Soft Computing

, Volume 21, Issue 18, pp 5207–5221 | Cite as

Crowdsourced healthcare knowledge creation using patients’ health experience-ontologies

  • Mye Sohn
  • Sunghwan Jeong
  • Jongmo Kim
  • Hyun Jung Lee
Focus

Abstract

In this research, we developed CHEKC framework for creation and integration of crowdsourced healthcare knowledge using experience-ontologies. The purpose is to provide patients’ healthcare information which contains similar healthcare experiences including conditions and symptoms and integrates the features and relations in the particular patients’ data according to users’ queries. To do this, we developed three modules and ontologies. The modules are Crowdsourced Health Data Manipulation Module (CHMM), Health Ontology-based Relevant Patient Finding Module (HRFM), and Ontology-guided Healthcare Knowledge Integration Module (OKIM). CHMM is developed to transform patients’ data to structured cases with problem-solution. The cases are stored in CHEKC Patient Ontology. HRFM is developed to find relevant cases according to the user’s query using CHEKC Upper Ontology. To do this, ensemble semantic similarity is proposed using semantic similarity and fuzzy C-means clustering and the relevant cases are stored in Interim Ontology. OKIM is developed for the integration of the relevant cases using SWRL rule-base. However, it is not guaranteed to find suitable rules and generate necessary knowledge from the rule-base. To relieve the problem, ontology-guided knowledge integration is proposed, which supports the inferring relations among classes in CHEKC Interim Ontology. CHEKC framework provides the integrated healthcare information and knowledge which are generated through the illustrated processes using the selected similar healthcare cases with users’ query. In particular, the cases are constructed by crowdsourcing on healthcare-featured social media and are based on patients’ healthcare experiences from the perspectives of patients. Through the conducting of two experiments, we proved the effectiveness of CHEKC framework. The conducted experiments proved the efficiency of CHEKC framework by the reduction in search volumes and the improvement in accuracy of query results.

