AI-driven disease classification from unstructured textual symptom descriptions: A multi-model NLP benchmarking study across categorical, UMLS-derived, and synthetic datasets
- DOI
- 10.1016/j.compbiolchem.2026.109281
- Published
- 2026-12
- Container
- Computational Biology and Chemistry
- Publisher
- Elsevier BV
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.compbiolchem.2026.109281,
title = {AI-driven disease classification from unstructured textual symptom descriptions: A multi-model NLP benchmarking study across categorical, UMLS-derived, and synthetic datasets},
author = {Sayak Mukhopadhyay and Shilpa Gite and Ketan Kotecha and Ganeshsree Selvachandran and Palak Anand and Ajith Abraham},
year = {2026},
journal = {Computational Biology and Chemistry},
doi = {10.1016/j.compbiolchem.2026.109281},
url = {https://doi.org/10.1016/j.compbiolchem.2026.109281}
}RIS
TY - JOUR TI - AI-driven disease classification from unstructured textual symptom descriptions: A multi-model NLP benchmarking study across categorical, UMLS-derived, and synthetic datasets AU - Sayak Mukhopadhyay AU - Shilpa Gite AU - Ketan Kotecha AU - Ganeshsree Selvachandran AU - Palak Anand AU - Ajith Abraham PY - 2026 JO - Computational Biology and Chemistry DO - 10.1016/j.compbiolchem.2026.109281 UR - https://doi.org/10.1016/j.compbiolchem.2026.109281 ER -
APA
Mukhopadhyay, S., Gite, S., Kotecha, K., Selvachandran, G., Anand, P., & Abraham, A. (2026). AI-driven disease classification from unstructured textual symptom descriptions: A multi-model NLP benchmarking study across categorical, UMLS-derived, and synthetic datasets. Computational Biology and Chemistry. https://doi.org/10.1016/j.compbiolchem.2026.109281
Source records
- crossref · retrieved 2026-09-25T18:47:47.103Z