Skip to main content
SpringerLink
Log in

Computerunterstützte Diagnosefindung bei seltenen Erkrankungen

Computer-assisted diagnosis of rare diseases

  • Seltene Erkrankungen
  • Published:
Der Internist Aims and scope Submit manuscript

Zusammenfassung

Die Diagnosefindung ist die intellektuell herausforderndste Aufgabe des Arztes. Seltene Erkrankungen stellen hohe Anforderungen an die Differenzialdiagnose. Aufgrund der hohen Anzahl und Variabilität der Symptomkomplexe können keinem Kliniker alle Entitäten bekannt sein. Spezifische Systeme zur Entscheidungsunterstützung liefern in diesem Kontext bessere Ergebnisse als Standardsuchmaschinen. Die Systeme FindZebra, Phenomizer, Orphanet und Isabel werden hier prägnant vorgestellt, mitsamt Vorteilen und Limitationen. Zudem wird ein Ausblick auf Systeme im Bereich der sozialen Medien und Big-Data-Verfahren gegeben. Da die Zahl initialer Fehldiagnosen hoch ist und vor einer konfirmatorischen Diagnose lange Zeiträume verstreichen, könnten diese Werkzeuge die Diagnosestellung bei seltenen Erkrankungen verbessern.

Abstract

To establish a comprehensive diagnosis is by far the most challenging task in a physician’s daily routine. Especially rare diseases place high demands on differential diagnosis, caused by the high number of around 8000 diseases and their clinical variability. No clinician can be aware of all the different entities and memorizing them all is impossible and inefficient. Specific diagnostic decision-supported systems provide better results than standard search engines in this context. The systems FindZebra, Phenomizer, Orphanet, and Isabel are presented here concisely with their advantages and limitations. An outlook is given to social media usage and big data technologies. Due to the high number of initial misdiagnoses and long periods of time until a confirmatory diagnosis is reached, these tools might be promising in practice to improve the diagnosis of rare diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Abb. 1
Abb. 2
Abb. 3

Literatur

  1. Ayme S, Schmidtke J (2007) Networking for rare diseases: a necessity for Europe. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 50(12):1477–1483. doi:10.1007/s00103-007-0381-9

    Article  CAS  PubMed  Google Scholar 

  2. Ayme S, Bellet B, Rath A (2015) Rare diseases in ICD11: making rare diseases visible in health information systems through appropriate coding. Orphanet J Rare Dis 10:35. doi:10.1186/s13023-015-0251-8

    Article  PubMed  PubMed Central  Google Scholar 

  3. Barrera LA, Galindo GC (2010) Ethical aspects on rare diseases. Adv Exp Med Biol 686:493–511. doi:10.1007/978-90-481-9485-8_27

    Article  PubMed  Google Scholar 

  4. Basson CT, Bachinsky DR, Lin RC, Levi T, Elkins JA, Soults J, Grayzel D, Kroumpouzou E, Traill TA, Leblanc-Straceski J, Renault B, Kucherlapati R, Seidman JG, Seidman CE (1997) Mutations in human TBX5 corrected cause limb and cardiac malformation in Holt-Oram syndrome. Nat Genet 15(1):30–35. doi:10.1038/ng0197-30

    Article  CAS  PubMed  Google Scholar 

  5. Birt Hogg Dubé Foundation (2015) FindZebra: a specialised search engine for rare diseases – Birt-Hogg-Dubé Syndrome. https://www.bhdsyndrome.org/forum/bhd-research-blog/findzebra-a-specialised-search-engine-for-rare-diseases/. Zugegriffen: 10. Februar 2017

    Google Scholar 

  6. Bond WF, Schwartz LM, Weaver KR, Levick D, Giuliano M, Graber ML (2012) Differential diagnosis generators: an evaluation of currently available computer programs. J Gen Intern Med 27(2):213–219. doi:10.1007/s11606-011-1804-8

    Article  PubMed  Google Scholar 

  7. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comp Netw ISDN Syst 30(1–7):107–117. doi:10.1016/S0169-7552(98)00110-X

    Article  Google Scholar 

  8. Christiansen AL, Brusgaard K, Hertz JM, Bygum A (2015) Googlede lysfolsomhed og diagnosticerede sig selvmed erytropoietisk protoporfyri. Ugeskr Laeger 177(30):2–3

