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    Genovis Inc selection operator lgbm light gradient boosting machine lime local interpretable model
    Selection Operator Lgbm Light Gradient Boosting Machine Lime Local Interpretable Model, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 92 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/gradient+boosting/OpeRATOR+Lyophilized/pm41286838-318-49-50
    Average 93 stars, based on 92 article reviews
    selection operator lgbm light gradient boosting machine lime local interpretable model - by Bioz Stars, 2026-10
    93/100 stars

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    Selection:

    Article Title: Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS.
    Article Snippet: .. CHARLS China Health and Retirement Longitudinal Study LASSO Least Absolute Shrinkage and Selection Operator ML Machine learning CVD Cardiovascular disease FPG Fasting plasma glucose TC Total cholesterol HDL-C Low high-density lipoprotein cholesterol LR Logistic regression RF Random Forest XGBoost Extreme Gradient Boosting SVM Support Vector Machine KNN K-nearest neighbors LightGBM Light Gradient Boosting Machine ROC Receiver operator characteristic DCA Decision curve analysis CESD-10 Center for Epidemiologic Studies Depression Scale-10 CAPI Computer-assisted personal interviewing Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . ..

    Article Title: Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.
    Article Snippet: .. 7Zunfeng Fu and Jing Wang have contributed equally to this work. email: lvchunju123@163.com; penglin0125@163.com OPEN Scientific Reports | (2025) 15:23595 1| https://doi.org/10.1038/s41598-025-09115-7 www.nature.com/scientificreports BMI Body mass index CI Confidence interval CNN Convolutional neural networks CPP Cerebral perfusion pressure DC Decompressive craniectomy DCA Decision curve analysis DICOM Digital imaging and communications in medicine DTL Deep transfer learning DLR Deep learning radiomics EDV End-diastolic velocity (MCA) EF Ejection fraction GCS Glasgow coma scale GLCM Gray-level co-occurrence matrix GLRLM Gray-level run-length matrix GLSZM Gray-level size zone matrix GLDM Gray-level dependence matrix ICC Intraclass correlation coefficient ICP Intracranial pressure IH Intracranial hypertension KNN KNEAREST neighbors ICPi Invasive ICP LASSO Least absolute shrinkage and selection operator Light GBM Light Gradient Boosting Machine MCASDI Spectral doppler imaging of middle cerebral artery MAP Mean arterial pressure MCA Middle cerebral artery MCA_mean Mean velocity (MCA) MCAED End-dystolic velocity of MCA MCA: middle cerebral artery MCAPS Peak systolic velocity of MCA MCAPI Pulse index (middle cerebral artery) NPV Negative predictive value ONS Optic nerve sheath ONSD Optic nerve sheath diameter ORs Odds ratios PaO2 Oxygen partial pressure (Artery) PaCO2 Carbon dioxide partial pressure (Artery) PACS Picture archiving and communication systems PI Pulse index PPV Positive predictive value RF Random forest ROC Receiver operating characteristic ROI Region of interest TBI Traumatic brain injury TCD Transcranial Doppler TCCD Transcranial color doppler SNR Signal-to-noise ratio CNR Contrast-to-noise ratio PRF Pulse repetition frequency FPS Frames per second Accurate early prediction of intracranial hypertension (IH) is critical in the management of brain injury patients, as it can lead to brain herniation and pose a significant risk of irreversible brain damage and death1,2. ..

    Article Title: Estimation Method of Leaf Nitrogen Content of Dominant Plants in Inner Mongolia Grassland Based on Machine Learning
    Article Snippet: .. Abbreviations The following abbreviations are used in this manuscript: N Nitrogen ASD Analytical Spectral Devices FDT First-order Derivative Transformation CWT Continuous Wavelet Transformation CRT Continuum Removal Transformation LASSO Least Absolute Shrinkage and Selection Operator XGBoost Extreme Gradient Boosting SVM Support Vector Machine ANN Artificial Neural Network KNN K-Nearest Neighbors CNC Canopy Nitrogen Concentration LNC Leaf Nitrogen Content SMLR Stepped-Multiple Linear Regression SVR Support Vector Regression GA-ELM Genetic Algorithm–Extreme Learning Machine VI Vegetation Indices Max Maximum Min Minimum Mean Arithmetic Mean SD Standard Deviation CV Coefficient of Variation ..

