Selection of patients for clinical trials: An interactive web-based system

Artificial Intelligence in Medicine

Volumn 31, Issue 3, 2004, Pages 241-254

  Fink, Eugene ^a   Kokku, Princeton K ^a   Nikiforou, Savvas ^a   Hall, Lawrence O ^a   Goldgof, Dmitry B ^a   Krischer, Jeffrey P ^a  

^a UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

Breast cancer; Clinical trials; Cost reduction; Expert system

Indexed keywords

FUNCTIONS; PATIENT MONITORING; PROBLEM SOLVING; WORLD WIDE WEB;

CLINICAL TRIALS; MEDICAL RECORDS;

MEDICAL IMAGING;

ARTICLE; BREAST BIOPSY; BREAST CANCER; CLINICAL TRIAL; EXPERT SYSTEM; HEALTH CARE COST; HUMAN; MAMMOGRAPHY; MEDICAL RESEARCH; PATIENT SELECTION; PRIORITY JOURNAL;

BREAST NEOPLASMS; CLINICAL TRIALS; COST CONTROL; DIAGNOSTIC TESTS, ROUTINE; ELIGIBILITY DETERMINATION; EXPERT SYSTEMS; FEMALE; HUMANS; INTERNET; PATIENT SELECTION; TEACHING; USER-COMPUTER INTERFACE;

EID: 4143063686     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2004.01.017     Document Type: Article

References (48)