    Google Scholar 

  9. Densen P (2011) Challenges and opportunities facing medical education. Trans Am Clin Climatol Assoc 122:48–58

    PubMed  PubMed Central  Google Scholar 

  10. DIMDI: Deutsches Institut für Medizinische Dokumentation und Information (2017) DIMDI – Anwendungsbereich. http://www.dimdi.de/static/de/klassi/alpha-id/seltene-erkrankungen.htm#international. Zugegriffen: 11. Februar 2017

    Google Scholar 

  11. Dragusin R, Petcu P, Lioma C, Larsen B, Jorgensen HL, Cox IJ, Hansen LK, Ingwersen P, Winther O (2013) FindZebra: a search engine for rare diseases. Int J Med Inform 82(6):528–538. doi:10.1016/j.ijmedinf.2013.01.005

    Article  PubMed  Google Scholar 

  12. Dragusin R, Petcu P, Lioma C, Larsen B, Jorgensen HL, Cox IJ, Hansen LK, Ingwersen P, Winther O (2013) Specialized tools are needed when searching the web for rare disease diagnoses. Rare Dis 1:e25001. doi:10.4161/rdis.25001

    Article  PubMed  PubMed Central  Google Scholar 

  13. Eddy DM, Clanton CH (1982) The art of diagnosis: solving the clinicopathological exercise. N Engl J Med 306(21):1263–1268. doi:10.1056/NEJM198205273062104

    Article  CAS  PubMed  Google Scholar 

  14. European Parliament (1999) Regulation (EC) No 596/2009 of the European Parliament and of the Council of 18 June 2009

  15. Eurordis (2007) Rare diseases europe fact sheet Eurordiscare2. http://www.eurordis.org/sites/default/files/publications/Fact_Sheet_Eurordiscare2.pdf. Zugegriffen: 13. Februar 2017

    Google Scholar 

  16. Ferry Q, Steinberg J, Webber C, FitzPatrick DR, Ponting CP, Zisserman A, Nellaker C (2014) Diagnostically relevant facial gestalt information from ordinary photos. Elife 3:e02020. doi:10.7554/eLife.02020

    Article  PubMed  PubMed Central  Google Scholar 

  17. Fox S, Duggan M (2013) Health online 2013. http://www.pewinternet.org/2013/01/15/health-online-2013/. Zugegriffen: 10. Februar 2017

    Google Scholar 

  18. Google Inc (2016) So funktioniert die Suche – Die Story – Alles über die Suche – Google. https://www.google.com/insidesearch/howsearchworks/thestory/. Zugegriffen: 09. Februar 2017

    Google Scholar 

  19. Google Inc (2017) Google Organic CTR Study 2014. https://www.advancedwebranking.com/google-ctr-study-2014.html. Zugegriffen: 10. Februar 2017

    Google Scholar 

  20. Google/Manhattan Research (2012) Screen to Script – The Doctor’s Digital Path to Treatment. https://www.thinkwithgoogle.com/research-studies/the-doctors-digital-path-to-treatment.html. Zugegriffen: 10. Februar 2017

  21. Graber ML, Mathew A (2008) Performance of a web-based clinical diagnosis support system for internists. J Gen Intern Med 23(Suppl 1):37–40. doi:10.1007/s11606-007-0271-8

    Article  PubMed  Google Scholar 

  22. Grigull L, Lechner W, Petri S, Kollewe K, Dengler R, Mehmecke S, Schumacher U, Lucke T, Schneider-Gold C, Kohler C, Guttsches A‑K, Kortum X, Klawonn F (2016) Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial. BMC Med Inform Decis Mak 16:31. doi:10.1186/s12911-016-0268-5

    Article  PubMed  PubMed Central  Google Scholar 

  23. Isabel Healthcare (2017) Isabel Healthcare differential diagnosis tool. http://www.isabelhealthcare.com/. Zugegriffen: 11. Februar 2017

    Google Scholar 

  24. Kerkmann C (2016) BM und Rhön-Klinikum: Wenn der Supercomputer die Diagnose stellt. Handelsblatt

    Google Scholar 

  25. Kohler S, Doelken SC, Mungall CJ et al (2014) The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res 42(Database issue):D966–74. doi:10.1093/nar/gkt1026

