scholarly journals Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach

BMC Cancer ◽  
2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Frank P. Y. Lin ◽  
Adrian Pokorny ◽  
Christina Teng ◽  
Rachel Dear ◽  
Richard J. Epstein
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Making ◽  
Multidisciplinary Team ◽  
Computational Prediction ◽  
Cancer Drug ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Adjuvant Breast Cancer ◽  
Team Decision

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. 137-138
Keyword(s):  
Linked Data ◽  
Clinical Decision Support Systems ◽  
Computational Prediction ◽  
Clinical Decision ◽  
Cancer Drug ◽  
Learning Approach ◽  
Health Records ◽  
Machine Learning Approach ◽  
Adjuvant Breast Cancer ◽  
Team Decision

Lin FP, Pokorny A, Teng C, Dear R, Epstein RJ. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach. BMC Cancer 2016 Dec 1;16(1):929 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5131452/ Marco-Ruiz L, Pedrinaci C, Maldonado JA, Panziera L, Chen R, Bellika JG. Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach. J Biomed Inform 2016 Aug;62:243-64 http://www.sciencedirect.com/science/article/pii/S153204641630065X?via%3Dihub McEvoy DS, Sittig DF, Hickman TT, Aaron S, Ai A, Amato M, Bauer DW, Fraser GM, Harper J, Kennemer A, Krall MA, Lehmann CU, Malhotra S, Murphy DR, O’Kelley B, Samal L, Schreiber R, Singh H, Thomas EJ, Vartian CV, Westmorland J, McCoy AB, Wright A. Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. J Am Med Inform Assoc 2017 Mar 1;24(2):331-8 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391726/ Zamborlini V, Hoekstra R, Da Silveira M, Pruski C, ten Teije A, van Harmelen F. Inferring recommendation interactions in clinical guidelines. Semantic Web 2016;7(4):421-46 https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwin39e8r77VAhXEaFAKHeLkAC0QFgg_MAI&url=http%3A%2F%2Fwww.semantic-web-journal.net%2Fsystem%2Ffiles%2Fswj891.pdf&usg=AFQjCNF7CEXY49F929iQL09sRbGCl2Lc2A

scholarly journals Best Paper Selection

2017 ◽  
Vol 26 (01) ◽  
pp. e11-e12
Keyword(s):  
Linked Data ◽  
Clinical Decision Support Systems ◽  
Computational Prediction ◽  
Clinical Decision ◽  
Cancer Drug ◽  
Learning Approach ◽  
Health Records ◽  
Machine Learning Approach ◽  
Adjuvant Breast Cancer ◽  
Team Decision

Lin FP, Pokorny A, Teng C, Dear R, Epstein RJ. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach. BMC Cancer 2016 Dec 1;16(1):929 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5131452/ Marco-Ruiz L, Pedrinaci C, Maldonado JA, Panziera L, Chen R, Bellika JG. Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach. J Biomed Inform 2016 Aug;62:243-64 http://www.sciencedirect.com/science/article/pii/S153204641630065X?via%3Dihub McEvoy DS, Sittig DF, Hickman TT, Aaron S, Ai A, Amato M, Bauer DW, Fraser GM, Harper J, Kennemer A, Krall MA, Lehmann CU, Malhotra S, Murphy DR, O’Kelley B, Samal L, Schreiber R, Singh H, Thomas EJ, Vartian CV, Westmorland J, McCoy AB, Wright A. Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. J Am Med Inform Assoc 2017 Mar 1;24(2):331-8 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391726/ Zamborlini V, Hoekstra R, Da Silveira M, Pruski C, ten Teije A, van Harmelen F. Inferring recommendation interactions in clinical guidelines. Semantic Web 2016;7(4):421-46 https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&uact=8&ved=0ahUKEwin39e8r77VAhXEaFAKHeLkAC0QFgg_MAI&url=http%3A%2F%2Fwww.semantic-web-journal.net%2Fsystem%2Ffiles%2Fswj891.pdf&usg=AFQjCNF7CEXY49F929iQL09sRbGCl2Lc2A

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

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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