Keywords

Crowdsourced Health care Ontology Semantic similarity Fuzzy Clustering 

References

  1. AMN Healthcare (2013) 2013 Survey of social media and mobile usage by healthcare professionals job search and career trends. AMN Healthcare, pp 1–27. http://www.amnhealthcare.com
  2. Anjum A, Bloodsworth P, Branson A, Hauer T, McClatchey R, Munir K, Rogulin D, Shamdasani J (2007) The requirements for ontologies in medical data integration: a case study. In: International database engineering and applications symposium (IDEAS2007), pp 308–314Google Scholar
  3. ASHP (2014) ASHP statement on use of social media by pharmacy professionals. Automation and Information Technology-Statements, pp 22–24Google Scholar
  4. Avogadri R, Valentini G (2009) Fuzzy ensemble clustering based on random projections for DNA microarray data analysis. Artif Intell Med 45(2):173–183CrossRefGoogle Scholar
  5. Bernhardt M, Alber J, Gold RS (2014) A social media primer for professionals: digital do’s and don’ts. Health Promot Pract 15(2):168–172CrossRefGoogle Scholar
  6. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Springer, New York and LondonCrossRefMATHGoogle Scholar
  7. Boyd T, Lee B, Savel T, Stinn J, Kesarinath G (2011) An example of the use of Public Health Grid (PHGrid) technology during the 2009 H1N1 influenza pandemic. Int J Grid Util Comput 2:148–155CrossRefGoogle Scholar
  8. Ceusters W, Smith B, De Moor G (2005) Ontology-based integration of medical coding systems and electronic patient records. In: International congress of the European Federation for Medical Informatics (MIE2005)Google Scholar
  9. Chattopadhyay S, Pratihar DK, De Sarkar SC (2012) A comparative study of fuzzy C-means algorithm and entropy-based fuzzy clustering algorithms. Comput Inform 30:701–720MATHGoogle Scholar
  10. Chiauzzi E, Rodarte C, DasMahapatra P (2015) Patient-centered activity monitoring in the self-management of chronic health conditions. BMC Med 2015:1–6Google Scholar
  11. Chiauzzi E, Eichler GS, Wicks P (2016) Crowdsourcing advancements in health care research: applications for cancer treatment discoveries. Elsevier, Oncology Informatics, pp 307–329Google Scholar
  12. Chauhan B, George R, Coffin J (2012) Social media and you: what every physician needs to know. J Med Pract Manag 28(3):206–209Google Scholar
  13. Cybercitizen Health v9.0 (2010) Manhattan Research, New York, NYGoogle Scholar
  14. Dizon DS, Graham D, Thompson MA, Johnson LJ, Johnston C, Fisch MJ, Miller R (2012) Practical guidance: the use of social media in oncology practice. J Oncol Pract 8(5):114–124CrossRefGoogle Scholar
  15. Elkin N (2008) How America Searches: health and wellness. iCrossing. http://www.rx-edge.com/
  16. Farnan JM, Sulmasy LS, Worster BK, Chaudhry HJ, Rhyne JA, Arora VM (2013) Online medical professionalism: patient and public relationships: policy statement from the American College of Physicians and the Federation of State Medical Boards. Ann Intern Med 158(8):620–627CrossRefGoogle Scholar
  17. Fogelson NS, Rubin ZA, Ault KA (2013) Beyond likes and tweets: an in-depth look at the physician social media landscape. Clin Obstet Gynecol 56(3):495–508CrossRefGoogle Scholar
  18. George DR, Rovniak LS, Kraschnewski JL (2013) Dangers and opportunities for social media in medicine. Clin Obstet Gynecol 56(3):453–462CrossRefGoogle Scholar
  19. Grajales FJ III, Sheps S, Ho K, Novak-Lauscher H, Eysenbach G (2014) Social media: a review and tutorial of applications in medicine and health care. J Med Internet Res 16:e13CrossRefGoogle Scholar
  20. Jiang JJ, Conrath DW (1997) Semantic similarity based on corpus statistics and lexical taxonomy. In: International conference on research in computational linguistics (ROCLING X), pp 19–33Google Scholar
  21. Juarez JM, Salort J, Palma J, Marin R (2007) Case representation ontology for case retrieval systems in medical domains. In: IASTED international multi-conference: artificial intelligence and applications (AIAP’07), pp 168–173Google Scholar
  22. Kear T, Harrington M, Bhattacharya A (2015) Partnering with patients using social media to develop a hypertension management instrument. J Am Soc Hypertens 9(9):725–734CrossRefGoogle Scholar
  23. Ko EJ, Lee HJ, Lee JW (2007) Ontology-based context modeling and reasoning for U-HealthCare. IEICE Trans Inf Syst E90-D:1262–1270Google Scholar
  24. Lambert KM, Barry P, Stokes G (2012) Risk management and legal issues with the use of social media in the healthcare setting. J Healthc Risk Manag 31(4):41–47CrossRefGoogle Scholar
  25. Leacock C, Chodorow M (1998) Combining local context and WordNet similarity for word sense identification. WordNet: an electronic lexical database. MIT Press, pp 265–283Google Scholar