    Article Title: Artificial intelligence in polycystic ovary syndrome: a systematic review of diagnostic and predictive applications.
    Article Snippet: .. Abbreviations AI Artificial Intelligence ANN Artificial Neural Network AUC Area Under the Curve CNN Convolutional Neural Network CV Cross-Validation DL Deep Learning DT Decision Tree HER Electronic Health Record EMR Electronic Medical Record GB Gradient Boosting GBDT Gradient Boosting Decision Tree KNN k-Nearest Neighbors LASSO Least Absolute Shrinkage and Selection Operator LGBM Light Gradient Boosting Machine LIME Local Interpretable Model-Agnostic Explanations LLM Large Language Model LR Logistic Regression ML Machine Learning MLP Multilayer Perceptron NB Naïve Bayes NN Neural Network PCOS Polycystic Ovary Syndrome PCA Principal Component Analysis PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses QUADAS-2 Quality Assessment of Diagnostic Accuracy Studies-2 RF Random Forest RNN Recurrent Neural Network ROBIS Risk of Bias in Systematic Reviews ROC Receiver Operating Characteristic SHAP Shapley Additive Explanations SVM Support Vector Machine TL Transfer Learning XAI Explainable Artificial Intelligence XGB Extreme Gradient Boosting MRI Magnetic Resonance Imaging ..

    Article Title: A Novel Lumbar Disc Herniation Classification Based on Endplate Destruction.
    Article Snippet: .. 479 480 481 Jo urn al Pr e-p roo f Table 3 The intra-observer agreement of the classifications Observer k (95% CI) Agreement Reader 1 0.863(0.767-0.959) almost perfect Reader 2 0.793(0.697-0.889) Substantial Table 4 Clinical manifestations of the classifications A B C P VAS First follow-up VAS 7.30±1.80 6.63±1.96 5.53±1.68 <0.05a,b,c Last follow-up VAS 0.46±0.95 0.72±1.03 0.40±0.62 0.139 ODI First follow-up ODI 31.16±11.06 25.89±9.13 27.10±9.89 0.002a Last follow-up ODI 7.14±5.51 8.64±6.36 9.16±5.42 0.080 aP, P<0.05 between type A and type B bP, P<0.05 between type B and type C cP, P<0.05 between type A and type C Jo urn al Pr e-p roo f Table 6 Multiple linear regression analysis of first follow-up VAS and ODI Model Unstandardized coefficients Standardized coefficients B S.E. β t P First follow-up VAS (model 1) Constant 6.954 0.265 26.285 <0.001 Type A 0.618 0.27 0.157 2.293 0.023 Type C -1.162 0.402 -0.198 -2.891 0.004 Gender -0.553 0.23 -0.145 -2.403 0.017 First follow-up ODI (model 2) Constant 45.373 5.94 7.638 <0.001 Type A 5.207 1.53 0.236 3.403 0.001 Type C 1.397 2.274 0.043 0.614 0.54 Gender -4.063 1.314 -0.19 -3.092 0.002 Levels -2.994 1.088 -0.175 -2.751 0.006 Age -0.094 0.047 -0.125 -1.988 0.048 Table 7 Multiple linear regression analysis of the last follow-up VAS and ODI Model Unstandardized coefficients Standardized coefficients B S.E. β t P the last follow-up VAS (model 3) Constant 1.001 0.214 4.671 <0.001 Type A -0.233 0.136 -0.12 -1.719 0.087 Type C -0.264 0.202 -0.091 -1.308 0.192 Diabetes 1.143 0.281 0.26 4.074 <0.001 age -0.008 0.004 -0.127 -1.992 0.048 the last follow-up Constant 7.349 0.635 11.567 <0.001 Jo urn al Pr e-p roo f ODI (model 4) Type A 0.688 0.744 0.055 0.925 0.356 Type C 1.267 1.108 0.068 1.143 0.254 Diabetes 16.535 1.484 0.581 11.145 <0.001 Jo urn al Pr e-p roo f Jo urn al Pr e-p roo f Jo urn al Pr e-p roo f Jo urn al Pr e-p roo f Jo urn al Pr e-p roo f Abbreviations and Acronyms: LDH:Lumbar disc herniation ODI:Oswestry Disability Index VAS:Visual Analog Scale Lasso: Least Absolute Shrinkage and Selection Operator XGBoost: Extreme Gradient Boosting VSF: Variable Selection Frequency ROC: Receiver Operating Characteristic AUC: Area Under the Curve Jo urn al Pr e-p roo f Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ..

