Risk Prediction-Based Breast Cancer Diagnosis Using Personal Health Records and Machine Learning Models

2021 ◽  
pp. 445-460
Author(s):  
Sireesha Moturi ◽  
S. N. Tirumala Rao ◽  
Srikanth Vemuru
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Prediction ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Personal Health Records ◽  
Personal Health ◽  
Learning Models ◽  
Health Records ◽  
Machine Learning Models

Early Breast Cancer Diagnosis and Risk Prediction based on Machine Learning

2021 ◽  
Author(s):  
Aryan Mital ◽  
Yogesh ◽  
Namra Shamim ◽  
Bharath Chandra B ◽  
Usha Keshwala
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Risk Prediction ◽  
Early Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis

scholarly journals Predicting breast cancer risk using personal health data and machine learning models

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0226765 ◽  
Author(s):  
Gigi F. Stark ◽  
Gregory R. Hart ◽  
Bradley J. Nartowt ◽  
Jun Deng
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Health Data ◽  
Personal Health ◽  
Learning Models ◽  
Machine Learning Models

Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review

2021 ◽  
Author(s):  
Nghia H Nguyen ◽  
Dominic Picetti ◽  
Parambir S Dulai ◽  
Vipul Jairath ◽  
William J Sandborn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Risk Prediction ◽  
Statistical Models ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Learning Models ◽  
Bowel Diseases ◽  
Inflammatory Bowel ◽  
Machine Learning Models

Abstract Background and Aims There is increasing interest in machine learning-based prediction models in inflammatory bowel diseases (IBD). We synthesized and critically appraised studies comparing machine learning vs. traditional statistical models, using routinely available clinical data for risk prediction in IBD. Methods Through a systematic review till January 1, 2021, we identified cohort studies that derived and/or validated machine learning models, based on routinely collected clinical data in patients with IBD, to predict the risk of harboring or developing adverse clinical outcomes, and reported its predictive performance against a traditional statistical model for the same outcome. We appraised the risk of bias in these studies using the Prediction model Risk of Bias ASsessment (PROBAST) tool. Results We included 13 studies on machine learning-based prediction models in IBD encompassing themes of predicting treatment response to biologics and thiopurines, predicting longitudinal disease activity and complications and outcomes in patients with acute severe ulcerative colitis. The most common machine learnings models used were tree-based algorithms, which are classification approaches achieved through supervised learning. Machine learning models outperformed traditional statistical models in risk prediction. However, most models were at high risk of bias, and only one was externally validated. Conclusions Machine learning-based prediction models based on routinely collected data generally perform better than traditional statistical models in risk prediction in IBD, though frequently have high risk of bias. Future studies examining these approaches are warranted, with special focus on external validation and clinical applicability.

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