Research graph
References from Machine learning for predicting mortality in women diagnosed with breast cancer in the State of Mato Grosso, Brazil using linked population-based cancer registry and mortality data. Local targets link to admitted publications; unresolved targets remain external evidence.
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
2024 · External reference
Current and future burden of breast cancer: global statistics for 2020 and 2040
10.1016/j.breast.2022.08.010 · 2022 · External reference
Breast cancer heterogeneity and its implication in personalized precision therapy
10.1186/s40164-022-00363-1 · 2023 · External reference
Breast cancer in Brazil: epidemiology and treatment challenges
2015 · External reference
Breast cancer mortality in young women in Brazil
10.3389/fonc.2020.569933 · 2021 · External reference
Disparities in female breast cancer mortality rates between urban centers and rural areas of Brazil: ecological time-series study
10.1016/j.breast.2014.01.006 · 2014 · External reference
Effects of the high-inequality of income on the breast cancer mortality in Brazil
10.1038/s41598-019-41012-8 · 2019 · External reference
Nomogram model for predicting the long-term prognosis of cervical cancer patients: a population-based study in Mato Grosso, Brazil
10.1186/s12885-025-14056-5 · 2025 · External reference
Trend in cancer incidence in Mato Grosso and its health regions, Brazil, 2001-2018
10.1186/s13690-025-01503-9 · 2025 · External reference
Incidence and mortality by the main types of cancer in the city of Cuiabá, Mato Grosso, between the years of 2008 and 2016
2022 · External reference
A population-based validation of the prognostic model PREDICT for early breast cancer
10.1016/j.ejso.2011.02.001 · 2011 · External reference
Artificial intelligence and machine learning technologies in cancer care: addressing disparities, bias, and data diversity
10.1158/2159-8290.cd-22-0373 · 2022 · External reference
Unresolved reference
External reference
Unresolved reference
2025 · External reference
Application of machine learning in breast cancer survival prediction using a multimethod approach
10.1038/s41598-024-81734-y · 2024 · External reference
Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms
10.1038/s41598-023-27548-w · 2023 · External reference
Predicting factors for survival of breast cancer patients using machine learning techniques
10.1186/s12911-019-0801-4 · 2019 · External reference
Machine learning for longitudinal mortality risk prediction in patients with malignant neoplasm in São Paulo, Brazil
10.1016/j.ailsci.2023.100061 · 2023 · External reference
Cervical cancer specific survival in Grande Cuiabá, Mato Grosso State, Brazil
2022 · External reference
Unresolved reference
2000 · External reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · External reference
CatBoost: unbiased boosting with categorical features
2018 · External reference
Lightgbm: a highly efficient gradient boosting decision tree
2017 · External reference
Interpretable artificial intelligence (AI) for cervical cancer risk analysis leveraging stacking ensemble and expert knowledge
2025 · External reference
Supervised machine learning in oncology: a clinician’s guide
10.1055/s-0040-1705097 · 2020 · External reference
A guide to cross-validation for artificial intelligence in medical imaging
10.1148/ryai.220232 · 2023 · External reference
Evaluation metrics and statistical tests for machine learning
10.1038/s41598-024-56706-x · 2024 · External reference
Modified Brier score for evaluating prediction accuracy for binary outcomes
10.1177/09622802221122391 · 2022 · External reference
Assessing the performance of prediction models: a framework for traditional and novel measures
10.1097/ede.0b013e3181c30fb2 · 2010 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development
10.1111/cts.70056 · 2024 · External reference
Understanding decision curve analysis in clinical prediction model research
10.1093/postmj/qgae027 · 2024 · External reference
Unresolved reference
External reference
Machine learning approaches to predict 6-month mortality among patients with cancer
10.1001/jamanetworkopen.2019.15997 · 2019 · External reference
Global disparities of cancer and its projected burden in 2050
10.1001/jamanetworkopen.2024.43198 · 2024 · External reference
Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
10.1136/bmjopen-2013-004007 · 2014 · External reference
Decision curve analysis: a discussion
10.1177/0272989x07312725 · 2008 · External reference
No free lunch theorems for optimization
10.1109/4235.585893 · 1997 · External reference
On evaluation metrics for medical applications of artificial intelligence
10.1038/s41598-022-09954-8 · 2022 · External reference
Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance
10.1016/j.landig.2025.100916 · 2025 · External reference
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
10.1371/journal.pone.0118432 · 2015 · External reference
Data-driven survival modeling for breast cancer prognostics: a comparative study with machine learning and traditional survival modeling methods
10.1371/journal.pone.0318167 · 2025 · External reference
Mortality prediction modeling for patients with breast cancer based on explainable machine learning
10.3390/cancers16223799 · 2024 · External reference
Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival
10.1038/s41598-021-86327-7 · 2021 · External reference
Nomograms to estimate long-term overall survival and breast cancer-specific survival of patients with luminal breast cancer
10.18632/oncotarget.7975 · 2016 · External reference
Nomogram construction and survival analysis in T3N0M0 breast cancer: a SEER population-based analysis