    Article Title: A Novel Lumbar Disc Herniation Classification Based on Endplate Destruction.
    Article Snippet: .. 234 Figure 5 Machine Learning and Validation (a-b) Least Absolute Shrinkage and Selection Operator (c) 235 Extreme Gradient Boosting (d) Variable Selection Frequency (e) Nomogram (f) Receiver Operating 236 Characteristic 237 6. ..

    Article Title: Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.
    Article Snippet: .. Abbreviations AUC Area Under the Curve CDC Centers for Disease Control and Prevention CDM Common data model EHE Ending the HIV Epidemic EHR Electronic health record FDA Food and Drug Administration FROST Florida Drug-Related Outcomes Surveillance and Tracking HIPAA Health Insurance Portability and Accountability Act HIV Human immunodeficiency virus HPV Human papillomavirus ICD International Classification of Diseases IRB Institutional Review Board LASSO Least Absolute Shrinkage and Selection Operator LGBM Light Gradient Boosting Machine ML Machine learning NPV Negative predicted value PCORI Patient-Centered Outcomes Research Institute PHI Protected health information PPV Positive predicted value PrEP Pre-exposure prophylaxis RF Random Forest ROC Receiver Operating Characteristic SD Standard deviation SDoH Social determinants of health SHAP Shapley Additive Explanations STI Sexually transmitted infection SVC Support Vector Classification TRIPOD Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis US United States XGB eXtreme Gradient Boosting Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . ..

    Article Title: Identification of a New Lung Cancer Biomarker Signature Using Data Mining and Preliminary In Vitro Validation
    Article Snippet: .. Abbreviations The following abbreviations are used in this manuscript: LUAD Lung adenocarcinoma NSCLC Non-small cell lung cancer ML Machine learning SMOTE Synthetic Minority Oversampling Technique GEO Gene Expression Omnibus RMA Robust Multi-array Average DE Differentially expressed mRMR minimum Redundancy Maximum Relevance LASSO Least Absolute Shrinkage and Selection Operator SVM Support Vector Machine RF Random Forest LR Logistic Regression XGBoost Extreme Gradient Boosting GB Gradient Boosting AB AdaBoost ET Extra Trees kNN k-Nearest Neighbors LDA Linear Discriminant Analysis ..

    Clinical Proteomics:

    Article Title: Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS.
    Article Snippet: .. CHARLS China Health and Retirement Longitudinal Study LASSO Least Absolute Shrinkage and Selection Operator ML Machine learning CVD Cardiovascular disease FPG Fasting plasma glucose TC Total cholesterol HDL-C Low high-density lipoprotein cholesterol LR Logistic regression RF Random Forest XGBoost Extreme Gradient Boosting SVM Support Vector Machine KNN K-nearest neighbors LightGBM Light Gradient Boosting Machine ROC Receiver operator characteristic DCA Decision curve analysis CESD-10 Center for Epidemiologic Studies Depression Scale-10 CAPI Computer-assisted personal interviewing Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . ..