    Article  PubMed  Google Scholar 

  26. Kohler S, Schulz MH, Krawitz P, Bauer S, Dolken S, Ott CE, Mundlos C, Horn D, Mundlos S, Robinson PN (2009) Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet 85(4):457–464. doi:10.1016/j.ajhg.2009.09.003

    Article  PubMed  PubMed Central  Google Scholar 

  27. Köhler S (2017) HPO translation – translation project on Crowdin. https://crowdin.com/project/hpo-translation. Zugegriffen: 11. Februar 2017

    Google Scholar 

  28. Maiella S, Rath A, Angin C, Mousson F, Kremp O (2013) Orphanet et son réseau. Où trouver une information validée sur les maladies rares. Rev Neurol (Paris) 169:S3–S8. doi:10.1016/S0035-3787(13)70052-3

    Article  Google Scholar 

  29. Marshall JG, Sollenberger J, Easterby-Gannett S, Morgan LK, Klem ML, Cavanaugh SK, Oliver KB, Thompson CA, Romanosky N, Hunter S (2013) The value of library and information services in patient care: results of a multisite study. J Med Libr Assoc 101(1):38–46. doi:10.3163/1536-5050.101.1.007

    Article  PubMed  PubMed Central  Google Scholar 

  30. Menko FH, van Steensel M, Giraud S, Friis-Hansen L, Richard S, Ungari S, Nordenskjöld M, Hansen TV, Solly J, Maher ER (2009) Birt-Hogg-Dubé syndrome. Diagnosis and management. Lancet Oncol 10(12):1199–1206. doi:10.1016/S1470-2045(09)70188-3

    Article  CAS  PubMed  Google Scholar 

  31. Meyer AND, Longhurst CA, Singh H (2016) Crowdsourcing diagnosis for patients with undiagnosed illnesses: an evaluation of crowdMed. J Med Internet Res 18(1):e12. doi:10.2196/jmir.4887

    Article  PubMed  PubMed Central  Google Scholar 

  32. Mueller T, Jerrentrup A, Bauer MJ, Fritsch HW, Schaefer JR (2016) Characteristics of patients contacting a center for undiagnosed and rare diseases. Orphanet J Rare Dis 11(1):81. doi:10.1186/s13023-016-0467-2

    Article  PubMed  PubMed Central  Google Scholar 

  33. Müller T, Jerrentrup A, Fritsch H‑W, Schäfer J (2016) Software zur Unterstützung der Differenzialdiagnose in der Inneren Medizin – Auswirkungen auf die Qualität der Medizin. Klinikarzt 45(05):250–256. doi:10.1055/s-0042-106355

    Article  Google Scholar 

  34. Mumoli N, Vitale J, Sabatini S, Manni C, Masi L, Mazzi V, Cei M, Giorgetti S, Rossi M, Comassi M, Camaiti A (2015) A “Google Image” diagnosis of Madelung’s disease. JRSM Open 6(2):2054270414565957. doi:10.1177/2054270414565957

    Article  PubMed  PubMed Central  Google Scholar 

  35. Munich RE (2015) Rare diseases – increasing in frequency. https://www.munichre.com/en/reinsurance/magazine/topics-online/2015/02/rare-diseases/index.html. Zugegriffen: 09. Februar 2017

    Google Scholar 

  36. Nash DB (2010) Isabel, a new diagnostic aid for the 21st century. P T 35(12):651

    PubMed Central  Google Scholar 

  37. Orphanet (2016) Birt Hogg Dubé syndrome. http://www.orpha.net/consor/cgi-bin/OC_Exp.php?Expert=122. Zugegriffen: 10. Februar 2017

    Google Scholar 

  38. Page L (2001) Method for node ranking in a linked database. Google patents. http://www.google.de/patents/US6285999. Zugegriffen: 09. Februar 2017

    Google Scholar 

  39. Rath A, Olry A, Dhombres F, Brandt MM, Urbero B, Ayme S (2012) Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users. Hum Mutat 33(5):803–808. doi:10.1002/humu.22078

    Article  PubMed  Google Scholar 

  40. Riches N, Panagioti M, Alam R, Cheraghi-Sohi S, Campbell S, Esmail A, Bower P (2016) The effectiveness of electronic differential diagnoses (DDX) generators: a systematic review and Meta-analysis. PLOS ONE 11(3):e0148991. doi:10.1371/journal.pone.0148991

