6 Papers
5 Citations
André Pfob is an academic researcher from University of Texas MD Anderson Cancer Center. The author has contributed to research in topics: Breast cancer & Mastectomy. The author has an hindex of 2, co-authored 6 publications. Previous affiliations of André Pfob include Heidelberg University & University Hospital Heidelberg.
Chat about Author
Papers
Contrast of Digital and Health Literacy Between IT and Health Care Specialists Highlights the Importance of Multidisciplinary Teams for Digital Health-A Pilot Study.
André Pfob,André Pfob,Chris Sidey-Gibbons,Maximilian Schuessler,Sheng-Chieh Lu,Cai Xu,Peter Dubsky,Michael Golatta,Joerg Heil +8 more
- 08 Jul 2021
TL;DR: In this article, the authors identify barriers to successful implementation of digital technologies in routine clinical practice, making the identification of barriers for successful implementation a difficult task and thus making it difficult to be solved.
36
Towards data-driven decision-making for breast cancer patients undergoing mastectomy and reconstruction: Prediction of individual patient-reported outcomes at two-year follow-up using machine learning.
André Pfob,Babak J. Mehrara,Jonas A. Nelson,Edwin G. Wilkins,Andrea L. Pusic,Chris Sidey-Gibbons +5 more
TL;DR: Post-surgical satisfaction with breasts is a key outcome for women undergoing cancer-related mastectomy and reconstruction and current decision making relies on group-level evidence, which is limited by the lack of unanimity among experts.
4
Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001).
André Pfob,Babak J. Mehrara,Jonas A. Nelson,Edwin G. Wilkins,Andrea L. Pusic,Chris Sidey-Gibbons +5 more
TL;DR: In this paper, three machine learning algorithms (logistic regression with elastic net penalty, Extreme Gradient Boosting (XGBoost) tree, and neural network) were used to predict patient-reported satisfaction with breasts at 2-year follow-up to better inform the decision-making process for women with breast cancer.
Towards Patient-Centered Decision-Making in Breast Cancer Surgery: Machine Learning to Predict Individual Patient-Reported Outcomes at 1-Year Follow-up.
André Pfob,Babak J. Mehrara,Jonas A. Nelson,Edwin G. Wilkins,Andrea L. Pusic,Chris Sidey-Gibbons +5 more
TL;DR: In this paper, the authors developed, tested, and validated machine learning algorithms to predict individual patient-reported outcomes at 1-year follow-up to facilitate individualized, patient-centered decision-making for women with breast cancer.