    Imaging:

    Article Title: Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.
    Article Snippet: .. 7Zunfeng Fu and Jing Wang have contributed equally to this work. email: lvchunju123@163.com; penglin0125@163.com OPEN Scientific Reports | (2025) 15:23595 1| https://doi.org/10.1038/s41598-025-09115-7 www.nature.com/scientificreports BMI Body mass index CI Confidence interval CNN Convolutional neural networks CPP Cerebral perfusion pressure DC Decompressive craniectomy DCA Decision curve analysis DICOM Digital imaging and communications in medicine DTL Deep transfer learning DLR Deep learning radiomics EDV End-diastolic velocity (MCA) EF Ejection fraction GCS Glasgow coma scale GLCM Gray-level co-occurrence matrix GLRLM Gray-level run-length matrix GLSZM Gray-level size zone matrix GLDM Gray-level dependence matrix ICC Intraclass correlation coefficient ICP Intracranial pressure IH Intracranial hypertension KNN KNEAREST neighbors ICPi Invasive ICP LASSO Least absolute shrinkage and selection operator Light GBM Light Gradient Boosting Machine MCASDI Spectral doppler imaging of middle cerebral artery MAP Mean arterial pressure MCA Middle cerebral artery MCA_mean Mean velocity (MCA) MCAED End-dystolic velocity of MCA MCA: middle cerebral artery MCAPS Peak systolic velocity of MCA MCAPI Pulse index (middle cerebral artery) NPV Negative predictive value ONS Optic nerve sheath ONSD Optic nerve sheath diameter ORs Odds ratios PaO2 Oxygen partial pressure (Artery) PaCO2 Carbon dioxide partial pressure (Artery) PACS Picture archiving and communication systems PI Pulse index PPV Positive predictive value RF Random forest ROC Receiver operating characteristic ROI Region of interest TBI Traumatic brain injury TCD Transcranial Doppler TCCD Transcranial color doppler SNR Signal-to-noise ratio CNR Contrast-to-noise ratio PRF Pulse repetition frequency FPS Frames per second Accurate early prediction of intracranial hypertension (IH) is critical in the management of brain injury patients, as it can lead to brain herniation and pose a significant risk of irreversible brain damage and death1,2. ..

    Immunocytochemistry:

    Article Title: Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.
    Article Snippet: .. 7Zunfeng Fu and Jing Wang have contributed equally to this work. email: lvchunju123@163.com; penglin0125@163.com OPEN Scientific Reports | (2025) 15:23595 1| https://doi.org/10.1038/s41598-025-09115-7 www.nature.com/scientificreports BMI Body mass index CI Confidence interval CNN Convolutional neural networks CPP Cerebral perfusion pressure DC Decompressive craniectomy DCA Decision curve analysis DICOM Digital imaging and communications in medicine DTL Deep transfer learning DLR Deep learning radiomics EDV End-diastolic velocity (MCA) EF Ejection fraction GCS Glasgow coma scale GLCM Gray-level co-occurrence matrix GLRLM Gray-level run-length matrix GLSZM Gray-level size zone matrix GLDM Gray-level dependence matrix ICC Intraclass correlation coefficient ICP Intracranial pressure IH Intracranial hypertension KNN KNEAREST neighbors ICPi Invasive ICP LASSO Least absolute shrinkage and selection operator Light GBM Light Gradient Boosting Machine MCASDI Spectral doppler imaging of middle cerebral artery MAP Mean arterial pressure MCA Middle cerebral artery MCA_mean Mean velocity (MCA) MCAED End-dystolic velocity of MCA MCA: middle cerebral artery MCAPS Peak systolic velocity of MCA MCAPI Pulse index (middle cerebral artery) NPV Negative predictive value ONS Optic nerve sheath ONSD Optic nerve sheath diameter ORs Odds ratios PaO2 Oxygen partial pressure (Artery) PaCO2 Carbon dioxide partial pressure (Artery) PACS Picture archiving and communication systems PI Pulse index PPV Positive predictive value RF Random forest ROC Receiver operating characteristic ROI Region of interest TBI Traumatic brain injury TCD Transcranial Doppler TCCD Transcranial color doppler SNR Signal-to-noise ratio CNR Contrast-to-noise ratio PRF Pulse repetition frequency FPS Frames per second Accurate early prediction of intracranial hypertension (IH) is critical in the management of brain injury patients, as it can lead to brain herniation and pose a significant risk of irreversible brain damage and death1,2. ..

