Phenotype Discovery and Geographic Disparities of Late-Stage Breast Cancer Diagnosis across U.S. Counties: A Machine Learning Approach

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

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Proposed approach for breast cancer diagnosis using machine learning

Proceedings of the 4th International Conference on Smart City Applications - SCA '19 ◽

10.1145/3368756.3369089 ◽

2019 ◽

Author(s):

Hajar Saoud ◽

Abderrahim Ghadi ◽

Mohamed Ghailani

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis

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Supervised Machine Learning Approach For The Prediction of Breast Cancer

2020 International Conference on System, Computation, Automation and Networking (ICSCAN) ◽

10.1109/icscan49426.2020.9262403 ◽

2020 ◽

Author(s):

Tarun Jain ◽

Vivek Kumar Verma ◽

Mahek Agarwal ◽

Anju Yadav ◽

Ashish Jain

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Supervised Machine Learning ◽

Learning Approach ◽

Machine Learning Approach

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Receipt of Screening Mammography by Insured Women Diagnosed With Breast Cancer and Impact on Outcomes

Journal of the National Comprehensive Cancer Network ◽

10.6004/jnccn.2020.7801 ◽

2021 ◽

Author(s):

Marissa B. Lawson ◽

Christoph I. Lee ◽

Daniel S. Hippe ◽

Shasank Chennupati ◽

Catherine R. Fedorenko ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

Late Stage ◽

Breast Cancer Diagnosis ◽

Screening Mammography ◽

Socioeconomic Deprivation ◽

Tumor Characteristics ◽

Stage Disease ◽

Increased Risk ◽

Late Stage Disease

Background: The purpose of this study was to determine factors associated with receipt of screening mammography by insured women before breast cancer diagnosis, and subsequent outcomes. Patients and Methods: Using claims data from commercial and federal payers linked to a regional SEER registry, we identified women diagnosed with breast cancer from 2007 to 2017 and determined receipt of screening mammography within 1 year before diagnosis. We obtained patient and tumor characteristics from the SEER registry and assigned each woman a socioeconomic deprivation score based on residential address. Multivariable logistic regression models were used to evaluate associations of patient and tumor characteristics with late-stage disease and nonreceipt of mammography. We used multivariable Cox proportional hazards models to identify predictors of subsequent mortality. Results: Among 7,047 women, 69% (n=4,853) received screening mammography before breast cancer diagnosis. Compared with women who received mammography, those with no mammography had a higher proportion of late-stage disease (34% vs 10%) and higher 5-year mortality (18% vs 6%). In multivariable modeling, late-stage disease was most associated with nonreceipt of mammography (odds ratio [OR], 4.35; 95% CI, 3.80–4.98). The Cox model indicated that nonreceipt of mammography predicted increased risk of mortality (hazard ratio [HR], 2.00; 95% CI, 1.64–2.43), independent of late-stage disease at diagnosis (HR, 5.00; 95% CI, 4.10–6.10), Charlson comorbidity index score ≥1 (HR, 2.75; 95% CI, 2.26–3.34), and negative estrogen receptor/progesterone receptor status (HR, 2.09; 95% CI, 1.67–2.61). Nonreceipt of mammography was associated with younger age (40–49 vs 50–59 years; OR, 1.69; 95% CI, 1.45–1.96) and increased socioeconomic deprivation (OR, 1.05 per decile increase; 95% CI, 1.03–1.07). Conclusions: In a cohort of insured women diagnosed with breast cancer, nonreceipt of screening mammography was significantly associated with late-stage disease and mortality, suggesting that interventions to further increase uptake of screening mammography may improve breast cancer outcomes.

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