References and further reading

Ayer T, Alagoz O, Chhatwal J, Shavlik JW, Kahn Jr. CE, Burnside ES (2010). Breast Cancer Risk Estimation with Artificial Neural Networks Revisited: Discrimination and Calibration. Cancer 116 (14): 3310-3321.

Baines CJ (1989). Breast self-examination. Cancer 64(12 Suppl): 2661-2663.

Cruz JA, Wishart DS (2006). Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Informatics 2: 59-77.

D'Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB (2008). General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study. Circulation 117 (6): 743-753.

Donze J, Aujesky D, Williams D, Schnipper JL (2013). Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med 173(8): 632-638.

Donze JD, Williams MV, Robinson EJ et al. (2016). International Validity of the "HOSPITAL" Score to Predict 30-day Potentially Avoidable Readmissions in Medical Patients. JAMA Intern Med 176(4): 496-502.

Framingham Heart Study (2018a). History of the Framingham Heart Study. https://www.framinghamheartstudy.org/fhs-about/history/. Accessed June 16, 2018.

Framingham Heart Study (2018b). Research Milestones. https://www.framinghamheartstudy.org/fhs-about/research-milestones/. Accessed June 16, 2018.

Futoma J, Morris J, Lucas J (2015). A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics 56: 229-238.

Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal 13: 8-17.

Lenes K (2005). File: Echocardiogram 4chambers.jpg. https://commons.wikimedia.org/wiki/File:Echocardiogram_4chambers.jpg. Accessed June 23, 2018.

Longo DL (2005). "Approach to the Patient with Cancer." In Kasper DL, Braunwald E, Fauci AS, Hauser SL, Longo DL, Jameson JL. eds. Harrison's Principles of Internal Medicine, 16e. New York, NY: McGraw-Hill.

Morin PJ, Trent JM, Collins FS, Vogelstein B (2005). "Cancer Genetics." In Kasper DL, Braunwald E, Fauci AS, Hauser SL, Longo DL, Jameson JL. eds. Harrison's Principles of Internal Medicine, 16e. New York, NY: McGraw-Hill.

National Cancer Institute (1990). Mammogram Showing Cancer. Unknown Photographer, American College of Radiology. https://commons.wikimedia.org/wiki/File:Mammogram_showing_cancer.jpg. Accessed June 23, 2018.

National Cancer Institute (2015a). Breast Cancer Screening (PDQ®)-Health Professional Version. https://www.cancer.gov/types/breast/hp/breast-screening-pdq#section/all. Accessed June 23, 2018.

National Cancer Institute (2015b). Risk Factors for Cancer. https://www.cancer.gov/about-cancer/causes-prevention/risk. Accessed June 23, 2018.

National Heart Lung and Blood Institute (2013). File:Cardiac_Mri.jpg. https://commons.wikimedia.org/wiki/File:Cardiac_mri.jpg. Accessed June 23, 2018.

Son C, Kim Y, Kim H, Park H, Kim M (2012). Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches. Journal of Biomedical Informatics 45: 999-1008.

Tong L, Erdmann C, Daldalian M, Li J, Esposito T (2016). Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk. BMC Medical Research Methodology 2016(16): 26.

Tripoliti EE, Papadopoulos TG, Karanasiou GS, Naka KK, Fotiadis DI (2017). Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques. Computational and Structural Biotechnology Journal 15: 26-47.

US Preventive Services Task Force (2016). Final Recommendation Statement: Breast Cancer: Screening. https://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/breast-cancer-screening1. Accessed June 23, 2018.

van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, Austin PC, Forster AJ (2010). Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. Canadian Medical Association Journal 182(6): 551-557.

Wang X, Huang Y, Li L, Dai H, Song F, Chen K (2018). Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis. Breast Cancer Research 20: 18.

Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS One 12(4): e0174944. https://doi.org/10.1371/journal.pone.0174944

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