ArticleCommunications medicine2023
Text-based predictions of COVID-19 diagnosis from self-reported chemosensory descriptions.
Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Areas of research focus and trends in the research on the application of AIGC in healthcare.Journal of health, population, and nutrition · 2025Article
- The foundational capabilities of large language models in predicting postoperative risks using clinical notes.NPJ digital medicine · 2025Article
- DR-GPT: A large language model for medical report analysis of diabetic retinopathy patients.PloS one · 2024Article
- Developing Large Language Model-based Pipeline for Identification of Disease Diagnosis: A Case Study on Identifying Newly Diagnosed Multiple Myeloma and its Precursor Disease in Veterans Health Administration Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a prevailing view that humans' capacity to use language to characterize sensations like odors or tastes is poor, providing an unreliable source of information.
methodsHere, we developed a machine learning method based on Natural Language Processing (NLP) using Large Language Models (LLM) to predict COVID-19 diagnosis solely based on text descriptions of acute changes in chemosensation, i.e., smell, taste and chemesthesis, caused by the disease. The dataset of more than 1500 subjects was obtained from survey responses early in the COVID-19 pandemic, in Spring 2020.
resultsWhen predicting COVID-19 diagnosis, our NLP model performs comparably (AUC ROC ~ 0.65) to models based on self-reported changes in function collected via quantitative rating scales. Further, our NLP model could attribute importance of words when performing the prediction; sentiment and descriptive words such as "smell", "taste", "sense", had strong contributions to the predictions. In addition, adjectives describing specific tastes or smells such as "salty", "sweet", "spicy", and "sour" also contributed considerably to predictions.
conclusionsOur results show that the description of perceptual symptoms caused by a viral infection can be used to fine-tune an LLM model to correctly predict and interpret the diagnostic status of a subject. In the future, similar models may have utility for patient verbatims from online health portals or electronic health records.
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