Towards Explainable AI: Design and Development for Explanation of Machine Learning Predictions for a Patient Readmittance Medical Application

Author:

Meacham Sofia,Isaac Georgia,Nauck Detlef,Virginas Botond

Publisher

Springer International Publishing

Reference19 articles.

1. Dua, D., Karra Taniskidou, E.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2017). http://archive.ics.uci.edu/ml

2. Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2013). http://archive.ics.uci.edu/ml

3. Strack, B., DeShazo, J., Gennings, C., Olmo, J., Ventura, S., Cios, K., Clore, J.: Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. BioMed. Res. Int. 2014, 1–11 (2014). http://dx.doi.org/10.1155/2014/781670

4. DARPA, Information Innovation Office. Broad Agency Announcement, Explainable Artificial Intelligence (XAI). DARPA, Arlington, VA (2016)

5. Launchburry, J.: A DARPA Perspective on Artificial Intelligence (2017)

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