Semantic Sense in Medical Communication: A Collocational Analysis of 'Cancer' and 'Patient'

Authors

  • Khalid Rokan Mansoor

    English Department, Al Turath University, Baghdad 10013, Iraq
  • Afsaneh Shokri

    Academic Department of Foundation Studies (FS), Global College of Engineering and Technology, Muscat P.O. Box  2546, Oman
  • Khorshid Mousavi

    Department of English Language, The Islamic University, Najaf 54003, Iraq

DOI:

https://doi.org/10.30564/fls.v7i12.9662
Received: 23 April 2025 | Revised: 26 May 2025 | Accepted: 10 June 2025 | Published Online: 18 December 2025

Abstract

In this study, the semantic distribution of the clinical collocations "cancer" and "patient," with an emphasis on their medical applications, was analyzed using Hoey' s lexical priming model. A corpus of 1000 medical research articles from leading journals, encompassing approximately 2,576,035 words, was utilized. AntConc software identified high-frequency words, and the semantic loads of collocations of "cancer" and "patient" were categorized using Xiao and McEner's labels. A qualitative analysis further examined patterns of semantic load, clinical collocations, and contextual factors. Cross-validation with a second rater achieved high inter-rater reliability (Kappa = 0.983, p = 0.000). The findings revealed that "patients" typically carry a neutral semantic prosody, reflecting their association with various medical contexts, interventions, and cases without inherently positive or negative connotations. In contrast, "cancer" predominantly carries a negative semantic prosody, strongly linked to serious health risks, adverse outcomes, and mortality. These results underscore the importance of context in medical terminology for effective communication. This study supports Hoey' s theory that repeated exposure to specific contexts primes lexical items for clinical collocation. It emphasizes how context influences the emotional tone of medical communication. The findings have important clinical implications, particularly for enhancing doctor-patient communication, promoting empathetic interactions, and reducing misunderstandings in medical settings.

Keywords:

Cancer; Clinical Collocations; Chemical Health Risks; Medical Science; Patient; Semantic Sense

References

[1] Mignone, A., Grand, A., Fiori, A., et al., 2019. Semalytics: A semantic analytics platform for the exploration of distributed and heterogeneous cancer data in translational research. Database. baz080. DOI: https://doi.org/10.1093/database/baz080

[2] Wild, C.P., Stewart, B.W. (eds.), 2014. World Cancer Report 2014. World Health Organization: Geneva, Switzerland.

[3] Soerjomataram, I., Bray, F., 2021. Planning for Tomorrow: Global Cancer Incidence and the Role of Prevention 2020–2070. Nature Reviews Clinical Oncology. 18(10), 663–672.

[4] Louw, B., 1993. Irony in the text or insincerity in the writer? The diagnostic potential of semantic prosodies. In Text and Technology: In Honour of John Sinclair. John Benjamins: Amsterdam, Netherlands. pp. 156–159.

[5] McEnery, T., Xiao, R., Tono, Y., 2006. Corpus-Based Language Studies: An Advanced Resource Book. Taylor & Francis: London, UK.

[6] Mansoor, K.R., 2017. Investigating Semantic and Syntactic Equivalence in the Translation Process. Editura Politehnica: Timişoara, Romania.

[7] Azizmohammadi, F., Barjesteh, H., 2020. Studies in Ethnopragmatics, Cultural Semantics, and ‎Intercultural Communication: Ethnopragmatics and Semantic ‎Analysis, Lauren Sadow, Bet Peeters & Kerry Mullan (Eds.) ‎‎(2019), Springer, ISBN 978-981-32-9982-5‎. International Journal of Society, Culture & Language. 8(1), 126–131.

[8] Starcke, B., 2008. I Don't Know–Differences in Patterns of Collocation and Semantic Prosody in Phrases of Different Lengths. In Language, People and Numbers. Corpus Linguistics and Society. Brill: Amsterdam, Netherlands. pp. 199–216.

[9] Jiang, G., Solbrig, H.R., Chute, C.G., 2011. Quality Evaluation of Cancer Study Common Data Elements Using the UMLS Semantic Network. Journal of Biomedical Informatics. 44(Supplement 1), S78–S85.

[10] Hunston, S., 2007. Semantic Prosody Revisited. International Journal of Corpus Linguistics. 12(2), 249–268.

[11] Milojkovic, M., Louw, B., 2017. Towards a Corpus-Attested Definition of Creativity as Accessed Through a Subtextual Analysis of Student Writing. In Essential Competencies for English-Medium University Teaching. Springer: Singapore. pp. 125–141.

[12] Liu, Y., Tang, Y., Cao, Z.B., et al., 2019. Results from the China 2018 Report Card on Physical Activity for Children and Youth. Journal of Exercise Science & Fitness. 17(1), 3–7.

[13] Baker, S., Silins, I., Guo, Y., et al., 2016. Automatic Semantic Classification of Scientific Literature According to the Hallmarks of Cancer. Bioinformatics. 32(3), 432–440.

[14] Miranda-Escalada, A., Farré, E., Krallinger, M., 2020. Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in Spanish, corpus, guidelines, methods and results. In Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), Málaga, Spain, 23 September 2020; pp. 303–323.