  26. Lee HJ, Kim HS (2015) eHealth Recommendation service system using ontology and case-based reasoning. In: International symposium on cloud and service computing (SC22015)Google Scholar
  27. Lee HJ, Sohn M (2016) Health service knowledge management to support medical group decision making. In: Future internet and next generation networks (FINGNET2016)Google Scholar
  28. Levy M (2007) Online health: assessing the risks and opportunity of social and one-to-one media. Jupiter ResearchGoogle Scholar
  29. Lin D (1998) An information-theoretic definition of similarity. In: International conference on machine learning (ICML’98), pp 296–304Google Scholar
  30. Migliardi M, Gaudina M, Brogni A (2012) Pervasive services and mobile devices may support human memory and enhance daily efficiency. Int J Space-Based Situat Comput 2:175–186CrossRefGoogle Scholar
  31. Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C (2013) A new dimension of health care: systemic review of the uses, benefits, and limitations of social media for health care professionals. J Med Internet Res 15(4):e85Google Scholar
  32. Moore P, Thomas A, Tadros G, Xhafa F, Barolli L (2013) Detection of the onset of agitation in patients with dementia: real-time monitoring and the application of big-data solutions. Int J Space-Based Situat Comput 3:136–154CrossRefGoogle Scholar
  33. Nakamura C, Bromberg M, Bhargava S, Wicks P, Zeng QT (2012) Mining online social network data for biomedical research: a comparison of clinicians’ and patients’ perceptions about ALS treatments. J Med Internet Res 14(3):e90CrossRefGoogle Scholar
  34. Ogbuji C (2011) A framework ontology for computer-based patient record systems. In: International conference on biomedical ontology (ICBO2011), pp 217–223Google Scholar
  35. O’Hara B, Fox BJ, Donahue B (2013) Social media in pharmacy: heeding its call, leveraging its power. J Am Pharm Assoc 53(6):561–564CrossRefGoogle Scholar
  36. Peck JL (2014) Social media in nursing education: responsible integration for meaningful use. J Nurs Educ 53:164–169CrossRefGoogle Scholar
  37. Petrakis EGM, Varelas G, Hliaoutakis A, Raftopoulou P (2006) X-similarity: computing semantic similarity between concepts from different ontologies. J Digit Inf Manag 4:233–237Google Scholar
  38. Resnik P (1995) Using information content to evaluate semantic similarity in a taxonomy. In: International joint conference artificial intelligence (IJCAU’95), pp 448–453Google Scholar
  39. Rodriguez MA, Egenhofer MJ (2003) Determining semantic similarity among entity classes from different ontologies. IEEE Trans Knowl Data Eng 15:442–456CrossRefGoogle Scholar
  40. Rothman M, Gnanasakthy A, Wicks P, Papadopoulos EJ (2015) Can we use social media to support content validity of patient-reported outcome instruments in medical product development? Value Health 18(1):1–4CrossRefGoogle Scholar
  41. Schlenoff CI, Ivester RW, Libes DE, Denno PO, Szykman S (1999) An analysis of existing ontological systems for applications in manufacturing and healthcare. National Institute of Standards and Technology, pp 1–25Google Scholar
  42. Schroeder EB, Desai J, Schmittdiel JA, Paolino AR, Schneider JL, Goodrich GK, Lawrence JM, Newton KM, Nichols GA, O’Connor PJ, Fitz-Randolph M, Steiner JF (2015) An innovative approach to informing research: gathering perspectives on diabetes care challenges from an online patient community. Interact J Med Res 4(2):e13CrossRefGoogle Scholar
  43. Tversky A (1977) Features of similarity. Psychol Rev 84(4):327–352CrossRefGoogle Scholar
  44. Ventola CL (2014) Social media and health care professionals: benefits, risks, and best practices. Pharm Ther 39(7):491–499Google Scholar
  45. Von Muhlen M, Ohno-Machado L (2012) Reviewing social media use by clinicians. J Am Med Inform Assoc 19(5):777–781CrossRefGoogle Scholar
  46. Wu Z, Palmer M (1994) Verb semantics and lexical selection. In: Proceedings of the 32nd annual meeting on associations for computational linguistics (ACL’94), pp 133–138Google Scholar
  47. Ye M, Liu W, Wei J, Hu X (2016) Fuzzy \(c\)-means and cluster ensemble with random projection for big data clustering. Math Prob Eng 2016:1–13MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Mye Sohn
    • 1
  • Sunghwan Jeong
    • 1
  • Jongmo Kim
    • 1
  • Hyun Jung Lee
    • 2
  1. 1.Department of Industrial EngineeringSungkyunkwan UniversitySuwonKorea
  2. 2.Yonsei Institute of Convergence Technology, School of Integrated TechnologyYonsei UniversityIncheonKorea

Personalised recommendations