    Transformation Assay:

    Article Title: Estimation Method of Leaf Nitrogen Content of Dominant Plants in Inner Mongolia Grassland Based on Machine Learning
    Article Snippet: .. Abbreviations The following abbreviations are used in this manuscript: N Nitrogen ASD Analytical Spectral Devices FDT First-order Derivative Transformation CWT Continuous Wavelet Transformation CRT Continuum Removal Transformation LASSO Least Absolute Shrinkage and Selection Operator XGBoost Extreme Gradient Boosting SVM Support Vector Machine ANN Artificial Neural Network KNN K-Nearest Neighbors CNC Canopy Nitrogen Concentration LNC Leaf Nitrogen Content SMLR Stepped-Multiple Linear Regression SVR Support Vector Regression GA-ELM Genetic Algorithm–Extreme Learning Machine VI Vegetation Indices Max Maximum Min Minimum Mean Arithmetic Mean SD Standard Deviation CV Coefficient of Variation ..

    Concentration Assay:

    Article Title: Estimation Method of Leaf Nitrogen Content of Dominant Plants in Inner Mongolia Grassland Based on Machine Learning
    Article Snippet: .. Abbreviations The following abbreviations are used in this manuscript: N Nitrogen ASD Analytical Spectral Devices FDT First-order Derivative Transformation CWT Continuous Wavelet Transformation CRT Continuum Removal Transformation LASSO Least Absolute Shrinkage and Selection Operator XGBoost Extreme Gradient Boosting SVM Support Vector Machine ANN Artificial Neural Network KNN K-Nearest Neighbors CNC Canopy Nitrogen Concentration LNC Leaf Nitrogen Content SMLR Stepped-Multiple Linear Regression SVR Support Vector Regression GA-ELM Genetic Algorithm–Extreme Learning Machine VI Vegetation Indices Max Maximum Min Minimum Mean Arithmetic Mean SD Standard Deviation CV Coefficient of Variation ..

    Standard Deviation:

    Article Title: Estimation Method of Leaf Nitrogen Content of Dominant Plants in Inner Mongolia Grassland Based on Machine Learning
    Article Snippet: .. Abbreviations The following abbreviations are used in this manuscript: N Nitrogen ASD Analytical Spectral Devices FDT First-order Derivative Transformation CWT Continuous Wavelet Transformation CRT Continuum Removal Transformation LASSO Least Absolute Shrinkage and Selection Operator XGBoost Extreme Gradient Boosting SVM Support Vector Machine ANN Artificial Neural Network KNN K-Nearest Neighbors CNC Canopy Nitrogen Concentration LNC Leaf Nitrogen Content SMLR Stepped-Multiple Linear Regression SVR Support Vector Regression GA-ELM Genetic Algorithm–Extreme Learning Machine VI Vegetation Indices Max Maximum Min Minimum Mean Arithmetic Mean SD Standard Deviation CV Coefficient of Variation ..

    Article Title: Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.
    Article Snippet: .. Abbreviations AUC Area Under the Curve CDC Centers for Disease Control and Prevention CDM Common data model EHE Ending the HIV Epidemic EHR Electronic health record FDA Food and Drug Administration FROST Florida Drug-Related Outcomes Surveillance and Tracking HIPAA Health Insurance Portability and Accountability Act HIV Human immunodeficiency virus HPV Human papillomavirus ICD International Classification of Diseases IRB Institutional Review Board LASSO Least Absolute Shrinkage and Selection Operator LGBM Light Gradient Boosting Machine ML Machine learning NPV Negative predicted value PCORI Patient-Centered Outcomes Research Institute PHI Protected health information PPV Positive predicted value PrEP Pre-exposure prophylaxis RF Random Forest ROC Receiver Operating Characteristic SD Standard deviation SDoH Social determinants of health SHAP Shapley Additive Explanations STI Sexually transmitted infection SVC Support Vector Classification TRIPOD Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis US United States XGB eXtreme Gradient Boosting Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . ..

