Top

BMC Medical Informatics and Decision Making

Published in:

Open Access 01-12-2023 | Research

A universal diagnosis syntax

Author: Carl-Fredrik Bassøe

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

Abstract

Background

Diagnoses are crucial assets of clinical work and provide the foundation for treatment and follow up. They should be informative and customized to the patient’s problem. Common prefixes, morphemes, and suffixes may aid the implementation of expressions that generate diagnoses.

Results

Apt choices of symbols plays a major role in science. In this study, the variables e, o, and p are assigned to names of an etiological agent, a disorder, and a pathogenetic mechanism, respectively. The suffix -itis designates infections, allergies, inflammation, and/or immune reactions. Diagnoses (d) are generated by the formula d:= e&o&p where ‘&’ means concatenation and ‘:= ’ means assignment. Thus, with e:= ’Staphylococcus aureus ‘, o:= ’endocard’, and p:= ’itis’, d:= e&o&p generates the diagnosis d = ’Staphylococcus aureus endocarditis’. Diagnoses formed this way comply with common clinical diagnoses. Certain extensions generate complete, systematic medical diagnoses that are applicable to all medical specialties. For example, common medical prefixes, morphemes, and suffixes give rise to o = ’hypothyroidism’, o = ’tachycardia’, and o = ’hypophagocytosis’. The formula scales well with the developments in clinical medicine, systems biology, molecular biology, and microbiology. The diagnosis generating formula d:= e&o&p requires meticulous analysis of the components of diagnoses plus the introduction of appropriate variables and terms. Terms partition on established clinical categories and adhere to established clinical nomenclature. The syntax generates universal medical diagnoses.

Conclusions

The present study concerns a universal diagnosis syntax (UDS) that generates diagnoses using the formula d:= e&o&p with several extensions described in the study. The formula is easy to learn and covers diagnoses in all medical specialties. The present work succeeded in creating diagnoses from the formula. The fundamental insight is that no matter how complicated a diagnosis is it can be generated by a systematic process, which adds terms one by one. UDS may have implications for medical education and classifications. The formula lays a foundation for structured clinical decision-making. Formulas are hallmarks of hard science. So, d:= e&o&p anticipates a scientific medical revolution.

Gamut LTF. Logic, language and meaning, vol. 1. Introduction to logic. Chicago: University of Chicago Press; 1991.

Jones FA, editor. Richard Asher talking sense. London: Pitman Medical; 1972.

Curé OC, Maurer H, Shah NH, Le Pendu P. A formal concept analysis and semantic query expansion cooperation to refine health outcomes of interest. BMC Med Inform Decis Mak. 2015;15 Suppl 1:S8. [cited 2021 December 2]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4460622/

Frege KA. An introduction to the founder of modern analytic philosophy. Oxford: Blackwell; 2000.

Robins RH. A short history of linguistics. London: Longman; 1997.

Miller A. Philosophy of language. London: Routledge; 2004.

Chomsky N. Language and the mind. San Diego: Harcourt Brace; 1972.

Chomsky N. Knowledge and language. New York: Praeger; 1986.

Fitch WT, Friederici AD. Artificial grammar learning meets formal language theory: an overview. Philos Trans R Soc Lond B Biol Sci. 2012;367(1598]:1933–55. https://doi.org/10.1098/rstb.2012.0103. Available from: http://rstb.royalsocietypublishing.org/content/367/1598/1933.long

10.

ICD-10. 2015 [cited 2015 December 1]. Available from: http://apps.who.int/classifications/icd10/browse/2016/en

11.

ICPC-2. 2015 [cited 2015 December 1]. Available from: http://www.who.int/classifications/icd/adaptations/icpc2/en/

12.

SNOMED. 2018 [cited 2018 May 25]. Available from: http://www.snomed.com

13.

Cimino JJ, Zhu X. The practical impact of ontologies on biomedical informatics. Methods Inf Med. 2006;45(Suppl):1.

14.

Boorse C.A rebuttal on health. In: Humber JM, Almeder RF (editors). What is disease? Totowa: Humana Press; 1997.

15.

Humphreys BL, Lindberg DA. The UMLS project: making the conceptual connection between users and the information they need. Bull Med Libr Assoc. Methods Inf Med. 1993;32(4):281–91. Available from: https://doi.org/10.1055/s-0038-1634945.

16.

McCray AT, Aronson AR, Browne AC, Rindflesch TC, Razi A, Srinivasan S. UMLS knowledge for biomedical language processing. Bull Med Libr Assoc. 1993; 81(2):184–94. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC225761/

17.

Kashyap V. The UMLS Semantic Network and the Semantic Web. AMIA Annu Symp Proc. 2003;2003:351–5.PubMedPubMedCentral

18.