    Article  PubMed  PubMed Central  Google Scholar 

  41. Robinson PN, Kohler S, Bauer S, Seelow D, Horn D, Mundlos S (2008) The human phenotype ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet 83(5):610–615. doi:10.1016/j.ajhg.2008.09.017

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Seelos H‑J, Bürsner S, Dickhaus H, Graubner B, Guggenmoos-Holzmann I, Ingenerf J, Klar R, Pelikan E, Pietsch-Breitfeld B, Repges R, Schneider B, Selbmann HK, Spitzer K, Spreckelsen C, Tolxdorff T, Ulm K (1997) Medizinische Informatik, Biometrie und Epidemiologie. De Gruyter Lehrbuch. De Gruyter, Berlin

    Book  Google Scholar 

  43. Svenstrup D, Jorgensen HL, Winther O (2015) Rare disease diagnosis: a review of web search, social media and large-scale data-mining approaches. Rare Dis 3(1):e1083145. doi:10.1080/21675511.2015.1083145

    Article  PubMed  PubMed Central  Google Scholar 

  44. Tang H, Ng JHK (2006) Googling for a diagnosis – use of Google as a diagnostic aid: internet based study. BMJ 333(7579):1143–1145. doi:10.1136/bmj.39003.640567.AE

    Article  PubMed  PubMed Central  Google Scholar 

  45. Unbekannt (2017) Zebra (medicine). https://en.wikipedia.org/w/index.php?oldid=754603425. Zugegriffen: 10. Februar 2017

    Google Scholar 

  46. Vasilevsky N, Engelstad M, Foster E, McMurry J, Mungall C, Robinson P, Köhler S, Haendel M (2017) Finally, a medical terminology that patients, doctors, and machines can all understand. http://monarch-initiative.blogspot.de/2016/03/finally-medical-terminology-that.html. Zugegriffen: 11. Februar 2017

    Google Scholar 

  47. Wiley Business (2014) The everyday life of a physician: today’s connected medical professional. http://www.interface.wiley.com/the-everyday-life-of-a-physician/. Zugegriffen: 10. Februar 2017

    Google Scholar 

  48. Winther O, Svenstrup D, Henningsen PP, Kristiásson R, Jørgensen HL (2014) FindZebra – the search engine for difficult medical cases. Orphanet J Rare Dis 9(Suppl 1):O5. doi:10.1186/1750-1172-9-S1-O5

    Article  PubMed Central  Google Scholar 

  49. Younger P (2010) Internet-based information-seeking behaviour amongst doctors and nurses: a short review of the literature. Health Info Libr J 27(1):2–10. doi:10.1111/j.1471-1842.2010.00883.x

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Zentrum für unerkannte und seltene Erkrankungen (ZusE), Universitätsklinikum Gießen und Marburg (UKGM), Baldingerstr. 1, 35043, Marburg, Deutschland

    T. Müller, A. Jerrentrup & J. R. Schäfer

Authors
  1. T. Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Jerrentrup
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. R. Schäfer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Müller.

Ethics declarations

Interessenkonflikt

T. Müller gibt an, dass kein Interessenkonflikt besteht. A. Jerrentrup: wissenschaftliche Vortragstätigkeiten für Alexion, Berlin-Chemie, Novartis, Boehringer-Ingelheim, Teva, GSK, Mundipharma, Olympus und Gambro Hospal. J.R. Schäfer: Stiftungsprofessur der Dr. Reinfried Pohl Stiftung, Marburg; wissenschaftliche Beratungstätigkeit für MSD, Sanofi, Amgen; wissenschaftliche Vortragstätigkeiten für MSD, Sanofi, SYNLAB Academy und Berlin-Chemie.

Dieser Beitrag beinhaltet keine von den Autoren durchgeführten Studien an Menschen oder Tieren.

Additional information

Redaktion

J.R. Schäfer, Marburg

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Müller, T., Jerrentrup, A. & Schäfer, J.R. Computerunterstützte Diagnosefindung bei seltenen Erkrankungen. Internist 59, 391–400 (2018). https://doi.org/10.1007/s00108-017-0218-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00108-017-0218-z

Schlüsselwörter

Keywords

Search

Navigation