Home ›
Entities
› academia
› Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness
Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness
Research article (Journal of Orthopaedic Surgery and Research, 2024) · cited 15× · AI/ML
Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness
Summary
Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness is a scholarly article[1].
Key Facts
Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness. Retrieved May 24, 2026, from https://4ort.xyz/entity/preoperative-prediction-model-for-risk-of-readmission-after-total-joint-replacement-surgery-a-random-forest-approach-lev
MLA“Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/preoperative-prediction-model-for-risk-of-readmission-after-total-joint-replacement-surgery-a-random-forest-approach-lev.
BibTeX@misc{4ortxyz_preoperative-prediction-model-for-risk-of-readmission-after-total-joint-replacement-surgery-a-random-forest-approach-lev_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness}}, year = {2026}, url = {https://4ort.xyz/entity/preoperative-prediction-model-for-risk-of-readmission-after-total-joint-replacement-surgery-a-random-forest-approach-lev}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness — https://4ort.xyz/entity/preoperative-prediction-model-for-risk-of-readmission-after-total-joint-replacement-surgery-a-random-forest-approach-lev (retrieved 2026-05-24)