‐Yuan Peng, Ruei‐Hau Hsu, Chien‐Hung Chen   +4 more
wiley   +1 more source

Constructing a predictive model for early-onset sepsis in neonatal intensive care unit newborns based on SHapley Additive exPlanations explainable machine learning. [PDF]

Transl Pediatr
Tan X, Zhang X, Chai J, Ji W, Ru J, Yang C, Zhou W, Bai J, Xiong Y.   +8 more
europepmc   +1 more source

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Methods Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer‐reviewed studies ...
Amna Gillani, Rüdiger von Eisenhart‐Rothe, Klaus Woertler, Michael T. Hirschmann, Daniel Rueckert, Florian Hinterwimmer   +5 more
wiley   +1 more source

Sensitivity analysis of slope stability based on eXtreme gradient boosting and SHapley Additive exPlanations: An exploratory study. [PDF]

Heliyon
Lin H, Li L, Qiang Y, Zhang Y, Liang S, Xu X, Li H, Hu S.   +7 more
europepmc   +1 more source

Random forest and Shapley Additive exPlanations predict oxytocin targeted effects on brain functional networks involved in salience and sensorimotor processing, in a randomized clinical trial in autism. [PDF]

Neuropsychopharmacology
Andari E, Gopinath K, O'Leary E, Caceres GA, Nishitani S, Smith AK, Ousley O, Rilling JK, Cubells JF, Young LJ.   +9 more
europepmc   +1 more source

An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach. [PDF]

Front Psychiatry
Niu T, Cao S, Cheng J, Zhang Y, Zhang Z, Xue R, Ma J, Ran Q, Xian X.   +8 more
europepmc   +1 more source

Weighted Mean SHapley Additive exPlanations (WMSHAP)

openaire   +1 more source

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Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations

Reliability Engineering & System Safety, 2023
Pramudita Satria Palar, Lavi Rizki Zuhal, Koji Shimoyama   +2 more
openaire   +4 more sources

Explaining anomalies detected by autoencoders using Shapley Additive Explanations

Expert Systems with Applications, 2021
Abstract Deep learning algorithms for anomaly detection, such as autoencoders, point out the outliers, saving experts the time-consuming task of examining normal cases in order to find anomalies. Most outlier detection algorithms output a score for each instance in the database.
Liat Antwarg, Ronnie Mindlin Miller, Bracha Shapira, Lior Rokach   +3 more
openaire   +1 more source