    Diagnostic Assay:

    Article Title: Artificial intelligence in polycystic ovary syndrome: a systematic review of diagnostic and predictive applications.
    Article Snippet: .. Abbreviations AI Artificial Intelligence ANN Artificial Neural Network AUC Area Under the Curve CNN Convolutional Neural Network CV Cross-Validation DL Deep Learning DT Decision Tree HER Electronic Health Record EMR Electronic Medical Record GB Gradient Boosting GBDT Gradient Boosting Decision Tree KNN k-Nearest Neighbors LASSO Least Absolute Shrinkage and Selection Operator LGBM Light Gradient Boosting Machine LIME Local Interpretable Model-Agnostic Explanations LLM Large Language Model LR Logistic Regression ML Machine Learning MLP Multilayer Perceptron NB Naïve Bayes NN Neural Network PCOS Polycystic Ovary Syndrome PCA Principal Component Analysis PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses QUADAS-2 Quality Assessment of Diagnostic Accuracy Studies-2 RF Random Forest RNN Recurrent Neural Network ROBIS Risk of Bias in Systematic Reviews ROC Receiver Operating Characteristic SHAP Shapley Additive Explanations SVM Support Vector Machine TL Transfer Learning XAI Explainable Artificial Intelligence XGB Extreme Gradient Boosting MRI Magnetic Resonance Imaging ..

    Magnetic Resonance Imaging:

    Article Title: Artificial intelligence in polycystic ovary syndrome: a systematic review of diagnostic and predictive applications.
    Article Snippet: .. Abbreviations AI Artificial Intelligence ANN Artificial Neural Network AUC Area Under the Curve CNN Convolutional Neural Network CV Cross-Validation DL Deep Learning DT Decision Tree HER Electronic Health Record EMR Electronic Medical Record GB Gradient Boosting GBDT Gradient Boosting Decision Tree KNN k-Nearest Neighbors LASSO Least Absolute Shrinkage and Selection Operator LGBM Light Gradient Boosting Machine LIME Local Interpretable Model-Agnostic Explanations LLM Large Language Model LR Logistic Regression ML Machine Learning MLP Multilayer Perceptron NB Naïve Bayes NN Neural Network PCOS Polycystic Ovary Syndrome PCA Principal Component Analysis PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses QUADAS-2 Quality Assessment of Diagnostic Accuracy Studies-2 RF Random Forest RNN Recurrent Neural Network ROBIS Risk of Bias in Systematic Reviews ROC Receiver Operating Characteristic SHAP Shapley Additive Explanations SVM Support Vector Machine TL Transfer Learning XAI Explainable Artificial Intelligence XGB Extreme Gradient Boosting MRI Magnetic Resonance Imaging ..

    Biomarker Discovery:

    Article Title: A Novel Lumbar Disc Herniation Classification Based on Endplate Destruction.
    Article Snippet: .. 234 Figure 5 Machine Learning and Validation (a-b) Least Absolute Shrinkage and Selection Operator (c) 235 Extreme Gradient Boosting (d) Variable Selection Frequency (e) Nomogram (f) Receiver Operating 236 Characteristic 237 6. ..

    Article Title: Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.
    Article Snippet: .. Abbreviations AUC Area Under the Curve CDC Centers for Disease Control and Prevention CDM Common data model EHE Ending the HIV Epidemic EHR Electronic health record FDA Food and Drug Administration FROST Florida Drug-Related Outcomes Surveillance and Tracking HIPAA Health Insurance Portability and Accountability Act HIV Human immunodeficiency virus HPV Human papillomavirus ICD International Classification of Diseases IRB Institutional Review Board LASSO Least Absolute Shrinkage and Selection Operator LGBM Light Gradient Boosting Machine ML Machine learning NPV Negative predicted value PCORI Patient-Centered Outcomes Research Institute PHI Protected health information PPV Positive predicted value PrEP Pre-exposure prophylaxis RF Random Forest ROC Receiver Operating Characteristic SD Standard deviation SDoH Social determinants of health SHAP Shapley Additive Explanations STI Sexually transmitted infection SVC Support Vector Classification TRIPOD Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis US United States XGB eXtreme Gradient Boosting Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . ..

