Natural language processing for automated classification of qualitative data from interviews of patients with cancer
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OBJECTIVES: This study sought to explore the use of novel natural language processing (NLP) methods for classifying unstructured, qualitative textual data from interviews of patients with cancer to identify patient-reported symptoms and impacts on quality of life. METHODS: We tested the ability of 4 NLP models to accurately classify text from interview transcripts as "symptom," "quality of life impact," and "other." Interview data sets from patients with hepatocellular carcinoma (HCC) (n = 25), biliary tract cancer (BTC) (n = 23), and gastric cancer (n = 24) were used. Models were cross-validated with transcript subsets designated for training, validation, and testing. Multiclass classification performance of the 4 models was evaluated at paragraph and sentence level using the HCC testing data set and analyzed by the one-versus-rest technique quantified by the receiver operating characteristic area under the curve (ROC AUC) score. RESULTS: NLP models accurately classified multiclass text from patient interviews. The Bidirectional Encoder Representations from Transformers model generally outperformed all other models at paragraph and sentence level. The highest predictive performance of the Bidirectional Encoder Representations from Transformers model was observed using the HCC data set to train and BTC data set to test (mean ROC AUC, 0.940 [SD 0.028]), with similarly high predictive performance using balanced and imbalanced training data sets from BTC and gastric cancer populations. CONCLUSIONS: NLP models were accurate in predicting multiclass classification of text from interviews of patients with cancer, with most surpassing 0.9 ROC AUC at paragraph level. NLP may be a useful tool for scaling up processing of patient interviews in clinical studies and, thus, could serve to facilitate patient input into drug development and improving patient care.
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BERT data extraction performance exceeded 90% accuracy in pan-cancer contexts.
“Fang et al [22] and Mitchell et al (2022) [23] reported that the data extraction performance of BERT exceeded 90% accuracy in pan-cancer contexts.”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [22]
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Kermanidis, K. L., Tzimiris, S., Nikiforos, S., Nikiforos, M. N., & Mouratidis, D. (2025) · Applied Sciences · doi:10.3390/app15094667 · Cited by 1 · see citing works
Jumaah, J., Masnun, M., & Supardi, S. (2023) · Jurnal Ilmiah Profesi Pendidikan · doi:10.29303/jipp.v8i3.2115 · Cited by 1 · see citing works
“Through systematic observation protocols, researchers will document teaching methodologies (Fang et al., 2022), student interactions, and the overall classroom atmosphere.”
BackgroundSpies, E., Flynn, J. A., Oliveria, N. G., Karmalkar, P., & Gurulingappa, H. (2023) · doi:10.2196/preprints.50793
Machine Learning Methods of Symptom Extraction from Medical Notes
Galai, O., Kurdiumov, D., & Rusak, A. V. (2024) · International Russian Smart Industry Conference (SmartIndustryCon) · doi:10.1109/smartindustrycon61328.2024.10515546
“The ability to automatically identify and categorize symptoms can significantly enhance healthcare delivery, aid in early disease detection, and facilitate data-driven medical decision-making [2, 3, 4].”
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“The questions in the interview guide are designed to explore respondents' views on joint property division, their perceptions of the role of education in the process, and other factors influencing their perspectives (Fang et al., 2022).”
BackgroundTopic Extraction from Biographical Interviews
Babaki, S., Bagankar, S., Goyal, T., Shahriarizadeh, F., Reshadi, K., & Mehraeen, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-08697-6_9
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“These systems have assisted radiologists in detecting early signs of disease such as cancer for timely interventions and better patient outcomes [100].”
InfluentialAI-Powered Company-Specific Interview & Assessment Simulator
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Advancing Patient‐Centred AI With Adaptive Machine Learning
Kalogeropoulos, D., Barach, P. R., Downing, A., Verhulst, S., & Lustberg, M. B. (2026) · Cancer Medicine · doi:10.1002/cam4.72371
- Authors
- Fang, C., Markuzon, N., Patel, N., & Rueda, J.-D.
- Affiliations
- AstraZeneca (United States) (US)
- Corresponding author
- Markuzon, Natasha
- Venue
- Value in Health
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 25
- Issue
- 12
- Pages
- 1995-2002
- ISSN
- 1098-3015 (ISSN-L)
- OpenAlex
- W4285098504
- Cited by
- 36
- References
- 56
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 12 · 2023: 6
- FWCI
- 17.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Value in Health (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- AstraZeneca (163:461-464)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- natural language processing, text classification, patient-reported symptoms, quality of life, cancer patients, patient interviews, hepatocellular carcinoma, biliary tract cancer, gastric cancer, BERT, multi-class classification, drug development
- MeSH terms
- Carcinoma, Hepatocellular, Humans, Liver Neoplasms, Natural Language Processing, Quality of Life, Stomach Neoplasms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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