scholarly journals An Artificial Intelligence-Enabled Pipeline for Medical Domain: Malaysian Breast Cancer Survivorship Cohort as a Case Study

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1492
Author(s):  
Mogana Darshini Ganggayah ◽  
Sarinder Kaur Dhillon ◽  
Tania Islam ◽  
Foad Kalhor ◽  
Teh Chean Chiang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Automated Scoring

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.

scholarly journals Machine learning models in breast cancer survival prediction

2016 ◽  
Vol 24 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Mitra Montazeri ◽  
Mohadeseh Montazeri ◽  
Mahdieh Montazeri ◽  
Amin Beigzadeh
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Survival Prediction ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Challenges and Perspectives on Breast Cancer Survivorship: The Journey Continues

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 96-96
Author(s):  
Victoria Raveis ◽  
Simona Kwon
Keyword(s):  
Breast Cancer ◽  
Quality Of Life ◽  
Cancer Survivor ◽  
Breast Cancer Survivors ◽  
Cancer Survival ◽  
Survival Rates ◽  
Extended Period ◽  
Health Issues ◽  
Age Related

Abstract Women have a 1-in-8 lifetime risk of breast cancer. Earlier diagnosis and treatment advances have improved 15- and 20-year survival rates. Increased survival can mean coping with the effects of cancer and its treatment over an extended period of time, while experiencing age-related changes in functioning and the emergence of other health issues. To explore breast cancer survivors’ perspectives on their issues and concerns across the life-course, focus groups were conducted with a culturally diverse sample (N=18) of survivors (72% white, 28% Black, 11% Hispanic). Participants were 44-82 years old. Most, 83% were 50 and older, 56% were 60 and older. The majority (83%) were diagnosed in their 40’s and 50’s. Two were diagnosed in their early 30’s and one at age 68. Participants reaffirmed the necessity, as a breast cancer survivor, of being a life-long health advocate on their own behalf, and the importance of being self-informed. As one woman commented: “Knowledge is power”. Survivors shared that their emergent health issues were complicated by their cancer history, and, that, as a cancer survivor, “I never stop worrying”. A widespread concern was not knowing if the health issues and co-morbidities they experienced (such as joint pain, neuropathy, tendinitis, heart disease), were age-related, a consequence of their cancer, or a late treatment effect. An overriding sentiment expressed was that clinicians have not recognized the importance of quality of life in cancer survival. As a survivor succinctly stated: “We are living longer, but we need to live long with quality of life.”

scholarly journals Machine learning prediction of breast cancer survival using age, sex, length of stay, mode of diagnosis and location of cancer

2021 ◽  
Author(s):  
Hilary I. Okagbue ◽  
Patience I. Adamu ◽  
Pelumi E. Oguntunde ◽  
Emmanuela C. M. Obasi ◽  
Oluwole A. Odetunmibi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Length Of Stay ◽  
Cancer Survival ◽  
Breast Cancer Survival
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