    Control:

    Article Title: Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.
    Article Snippet: .. Abbreviations AUC Area Under the Curve CDC Centers for Disease Control and Prevention CDM Common data model EHE Ending the HIV Epidemic EHR Electronic health record FDA Food and Drug Administration FROST Florida Drug-Related Outcomes Surveillance and Tracking HIPAA Health Insurance Portability and Accountability Act HIV Human immunodeficiency virus HPV Human papillomavirus ICD International Classification of Diseases IRB Institutional Review Board LASSO Least Absolute Shrinkage and Selection Operator LGBM Light Gradient Boosting Machine ML Machine learning NPV Negative predicted value PCORI Patient-Centered Outcomes Research Institute PHI Protected health information PPV Positive predicted value PrEP Pre-exposure prophylaxis RF Random Forest ROC Receiver Operating Characteristic SD Standard deviation SDoH Social determinants of health SHAP Shapley Additive Explanations STI Sexually transmitted infection SVC Support Vector Classification TRIPOD Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis US United States XGB eXtreme Gradient Boosting Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . ..

    Virus:

    Article Title: Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.
    Article Snippet: .. Abbreviations AUC Area Under the Curve CDC Centers for Disease Control and Prevention CDM Common data model EHE Ending the HIV Epidemic EHR Electronic health record FDA Food and Drug Administration FROST Florida Drug-Related Outcomes Surveillance and Tracking HIPAA Health Insurance Portability and Accountability Act HIV Human immunodeficiency virus HPV Human papillomavirus ICD International Classification of Diseases IRB Institutional Review Board LASSO Least Absolute Shrinkage and Selection Operator LGBM Light Gradient Boosting Machine ML Machine learning NPV Negative predicted value PCORI Patient-Centered Outcomes Research Institute PHI Protected health information PPV Positive predicted value PrEP Pre-exposure prophylaxis RF Random Forest ROC Receiver Operating Characteristic SD Standard deviation SDoH Social determinants of health SHAP Shapley Additive Explanations STI Sexually transmitted infection SVC Support Vector Classification TRIPOD Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis US United States XGB eXtreme Gradient Boosting Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . ..

    Infection:

    Article Title: Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.
    Article Snippet: .. Abbreviations AUC Area Under the Curve CDC Centers for Disease Control and Prevention CDM Common data model EHE Ending the HIV Epidemic EHR Electronic health record FDA Food and Drug Administration FROST Florida Drug-Related Outcomes Surveillance and Tracking HIPAA Health Insurance Portability and Accountability Act HIV Human immunodeficiency virus HPV Human papillomavirus ICD International Classification of Diseases IRB Institutional Review Board LASSO Least Absolute Shrinkage and Selection Operator LGBM Light Gradient Boosting Machine ML Machine learning NPV Negative predicted value PCORI Patient-Centered Outcomes Research Institute PHI Protected health information PPV Positive predicted value PrEP Pre-exposure prophylaxis RF Random Forest ROC Receiver Operating Characteristic SD Standard deviation SDoH Social determinants of health SHAP Shapley Additive Explanations STI Sexually transmitted infection SVC Support Vector Classification TRIPOD Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis US United States XGB eXtreme Gradient Boosting Supplementary Information The online version contains supplementary material available at h t t p s : / / d o i . o r g / 1 0 . ..

    Gene Expression:

    Article Title: Identification of a New Lung Cancer Biomarker Signature Using Data Mining and Preliminary In Vitro Validation
    Article Snippet: .. Abbreviations The following abbreviations are used in this manuscript: LUAD Lung adenocarcinoma NSCLC Non-small cell lung cancer ML Machine learning SMOTE Synthetic Minority Oversampling Technique GEO Gene Expression Omnibus RMA Robust Multi-array Average DE Differentially expressed mRMR minimum Redundancy Maximum Relevance LASSO Least Absolute Shrinkage and Selection Operator SVM Support Vector Machine RF Random Forest LR Logistic Regression XGBoost Extreme Gradient Boosting GB Gradient Boosting AB AdaBoost ET Extra Trees kNN k-Nearest Neighbors LDA Linear Discriminant Analysis ..



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