[15] Shah-Mohammadi, F., Finkelstein, J., 2024. Addressing Semantic Variability in Clinical Outcome Reporting. BioMedInformatics. 4(4), 2173–2185.

[16] Yada, S., Nishiyama, T., Wakamiya, S., et al., 2025. Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs. PLoS One. 20(3), e0320522.

[17] Cui, M., Bai, R., Lu, Z., et al., 2019. Regular Expression Based Medical Text Classification Using Constructive Heuristic Approach. IEEE Access. 7, 147892–147904.

[18] Semchyshyn, V., Mykhalyk, D., 2023. Automated processing and analysis of medical texts. In Proceedings of the 3rd International Workshop on Information Technologies: Theoretical and Applied Problems 2023, Ternopil, Ukraine, Opole, Poland, 22–24 November 2023; pp. 300–305.

[19] Pépin, I., Alaca, F., Zulkernine, F., 2024. Privacy-Preserving Multi-Party Keyword-Based Classification of Unstructured Text Data. In Proceedings of the 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT),Abu Dhabi, United Arab Emirates, 29 April–1 May 2024; pp. 203–210.

[20] Dornyei, Z., 2007. Research Methods in Applied Linguistics. Oxford University Press: Oxford, UK.

[21] Anthony, L., 2011. AntConc (Version 3.2.4w) [Computer Software]. Waseda University: Tokyo, Japan.

[22] Ludwig, J.M., Ambinder, E.M., Ghodadra, A., et al., 2016. Lung Shunt Fraction prior to Yttrium-90 Radioembolization Predicts Survival in Patients with Neuroendocrine Liver Metastases: Single-Center Prospective Analysis. Cardiovascular and Interventional Radiology. 39(7), 1007–1014.

[23] Hughes, T.M.D., Elton, C., 2002. The Pathophysiology and Management of Bowel and Mesenteric Injuries Due to Blunt Trauma. Injury. 33(4), 295–302.

[24] Le, T.B., Moon, M.H., Jeon, Y.S., et al., 2016. Evaluation of Aneurysm Neck Angle Change After Endovascular Aneurysm Repair Clinical Investigations. Cardiovascular and Interventional Radiology. 39(5), 668–675.

[25] Nestle, U., Schimek-Jasch, T., Kremp, S., et al., 2020. Imaging-based target volume reduction in chemoradiotherapy for locally advanced non-small-cell lung cancer (PET-Plan): A multicentre, open-label, randomised, controlled trial. 21(4), 581–592.

[26] Masuda, K., Yamazoe, S., Baba, A., et al., 2021. Superselective intra-arterial chemoradiotherapy using altered blood flow compared to conventional systemic chemoradiotherapy for locally advanced oral squamous cell carcinoma: A single-center retrospective study. Oral Radiology. 37(4), 700–706.

[27] Schnell, J., Beer, M., Eggeling, S., et al., 2018. Management of Spontaneous Pneumothorax and Postinterventional Pneumothorax: German S3-Guideline. Zentralblatt für Chirurgie. 143(S01), S12–S43.

[28] Klok, F.A., Ageno, W., Ay, C., et al., 2022. Optimal follow-up after acute pulmonary embolism: A position paper of the European Society of Cardiology Working Group on Pulmonary Circulation and Right Ventricular Function, in collaboration with the European Society of Cardiology Working Group on Atherosclerosis and Vascular Biology, endorsed by the European Respiratory Society. European Heart Journal. 43(3), 183–189.

[29] Feng, S., Bose, R., Choi, Y., 2011. Learning General Connotation of Words Using Graph-Based Algorithms. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK, 27–31 July 2011; pp. 1092–1103.

[30] Perez, G.K., Mutchler, J., Yang, M.S., et al., 2016. Promoting quality care in cancer patients with Limited English Proficiency: Perspectives of medical interpreters. Psycho-Oncology. 25(10), 1241.

[31] Aasen, E.M., Crawford, P., Dahl, B.M., 2020. Discursive construction of the patient in online clinical cancer pathways information. Journal of Advanced Nursing. 76(11), 3113–3122.

[32] Selmistraitis, L., 2020. Semantic preference, prosody and distribution of synonymous adjectives in COCA. GEMA Online Journal of Language Studies. 20(3), 1–18.

[33] Bi, Z., 2019. A semantic prosody analysis on two synonymous pairs in English Native Speakers' and Chinese learners' writings. English Language Teaching. 12(8), 14–19.

[34] Sinclair, J., 2004. Trust the Text: Language, Corpus, and Discourse. Routledge: London, UK.

[35] Hoey, M., 2005. Lexical Priming: A New Theory of Words and Language. Routledge: London, UK.

Downloads

How to Cite

Rokan Mansoor, K., Shokri, A., & Mousavi, K. (2025). Semantic Sense in Medical Communication: A Collocational Analysis of ’Cancer’ and ’Patient’. Forum for Linguistic Studies, 7(12), 1903–1914. https://doi.org/10.30564/fls.v7i12.9662

Issue

Vol. 7 , Iss. 12 (December 2025): In progress

Article Type

Article (This article belongs to the Topical Collection on "Affective Reactions and Foreign Language Anxieties: Focus on Debilitating Anxiety")

License

Copyright © 2025 Khalid Rokan Mansoor, Afsaneh Shokri, Khorshid Mousavi

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.