Zhang L, Perl Y, Halper M, Geller J, Cimino JJ. An enriched unified medical language system semantic network with a multiple subsumption hierarchy. J Am Med Inform Assoc. 2004;11(3):195–206. [cited 2017 November 2]. Available from: https://doi.org/10.1197/jamia.M1269

19.

Carlsson M, Ahlfeldt H, Thurin A, Wigertz O. Terminology support for development of sharable knowledge modules. Med Inform (Lond). 1996;21(3):207–14. Available from: https://doi.org/10.3109/14639239609025358.

20.

Rector AL, Rogers JE, Zanstra PE, Van Der Haring E. OpenGALEN: open source medical terminology and tools. AMIA Annu Symp Proc. 2003;2003:982.PubMedPubMedCentral

21.

Rogers JE, Price C, Rector AL, Solomon WD, Smejko N. Validating clinical terminology structures: integration and cross-validation of Read Thesaurus and GALEN. Proc AMIA Symp. 1998:845–9. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2232201/

22.

Spackman KA, Campbell KE. Compositional concept representation using SNOMED: towards further convergence of clinical terminologies. Proc AMIA Symp. 1998:740–4. [cited 2015 August 7]. Available from: https://pubmed.ncbi.nlm.nih.gov/9929317/

23.

Boscá D, Maldonado JA, Moner D, Robles M. Automatic generation of computable implementation guides from clinical information models. J Biomed Inform. 2015;55:143–52. [cited 2017 November 13]. Available from: http://www.sciencedirect.com/science/article/pii/S1532046415000696

24.

Ceusters W, Smith B. Biomarkers in the ontology for general medical science. Stud Health Technol Inform. 2015;210:155–9. [cited 2020 January 20]. Available from: https://www.ncbi.nlm.nih.gov/pubmed/25991121

25.

Komenda M, Schwarz D, Švancara J, Vaitsis C, Zary N, Dušek L. Practical use of medical terminology in curriculum mapping. Comput Biol Med. 2015;63:74–82. [cited 2017 September 2]. Available from: https://www.ncbi.nlm.nih.gov/pubmed/26037030

26.

Marc DT, Zhang R, Beattie J, Gatewood LC, Khairat SS. Indexing Publicly Available Health Data with Medical Subject Headings (MeSH): An Evaluation of Coverage. Stud Health Technol Inform. 2015;216:529–33. [cited 2019 June 22]. Available from: https://www.ncbi.nlm.nih.gov/pubmed/26262107

27.

Seitinger A, Rappelsberger A, Leitich H, Binder M, Adlassnig KP. Executable medical guidelines with Arden Syntax-Applications in dermatology and obstetrics. Artif Intell Med. 2016;30321–9. [cited 2018 June 2]. Available from: https://www.ncbi.nlm.nih.gov/pubmed/27686851

28.

Livingston KM, Bada M, Baumgartner WA Jr, Hunter LE. KaBOB: ontology-based semantic integration of biomedical databases. 2015;16:126. [cited 2020 December 12]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4448321/

29.

Popper KR. The open society and its enemies. London: Routledge; 2011.

30.

Rasmussen J-E. Bassøe C-F Semantic analysis of medical records. Meth Inform Meth. 1993;32(1):66–72.CrossRef

31.

Bassøe C-F, Sørli WG. EPR records and forms in primary health care. Tidsskr Nor Legeforen. 1983;103:1270–4.

32.

Bassøe C-F. A combinatorial diagnostic system for general practice. Proceedings of the 11th Conference of the World Organisation of National Colleges, Academies and Academic Associations of General Practitioners/Family Physicians, London, 1986.

33.

Bassøe C-F. A combinatorial diagnostic system for general practice: Evaluation of the social impact of disease by a computerized medical record. In: Hansen R, Solheim BG, O'Moore RR, Roger FH (editors): Lecture notes in medical informatics, Springer-Verlag, Berlin, 1988.

34.

Botsis T, Bassøe CF, Hartvigsen G. Sixteen years of ICPC use in Norwegian primary care: looking through the facts. BMC Med Inform Decis Mak. 2010;10:11. [cited 2012 July 7]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2848129/

35.

Bassøe C-F. Combinatorial clinical decision-making. PhD dissertation, Department of Information Science and Media, Faculty of Social Sciences, University of Bergen, Norway, 2007. ISBN 978–82–308–0457–5. [cited 2022 December 23]. Available from: https://www.ekvimed.no/post/thesis-combinatorial-clinical-decision-making

36.

Cappello AR, Curcio R, Lappano R, Maggiolini M, Dolce V. The Physiopathological Role of the Exchangers Belonging to the SLC37 Family. Front Chem. 2018;6:122. [cited 2020 October 2]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5913288/

37.

Jadaon MM. Epidemiology of Activated Protein C Resistance and Factor V Leiden Mutation in the Mediterranean Region. Mediterr J Hematol Infect Dis. 2011;3:e2011037. [cited 2015 September 1]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3251907/

38.

