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Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI)

Research article (European journal of medical research, 2024) · cited 16× · AI/ML
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Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI)

Summary

Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI) is a scholarly article[1].

Key Facts

  • Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI)'s instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI). Retrieved May 24, 2026, from https://4ort.xyz/entity/machine-learning-enabled-prediction-of-prolonged-length-of-stay-in-hospital-after-surgery-for-tuberculosis-spondylitis-p
MLA “Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/machine-learning-enabled-prediction-of-prolonged-length-of-stay-in-hospital-after-surgery-for-tuberculosis-spondylitis-p.
BibTeX @misc{4ortxyz_machine-learning-enabled-prediction-of-prolonged-length-of-stay-in-hospital-after-surgery-for-tuberculosis-spondylitis-p_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI)}}, year = {2026}, url = {https://4ort.xyz/entity/machine-learning-enabled-prediction-of-prolonged-length-of-stay-in-hospital-after-surgery-for-tuberculosis-spondylitis-p}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI) — https://4ort.xyz/entity/machine-learning-enabled-prediction-of-prolonged-length-of-stay-in-hospital-after-surgery-for-tuberculosis-spondylitis-p (retrieved 2026-05-24)

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