10.1038/s41598-025-08518-w · 2025 · External reference
A prognostic nomogram for predicting breast cancer survival based on mammography and AJCC staging
2024 · External reference
Explainable artificial intelligence in breast cancer detection and risk prediction: a systematic scoping review
10.1002/cai2.136 · 2024 · External reference
Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach
10.2147/jmdh.s410301 · 2023 · External reference
Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review
10.1093/jamia/ocad180 · 2023 · External reference
Machine learning approaches to predict 6-month mortality among patients with cancer
10.1001/jamanetworkopen.2019.15997 · ExternalCitation · doi-reference
Global disparities of cancer and its projected burden in 2050
10.1001/jamanetworkopen.2024.43198 · ExternalCitation · doi-reference
Explainable artificial intelligence in breast cancer detection and risk prediction: a systematic scoping review
10.1002/cai2.136 · ExternalCitation · doi-reference
Machine learning for longitudinal mortality risk prediction in patients with malignant neoplasm in São Paulo, Brazil
10.1016/j.ailsci.2023.100061 · ExternalCitation · doi-reference
Disparities in female breast cancer mortality rates between urban centers and rural areas of Brazil: ecological time-series study
10.1016/j.breast.2014.01.006 · ExternalCitation · doi-reference
Current and future burden of breast cancer: global statistics for 2020 and 2040
10.1016/j.breast.2022.08.010 · ExternalCitation · doi-reference
A population-based validation of the prognostic model PREDICT for early breast cancer
10.1016/j.ejso.2011.02.001 · ExternalCitation · doi-reference
Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance
10.1016/j.landig.2025.100916 · ExternalCitation · doi-reference
Effects of the high-inequality of income on the breast cancer mortality in Brazil
10.1038/s41598-019-41012-8 · ExternalCitation · doi-reference
Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival
10.1038/s41598-021-86327-7 · ExternalCitation · doi-reference
On evaluation metrics for medical applications of artificial intelligence
10.1038/s41598-022-09954-8 · ExternalCitation · doi-reference
Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms
10.1038/s41598-023-27548-w · ExternalCitation · doi-reference
Evaluation metrics and statistical tests for machine learning
10.1038/s41598-024-56706-x · ExternalCitation · doi-reference
Application of machine learning in breast cancer survival prediction using a multimethod approach
10.1038/s41598-024-81734-y · ExternalCitation · doi-reference
Nomogram construction and survival analysis in T3N0M0 breast cancer: a SEER population-based analysis
10.1038/s41598-025-08518-w · ExternalCitation · doi-reference
Supervised machine learning in oncology: a clinician’s guide
10.1055/s-0040-1705097 · ExternalCitation · doi-reference
Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review
10.1093/jamia/ocad180 · ExternalCitation · doi-reference
Understanding decision curve analysis in clinical prediction model research
10.1093/postmj/qgae027 · ExternalCitation · doi-reference
Assessing the performance of prediction models: a framework for traditional and novel measures
10.1097/ede.0b013e3181c30fb2 · ExternalCitation · doi-reference
No free lunch theorems for optimization
10.1109/4235.585893 · ExternalCitation · doi-reference
Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development
10.1111/cts.70056 · ExternalCitation · doi-reference
Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
10.1136/bmjopen-2013-004007 · ExternalCitation · doi-reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · ExternalCitation · doi-reference
A guide to cross-validation for artificial intelligence in medical imaging
10.1148/ryai.220232 · ExternalCitation · doi-reference
Artificial intelligence and machine learning technologies in cancer care: addressing disparities, bias, and data diversity
10.1158/2159-8290.cd-22-0373 · ExternalCitation · doi-reference
Decision curve analysis: a discussion
10.1177/0272989x07312725 · ExternalCitation · doi-reference
Modified Brier score for evaluating prediction accuracy for binary outcomes
10.1177/09622802221122391 · ExternalCitation · doi-reference
Nomogram model for predicting the long-term prognosis of cervical cancer patients: a population-based study in Mato Grosso, Brazil
10.1186/s12885-025-14056-5 · ExternalCitation · doi-reference
Predicting factors for survival of breast cancer patients using machine learning techniques
10.1186/s12911-019-0801-4 · ExternalCitation · doi-reference
Trend in cancer incidence in Mato Grosso and its health regions, Brazil, 2001-2018
10.1186/s13690-025-01503-9 · ExternalCitation · doi-reference
Breast cancer heterogeneity and its implication in personalized precision therapy
10.1186/s40164-022-00363-1 · ExternalCitation · doi-reference
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
10.1371/journal.pone.0118432 · ExternalCitation · doi-reference
Data-driven survival modeling for breast cancer prognostics: a comparative study with machine learning and traditional survival modeling methods
10.1371/journal.pone.0318167 · ExternalCitation · doi-reference
Nomograms to estimate long-term overall survival and breast cancer-specific survival of patients with luminal breast cancer
10.18632/oncotarget.7975 · ExternalCitation · doi-reference
Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach
10.2147/jmdh.s410301 · ExternalCitation · doi-reference
Breast cancer mortality in young women in Brazil
10.3389/fonc.2020.569933 · ExternalCitation · doi-reference
Mortality prediction modeling for patients with breast cancer based on explainable machine learning
10.3390/cancers16223799 · ExternalCitation · doi-reference