Vakiti A, Mewawalla P. Cancer, Leukemia, Myeloid, Acute (AML, Erythroid Leukemia, Myelodysplasia-Related Leukemia, BCR-ABL Chronic Leukemia). Allegheny Health Network Cancer Inst. 2018. [cited 2018 June 29]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK507875/#article-25443.s2

39.

Jethwa A, Mink J, Macarthur C, Knights S, Fehlings T, Fehlings D. Development of the Hypertonia Assessment Tool (HAT): a discriminative tool for hypertonia in children. Dev Med Child Neurol. 2010;52:e83–7. e83–7. [cited 2014 December 1]. Available from: https://doi.org/10.1111/j.1469-8749.2009.03483.x

40.

Kurth J, Spieker T, Wustrow J, Strickler GJ, Hansmann LM, Rajewsky K, Küppers R. EBV-infected B cells in infectious mononucleosis: viral strategies for spreading in the B cell compartment and establishing latency. Immunity. 2000;13(4):485–95. [cited 2015 October 3]. Available from: https://doi.org/10.1016/s1074-7613(00)00048-0

41.

Jurcau A. Molecular Pathophysiological Mechanisms in Huntington's Disease. Biomedicines. 2022;10(6):1432. [cited 2022 July 3]. Available from: https://doi.org/10.3390/biomedicines10061432

42.

Edwards N, Honemann D, Burley D, Navarro M. Refinement of the Medicare diagnosis-related groups to incorporate a measure of severity. Health Care Financ Rev. 1994;16:45–64. [cited 2016 October 13]. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4193498/

43.

Karnofsky Scale. 2018 [cited 2018 July 12]. Available from: http://www.anapsid.org/cnd/diagnosis/karnofsky.html

44.

NYHA. New York Heart Association (NYHA) Classification. 2018 [cited 2018 July 12]. Available from: https://manual.jointcommission.org/releases/TJC2016A/DataElem0439.html

45.

Wang YY, Zhong JH, Su ZY, Huang JF, Lu SD, Xiang BD, et al. Albumin-bilirubin versus Child-Pugh score as a predictor of outcome after liver resection for hepatocellular carcinoma. Br J Surg. 2016;103:725–34. [cited 2019 September 9]. Available from: https://doi.org/10.1002/bjs.10095

46.

Hiraoka A, Kumada T, Kudo M, Hirooka M, Tsuji K, Itobayashi E, et al. Albumin-Bilirubin (ALBI) Grade as Part of the Evidence-Based Clinical Practice Guideline for HCC of the Japan Society of Hepatology: A Comparison with the Liver Damage and Child-Pugh Classifications. Liver Cancer. 2017;6:204–215. [cited 2019 June 3]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473065/

47.

Johnson PJ, Berhane S, Kagebayashi C, Satomura S, Teng M, Reeves HL, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade. J Clin Oncol. 2015; 33(6):550–8. [cited 2017 November 30]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4322258/

48.

Kringlen E. Diagnostikk som ideologi [Diagnosis as an ideology]. Tidsskr Nor Legeforen. 1995;115:630–2.

49.

Scadding JG. The semantic problems of psychiatry. Psychol Med. 1990;20:243–8.CrossRefPubMed

50.

Bassøe C-F. The skinache syndrome. J R Soc Med. 1995:88(10):565–9 [cited 2012 January 13]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1295357/

51.

Bassøe C-F. Automated diagnoses from clinical narratives: A medical system based on computerized medical records, natural language processing and neural network technology. Neural Networks 1995a;8:313–319. [cited 2014 September 29]. Available from: http://www.sciencedirect.com/science/article/pii/089360809400076X

52.

Bassøe C-F. Neutrophil functions studied by flow cytometry. In: Yen A, editor. Flow cytometry: Advanced Research and Clinical Applications. Boca Raton: CRC Press; 1989. p. 95–148.

53.

Bassøe CF. Representing health, disorder and their transitions by digraphs. Stud Health Technol Inform. 2008;136:133–8.PubMed

54.

Derbyshire J. Unknown quantity. Washington: Joseph Henry Press; 2006.

Title: A universal diagnosis syntax
Author: Carl-Fredrik Bassøe
Publication date: 01-12-2023
Publisher: BioMed Central
Published in: BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-023-02209-0

Keynote webinar | Spotlight on medication adherence

Springer Medicine

A universal diagnosis syntax

Abstract

Background

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2023

The protocol of DECO-MOM: a clinical trial testing the effectiveness of a mobile application for an environmental health intervention among pregnant women

Examining clinician choice to follow-up (or not) on automated notifications of medication non-adherence by clinical decision support systems

A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants

AI and semantic ontology for personalized activity eCoaching in healthy lifestyle recommendations: a meta-heuristic approach

Machine learning prediction models for different stages of non-small cell lung cancer based on tongue and tumor marker: a pilot study

Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration