Additional file 1: of A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data

Golas, Sara; Shibahara, Takuma; Agboola, Stephen; Otaki, Hiroko; Sato, Jumpei; Nakae, Tatsuya; Hisamitsu, Toru; Kojima, Go; Felsted, Jennifer; Kakarmath, Sujay; Kvedar, Joseph; Jethwani, Kamal

doi:10.6084/m9.figshare.6651320.v1

12911_2018_620_MOESM1_ESM.docx (28.12 kB)

Additional file 1: of A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data

journal contribution

posted on 2018-06-22, 05:00 authored by Sara Golas, Takuma Shibahara, Stephen Agboola, Hiroko Otaki, Jumpei Sato, Tatsuya Nakae, Toru Hisamitsu, Go Kojima, Jennifer Felsted, Sujay Kakarmath, Joseph Kvedar, Kamal Jethwani

Appendix A. Deep Unified Networks, supplemental. (DOCX 28Â kb)

Funding

Central Research Laboratory, Hitachi, Ltd.

History

Usage metrics

Keywords

Heart failure Machine learning Deep learning Deep unified networks Readmission reduction Value-based care

Licence

CC BY + CC0

Exports

RefWorks

BibTeX

Ref. manager

Endnote

DataCite

NLM

DC

Additional file 1: of A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data

Funding

Central Research Laboratory, Hitachi, Ltd.

History

Usage metrics

Categories

Keywords

Licence

Exports