Machine learning approaches for predicting 5‐year breast cancer survival: A multicenter study

Nguyen, Quynh Thi Nhu; Nguyen, Phung‐Anh; Wang, Chun‐Jung; Phuc, Phan Thanh; Lin, Ruo‐Kai; Hung, Chin‐Sheng; Kuo, Nei‐Hui; Cheng, Yu‐Wen; Lin, Shwu‐Jiuan; Hsieh, Zong‐You; Cheng, Chi‐Tsun; Hsu, Min‐Huei; Hsu, Jason C.

doi:10.1111/cas.15917

Cited by 8 publications

(1 citation statement)

References 45 publications

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“…The Taipei Medical University Clinical Research Database (TMUCRD) gathers data from multiple centers and sources of various data types. It systematically collects both structured and unstructured data from three affiliated hospitals: Taipei Medical University Hospital, Wanfang Hospital, and Shuangho Hospital (9)(10)(11). The National Health Insurance database in Taiwan has a gap of 2 years in the dissemination of data for research purposes.…”

Section: Introductionmentioning

confidence: 99%

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

Hien,

Tsai,

Chang

et al. 2024

Front. Med.

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BackgroundPrevious studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.ObjectiveThis study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.MethodsData were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.ResultsThe study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.ConclusionThis study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

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Section: Introductionmentioning

confidence: 99%

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

Hien,

Tsai,

Chang

et al. 2024

Front. Med.

Self Cite

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Application of machine learning in breast cancer survival prediction using a multimethod approach

Hamedi,

Emami,

Khayamzadeh

et al. 2024

Sci Rep

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Breast cancer is one of the most prevalent cancers with an increasing trend in both incidence and mortality rates in Iran. Survival analysis is a pivotal measure in setting appropriate care plans. To the best of our knowledge, this study is pioneering in Iran, introducing a multi-method approach using a Deep Neural Network (DNN) and 11 conventional machine learning (ML) methods to predict the 5 year survival of women with breast cancer. Supplying data from two centers comprising a total of 2644 records and incorporating external validation further distinguishes the study. Thirty-four features were selected based on a literature review and common variables in both datasets. Feature selection was also performed using a p value criterion (< 0.05) and a survey involving oncologists. A total of 108 models were trained. According to external validation, the DNN model trained with the Shiraz dataset, considering all features, exhibited the highest accuracy (85.56%). While the DNN model showed superior accuracy in external validation, it did not consistently achieve the highest performance across all evaluation metrics. Notably, models trained with the Shiraz dataset outperformed those trained with the Tehran dataset, possibly due to the lower number of missing values in the Shiraz dataset.

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Predictive Modeling for Breast Cancer Diagnosis and Prognosis: A Review

Samant,

Choudhary,

Ayushi

et al. 2024

2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)

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Machine learning approaches for predicting 5‐year breast cancer survival: A multicenter study

Cited by 8 publications

References 45 publications

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

Application of machine learning in breast cancer survival prediction using a multimethod approach

Predictive Modeling for Breast Cancer Diagnosis and Prognosis: A Review

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