Nomogram for prognosis prediction in metastatic pancreatic cancer patients undergoing intra-arterial infusion chemotherapy: incorporating immune-inflammation scores and coagulation... by Yifan Yang, Shaoqi Zong, Yongqiang Hua

“…Next, univariate analysis was utilized to identify prognostic factors, and a stepwise regression method was employed for variable selection to construct a nomogram based on the Cox proportional hazards model. …”
Get full text

A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…Univariate analysis was used to examine differential indicators, and variable selection was conducted through LASSO regression. …”
Get full text

Complex multivariate model predictions for coral diversity with climatic change by Tim R. McClanahan, Maxwell K. Azali, Nyawira A. Muthiga, Sean N. Porter, Michael H. Schleyer, Mireille M. M. Guillaume

“…Abstract Models of the future of coral reefs are potentially sensitive to theoretical assumptions, variable selectivity, interactions, and scales. A number of these aspects were evaluated using boosted regression tree models of numbers of coral taxa trained on ~1000 field surveys and 35 spatially complete influential environmental proxies at moderate scales (~6.25 km2). …”
Get full text

Risk Factors for Stress Fractures in Female Runners: Results of a Survey by Therese E Johnston, Allison E Jakavick, Caroline A Mancuso, Kathleen C McGee, Lily Wei, Morgan L Wright, Jeremy Close, Ayako Shimada, Benjamin E Leiby

“…Multivariable logistic regression models simultaneously investigated associations of multiple factors using backward variable selection. # Results Data from 1648 respondents were analyzed. …”
Get full text

Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain by Xiaoang Zhang, Yuping Liao, Daying Zhang, Weichen Liu, Zhijian Wang, Yaxin Jin, Shushu Chen, Jianmei Wei

“…SVM-RFE and LASSO regression were used for variable selection. We then developed machine learning models and interpreted them by SHAP. …”
Get full text

Time to Relapse and Relapse Predictors in Patients with Schizophrenia at Ayder Comprehensive Specialized Hospital, Northern Ethiopia by Habtamu Endashaw Hareru, Kebede Embaye Gezae, Daniel Sisay W/tsadik, Gebremedhin Berhe Gebregergs

“…Both univariate and multivariate Cox regression models were used for variable selection. Finally, after confirming the model’s diagnosis and assumptions, factors with a p value of less than 0.05 were declared to be statistically significant predictors of schizophrenia relapse. …”
Get full text

Predicting early diagnosis of intensive care unit-acquired weakness in septic patients using critical ultrasound and biological markers by Ling Lei, Liang He, Tongjuan Zou, Jun Qiu, Yi Li, Ran Zhou, Yao Qin, Wanhong Yin

“…Logistic regression analysis was conducted for variable selection and nomogram model construction. Results A total of 116 septic patients were included, comprising 77 males and 39 females (mean age: 56.94 ± 19.90 years). …”
Get full text

A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR by Yuqi Zhang, Sijin Li, Peibiao Mai, Yanqi Yang, Niansang Luo, Chao Tong, Kuan Zeng, Kun Zhang

“…The diagnosis of AF subtypes is confirmed by ECG observation for at least more than 7 days. Variable selection was performed by spearman correlation analysis, recursive feature elimination, and least absolute shrinkage and selection operator regression. …”
Get full text

Development and assessment of a mortality risk prediction nomogram model for pneumocystis disease in ICU within 28 days by Yiru Weng, Tingting Zhou, Honghua Ye

“…Using complete data modeling, the variable selection approach combines two methods: Lasso regression (glmnet package) and collinearity screening (car package). …”
Get full text

Systematic identification and quantification of factors and their interactions with age, sex, and panel wave influencing cognitive function in Korean older adults by Eunmi Kim, Eunmi Kim, Jinkyung Oh, Jinkyung Oh, Jungsoo Gim, Jungsoo Gim, Jungsoo Gim, Iksoo Huh, Iksoo Huh

“…Thus, identifying these factors including their interactions with age, sex, and panel wave and conducting a systematic quantification of their influences on cognitive function are both necessary for developing efficient intervention strategies.MethodsTo identify the influencing factors and their interactions, we applied a systematic stepwise variable selection using 2,535 community-dwelling older adults who participated in the Korean Longitudinal Study of Aging from Wave 5 (2014) to Wave 8 (2020). …”
Get full text

Small molecule metabolites drive plant rhizosphere microbial community assembly patterns by Yanwei Ma, Heqi Wang, Yalong Kang, Tao Wen

