Toward precision stroke rehabilitation: an integrated causal machine learning and clinician feedback approach
Causal ML can help to identify the mechanisms driving independence outcome variation. However, the large number of effects discovered and the small size of many effects make clinician feedback of paramount importance. Use of causal ML with clinician feedback throughout the process improves identification of appropriate measures and selection of relevant results.
Authors
- Aaron M. Baird (ORCID: https://orcid.org/0000-0002-5620-2926)
- Katheryn Metzger
- Yusen Xia (ORCID: https://orcid.org/0000-0003-2360-5574)
- Karen Patterson (ORCID: https://orcid.org/0000-0002-5596-6801)
- Liv Nyankori
- Houping Xiao (ORCID: https://orcid.org/0000-0002-6981-8842)
- Raeda Anderson (ORCID: https://orcid.org/0000-0003-0023-6649)
- C Sellers
Institutions
- Georgia State University (US)
- Shepherd Center (US)
- Colorado State University (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf146
- Citations
- 1
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.63