Cited by Advances in sarcopenia and urologic disorders

Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer DOI

Wen‐Han Hsu,

A.-R. Ko,

Chia‐Sui Weng

et al.

Journal of Cachexia Sarcopenia and Muscle, Journal Year: 2023, Volume and Issue: 14(5), P. 2044 - 2053

Published: July 12, 2023

Skeletal muscle loss during treatment is associated with poor survival outcomes in patients ovarian cancer. Although changes mass can be assessed on computed tomography (CT) scans, this labour-intensive process impair its utility clinical practice. This study aimed to develop a machine learning (ML) model predict based data and interpret the ML by applying SHapley Additive exPlanations (SHAP) method.

Language: Английский

Citations

Season and weather factors matter, but not enough: a machine learning-based study on predicting incremental lifetime cancer risk of polycyclic aromatic hydrocarbons DOI

Chenjia Li,

Yuxiang Deng, Nuo Chen

et al.

International Journal of Environmental Health Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 12

Published: Feb. 15, 2025

With the intensification of urbanization, air pollution has garnered global concern. This study aims to predict incremental lifetime cancer risk (ILCR) polycyclic aromatic hydrocarbons (PAHs) in atmospheric PM2.5. Utilizing machine learning regression algorithms and data from six cities Jiangsu Province 2018, we established models investigate relationship between ILCR various factors, with a special emphasis on seasonal meteorological data. After model training, SHapley Additive exPlanation (SHAP) analysis revealed that factors were even more influential than PM2.5 predicting ILCR. Models then validated using 2019 data, resulting an R2 0.42, which indicated decrease accuracy compared 2018 test set 0.74 but still represented improvement over alone (R2 = 0.2). suggests while related are crucial, additional needed build robust for future predictions.

Language: Английский

Citations

Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features DOI

Rongna Lian,

Huiyu Tang,

Zecong Chen

et al.

Aging Clinical and Experimental Research, Journal Year: 2025, Volume and Issue: 37(1)

Published: March 1, 2025

Abstract Objectives Sarcopenic obesity (SO), characterized by the coexistence of and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify validate explainable prediction model SO using easily available clinical characteristics. Setting participants A preliminary cohort 1,431 from three community regions Ziyang city, China, was used for development internal validation. For external validation, we utilized data 832 residents multi-center nursing homes. Measurements The diagnosis based on European Society Clinical Nutrition Metabolism (ESPEN) Association Study Obesity (EASO) criteria. Five machine learning models (support vector machine, logistic regression, random forest, light gradient boosting extreme boosting) were predict SO. performance these assessed area under receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) approach interpretation. Results After feature reduction, 8-feature demonstrated good predictive ability. Among five tested, support (SVM) performed best both (AUC = 0.862) 0.785) validation sets. eight key predictors identified BMI, gender, neck circumference, waist thigh time full tandem standing, five-times sit-to-stand, age. SHAP analysis revealed BMI gender as most influential predictors. To facilitate utilization SVM setting, developed a web application ( https://svcpredictapp.streamlit.app/ ). Conclusions populations. This offers novel, accessible, interpretable potential enhance early detection intervention strategies. Further studies are warranted our diverse populations evaluate its impact patient outcomes when integrated into comprehensive geriatric assessments.

Language: Английский

Citations

Association of nutritional and inflammatory status with all-cause and cardiovascular mortality in adults with sarcopenia: Insights from NHANES DOI

Yang Yang,

Si Shen,

Xiang Luo

et al.

Maturitas, Journal Year: 2025, Volume and Issue: 196, P. 108233 - 108233

Published: March 4, 2025

Language: Английский

Citations

Opportunities and Challenges of Machine Learning in Anticaner Drug Therapies DOI

M.I.A.O. Chunlei,

H.U.A.N.G.F.U. rui,

Chao Yuan

et al.

Intelligent Pharmacy, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

Explainable machine learning model and nomogram for predicting the efficacy of Traditional Chinese Medicine in treating Long COVID: a retrospective study DOI

Jisheng Zhang,

Yang Chen,

Aijun Zhang

et al.

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: March 13, 2025

Introduction Long COVID significantly affects patients' quality of life, yet no standardized treatment has been established. Traditional Chinese Medicine (TCM) presents a promising potential approach with targeted therapeutic strategies. This study aims to develop an explainable machine learning (ML) model and nomogram identify patients who may benefit from TCM, enhancing clinical decision-making. Methods We analyzed data 1,331 treated TCM between December 2022 February 2024 at three hospitals in Zhejiang, China. Effectiveness was defined as improvement two or more symptoms minimum 2-point increase the Syndrome Score (TCMSS). Data included 11 patient disease characteristics, 18 syndrome scores, 12 auxiliary examination indicators. The least absolute shrinkage selection operator (LASSO) method identified features linked efficacy. 1,204 served training set, while 127 formed testing set. Results employed five ML algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Neural Network (NN). XGBoost achieved Area Under Curve (AUC) 0.9957 F1 score 0.9852 demonstrating superior performance set AUC 0.9059 0.9027. Key through SHapley Additive exPlanations (SHAP) chest tightness, aversion cold, age, TCMSS, Short Form (36) Health Survey (SF-36), C-reactive protein (CRP), lymphocyte ratio. logistic regression-based demonstrated 0.9479 0.9384 Conclusion utilized multicenter multiple algorithms create for predicting efficacy treatment. Furthermore, developed assist improve decision-making efficiency applications management.

Language: Английский

Citations

Development and Multi-center validation of a machine learning Model for advanced colorectal neoplasms screening DOI

Mingqing Zhang, Yongdan Zhang, Lizhong Zhao

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 190, P. 110066 - 110066

Published: March 30, 2025

Language: Английский

Citations

Prediction of Lumbar Disc Degeneration Based on Interpretable Machine Learning Models: Retrospective Cohort Study DOI

Tenghui Li, Weihui Qi, Xinggang Mao

et al.

The Spine Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer DOI

Wan-Chun Lin,

Chia‐Sui Weng,

A.-R. Ko

et al.

Supportive Care in Cancer, Journal Year: 2024, Volume and Issue: 32(8)

Published: July 24, 2024

Language: Английский

Citations

Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer DOI

Lin Chun,

Denghuan Wang,

Liqiong He

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 27, 2024

Language: Английский

Citations