“…The ST treatment, enriched with these metabolites, produced 1,032,205 high-quality sequences and exhibited significant shifts in community composition (Adonis, p = 0.001, R = 0.463), with Rhizobium showing higher abundance compared to the control (CK). Variable selection (βNTI >2) drove phylogenetic turnover in ST, while stochastic processes (|βNTI| < 2) dominated in CK. …”
Get full text

Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases by Yuji Miyamoto, Takeshi Nakaura, Mayuko Ohuchi, Katsuhiro Ogawa, Rikako Kato, Yuto Maeda, Kojiro Eto, Masaaki Iwatsuki, Yoshifumi Baba, Toshinori Hirai, Hideo Baba

“…Results: Among the patients, 91 (61%) were responders and 59 (39%) were non-responders. Variable selection with Boruta revealed three key parameters (“DependenceVariance,” “ClusterShade,” and “RunVariance”). …”
Get full text

Development and approval of a Lasso score based on nutritional and inflammatory parameters to predict prognosis in patients with glioma by Huixian Li, Hui Hong, Jinling Zhang

“…A nomogram was constructed utilizing Cox regression and Lasso variable selection. This nomogram incorporated the Lasso score, age, pathological type, chemotherapy status, and Ki67 expression to predict overall survival (OS). …”
Get full text

Development of a Machine‐Learning Model for Diagnosis of Pancreatic Cancer from Serum Samples Analyzed by Thermal Liquid Biopsy by Sonia Hermoso‐Durán, Nicolas Fraunhoffer, Judith Millastre‐Bocos, Oscar Sanchez‐Gracia, Pablo F. Garrido, Sonia Vega, Ángel Lanas, Juan Iovanna, Adrián Velázquez‐Campoy, Olga Abian

“…The generated models are built applying algorithms based on penalized regression, resampling, categorization, cross validation, and variable selection. The ML‐based model demonstrates outstanding ability to discriminate between PDAC patients and control subjects, with a sensitivity of 90% and an area under the ROC receiver operating characteristic curve of 0.83 in the training and test groups. …”
Get full text

Outpatient treatment of worsening heart failure with intravenous and subcutaneous diuretics: a systematic review of the literature by Eric Wierda, Cathelijne Dickhoff, Martin Louis Handoko, Liane Oosterom, Wouter Emmanuel Kok, Y. deRover, B.A.J.M. deMol, Loek vanHeerebeek, Jutta Maria Schroeder‐Tanka

“…Of the 11 included studies 10 were single‐centre, using non‐randomized, observational registries of treatment with intravenous or subcutaneous diuretics for patients with worsening HF with highly variable selection criteria, baseline characteristics, and treatment design. …”
Get full text

Breast cancer risk assessment based on a predictive model: evaluation of risk factors among Japanese women by Michiyo Yamada, Takashi Chishima, Takashi Ishikawa, Kazutaka Narui, Sadatoshi Sugae, Peter J. Tonellato, Itaru Endo

“…Differences were observed between PRE and PERI versus POST with respect to variable selection in parity and FHx. Our models had moderate discriminatory accuracy. …”
Get full text

Development and Validation of a Predictive Model Based on Serum Silent Information Regulator 6 Levels in Chinese Older Adult Patients: Cross-Sectional Descriptive Study by Yuzi You, Wei Liang, Yajie Zhao

“…The nomogram was derived from the Extreme Gradient Boosting (XGBoost) model, with logistic regression for variable selection. Model performance was assessed by examining discrimination, calibration, and clinical use separately. …”
Get full text

Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model by M.I. Rumaling, F.P. Chee, H.W.J. Chang, C.M. Payus, S.K. Kong, J. Dayou, J. Sentian

“…This paper presents the development of PM10 forecasting model using nonlinear autoregressive with exogenous input model.METHODS: To improve performance of nonlinear autoregressive with exogenous input model, principal component analysis is used prior to the model for variable selection. The first stage of principal component analysis involves Scree plot, which determines the number of principal components based on explained variance. …”
Get full text

Health Care Professionals and Data Scientists’ Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Quali... by Joana Seringa, Anna Hirata, Ana Rita Pedro, Rui Santana, Teresa Magalhães

“…The relevance of ML models for improving patient outcomes, reducing health care costs, and promoting patient engagement in disease management is highlighted. Adequate variable selection, risk stratification, and response models were identified as essential components for the effective implementation of ML models in health care. …”
Get full text

Salt tolerance evaluation and key salt-tolerant traits at germination stage of upland cotton by Mengjie An, Xinhui Huang, Yilei Long, Yin Wang, Yanping Tan, Zhen Qin, Xiantao Ai, Yan Wang

“…In practical application, the variable selection for modelling could be adjusted based on the experimental workload. …”
Get full text