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Semantic interoperability and price analytics in hospital transparency data: a multi-stage pipeline with NLP and machine learning

Thomas Wiese

Informatics for Health and Social Care2026https://doi.org/10.1080/17538157.2026.2650681article
ABDC C
Weight
0.50

What the paper says

This work advances scalable, semi-automated MRF research methodology, transforming opaque pricing data into an analytically tractable form with implications for healthcare policy and consumer tools.

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https://doi.org/https://doi.org/10.1080/17538157.2026.2650681

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@article{thomas2026,
  title        = {{Semantic interoperability and price analytics in hospital transparency data: a multi-stage pipeline with NLP and machine learning}},
  author       = {Thomas Wiese},
  journal      = {Informatics for Health and Social Care},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/17538157.2026.2650681},
}

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Semantic interoperability and price analytics in hospital transparency data: a multi-stage pipeline with NLP and machine learning

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.