1. Keppel KG, Pearcy JN, Wagener DK. Trends in racial and ethnic-specific rates for the health status indicators: United States, 1990-98. Healthy people 2000. Statistical notes, vol. 23. 2002.
2. Cancer facts and figures 2002. American Cancer Society; 2002.
3. Gennari JH, Reddy M. Participatory design and an eligibility screening tool. In: Proceedings of the American Medical Informatics Association Annual Fall Symposium, 2000. p. 290-4.
4. Gotay C.C. Accrual to cancer clinical trials: directions from the research literature. Soc Sci Med. 33(5):1991;569-577
5. Kotwall C., Mahoney L.J., Myers R.E., Decoste L. Reasons for non-entry in randomized clinical trials for breast cancer: a single institutional study. J Surg Oncol. 50:1992;125-129
6. Séroussi B, Bouaud J, Antoine É-C. Enhancing clinical practice guideline compliance by involving physicians in the decision process. In Horn W, Shahar Y, Lindberg G, Andreassen S, Wyatt JC, editors. Artificial intelligence in medicine. Berlin, Germany: Springer-Verlag; 1999. p. 76-85.
7. Tu S.W., Kemper C.A., Lane N.M., Carlson R.W., Musen M.A. A methodology for determining patients' eligibility for clinical trials. J Methods Inform Med. 32(4):1993;317-325
8. Yusuf S., Held P., Teo K.K., Toretsky E.R. Selection of patients for randomized controlled trials: implications of wide or narrow eligibility criteria. Stat Med. 9:1990;73-86
9. Smith B.J., McNeely M.D.D. The influence of an expert system for test ordering and interpretation on laboratory investigations. Clin Chem. 45(8):1999;1168-1175
10. Van Walraven C., Naylor C.D. Do we know what inappropriate laboratory utilization is? A systematic review of laboratory clinical audits. J Am Med Assoc. 280(6):1998;550-558
11. Bareford D., Hayling A. Inappropriate use of laboratory services: long term combined approach to modify request patterns. Br Med J. 301(6764):1990;1305-1307
12. Perraro F., Rossi P., Liva C., Bulfoni A., Ganzini G., Giustinelli A. Inappropriate emergency test ordering in a general hospital: preliminary reports. Quality Assurance Health Care. 4:1992;77-81
13. Musen M.A., Tu S.W., Das A.K., Shahar Y. EON: A component-based approach to automation of protocol-directed therapy. J Am Med Inform Assoc. 3(6):1996;367-388
14. 2: A decision-support tool for decreasing physicians' uncertainty regarding patient eligibility for HIV treatment protocols. In: Proceedings of the Seventeenth Annual Symposium on Computer Applications in Medical Care, 1993. p. 429-33.
15. Bouaud J., Séroussi B., Antoine É.-C., Gozy M., Khayat D., Boisvieux J.-F. Hypertextual navigation operationalizing generic clinical practice guidelines for patient-specific therapeutic decisions. J Am Med Inform Assoc. 5(Suppl.):1998;488-492
16. Bouaud J, Séroussi B, Antoine E.-C, Zelek L, Spielmann M. Reusing ONCODOC, a guideline-based decision support system, across institutions: A successful experiment in sharing medical knowledge. In: Proceedings of the American Medical Informatics Association Annual Symposium, vol. 7, 2000.
17. Séroussi B., Bouaud J., Antoine É.-C. Users' evaluation of ONCODOC, a breast cancer therapeutic guideline delivered at the point of care. J Am Med Inform Assoc. 6(5):1999;384-389
18. Séroussi B., Bouaud J., Antoine É.-C. ONCODOC: A successful experiment of computer-supported guideline development and implementation in the treatment of breast cancer. Artif Intell Med. 22(1):2001;43-64
19. Hammond P., Sergot M.J. Computer support for protocol-based treatment of cancer. J Logic Program. 26(2):1996;93-111
20. McNeely M.D.D., Smith B.J. An interactive expert system for the ordering and interpretation of laboratory tests to enhance diagnosis and control utilization. Can Med Inform. 2(3):1995;16-19
21. Papaconstantinou C., Theocharous G., Mahadevan S. An expert system for assigning patients into clinical trials based on Bayesian networks. J Med Syst. 22(3):1998;189-202
22. Theocharous G. An expert system for assigning patients into clinical trials based on Bayesian networks. Master's thesis. Computer Science and Engineering Department, University of South Florida; 1996.
23. Bhanja S, Fletcher LM, Hall LO, Goldgof DB, Krischer JP. A qualitative expert system for clinical trial assignment. In: Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference, 1998. p. 84-8.
24. Breitfeld P.P., Ullrich F., Anderson J., Crist W.M. Web-based decision support for clinical trial eligibility determination in an international clinical trials network. Controlled Clin Trials. 24(6):2003;702-710
25. Fallowfield L., Ratcliffe D., Souhami R. Clinicians' attitudes to clinical trials of cancer therapy. Eur J Cancer. 33(13):1997;2221-2229
26. Carlson RW, Tu SW, Lane NM, Lai TL, Kemper CA, Musen MA, Shortliffe EH. Computer-based screening of patients with HIV/AIDS for clinical trial eligibility. Online J Curr Clin Trials 1995;4(179).
27. Ohno-Machado L., Gennari J.H., Murphy S., Jain N.L., Tu S.W., Oliver D.E., Pattison-Gordon E. The guideline interchange format: a model for representing guidelines. J Am Med Inform Assoc. 5(4):1998;357-372
28. Lindberg D.A.B., Humphreys B.L., McCray A.T. The unified medical language system. J Methods Inform Med. 4(32):1993;281-291
29. Rubin DL, Gennari JH, Musen MA. Knowledge representation and tool support for critiquing clinical trial protocols, In: Proceedings of the American Medical Informatics Association Fall Symposium, 2000. p. 724-8.
30. Rubin DL, Gennari JH, Srinivas S, Yuen A, Kaizer H, Musen MA, Silva JS. Tool support for authoring eligibility criteria for cancer trials. In: Proceedings of the American Medical Informatics Association Annual Symposium, 1999. p. 369-73.
31. Wang D., Peleg M., Tu S.W., Shortliffe E.H., Greenes R.A. Representation of clinical practice guidelines for computer-based implementations. Med Inform. 10:2001;285-289
32. Eriksson H. Specification and generation of custom-tailored knowledge-acquisition tools. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, vol. 1, 1993. p. 510-8.
33. Gil Y, Tallis M. A script-based approach to modifying knowledge bases. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence, 1997. p. 377-83.
34. Tallis M. A script-based approach to modifying knowledge-based systems. In: Proceedings of the Tenth Innovative Applications of Artificial Intelligence Conference, 1998. p. 1183-95.
35. Tallis M, Gil Y. Designing scripts to guide users in modifying knowledge-based systems. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence, 1999. p. 242-9.
36. Blythe J, Kim J, Ramachandran S, Gil Y. An integrated environment for knowledge acquisition. In: Proceedings of the International Conference on Intelligent User Interfaces, 2001. p. 13-20.
37. Musen MA. Automated generation of model-based knowledge acquisition tools. San Mateo, CA: Morgan Kaufmann; 1989.
38. Puerta A.R., Egar J.W., Tu S.W., Musen M.A. A multiple-method knowledge-acquisition shell for the automatic generation of knowledge- acquisition tools. Knowledge Acquisition. 4(2):1992;171-196
39. Puerta AR, Eriksson H, Egar JW, Musen MA. Generation of knowledge-acquisition tools from reusable domain ontologies. Technical Report KSL-92-81. Computer Science Department, Stanford University; 1992.
40. Johnson PD, Musen MA. Development of a guideline authoring tool with PROTÉGÉ-II based on the DILEMMA generic protocol and guideline model. Technical Report SMI-96-0620. Stanford Medical Informatics Group, Stanford School of Medicine; 1996.
41. Musen MA, Fergerson RW, Grosso WE, Noy NF, Crubezy M, Gennari JH. Component-based support for building knowledge-acquisition systems. In: Proceedings of the Conference on Intelligent Information Processing of the International Federation for Information Processing World Computer Congress, 2000.
42. Díez F.J., Mira J., Iturralde E., Zubillaga S. DIAVAL, a Bayesian expert system for echocardiography. Artif Intell Med. 10(1):1997;59-73
43. Onisko A, Druzdzel MJ, Wasyluk H. Application of Bayesian belief networks to diagnosis of liver disorders. In: Proceedings of the Third Conference on Neural Networks and Their Applications, 1997. p. 730-6.
44. Oniśko A, Druzdzel MJ, Wasyluk H. Learning Bayesian network parameters from small data sets: Application of noisy-Or gates. In: Proceedings of the Workshop on Bayesian and Causal Networks: From Inference to Data Mining, 2000.
45. Wang H, Druzdzel MJ. User interface tools for navigation in conditional probability tables and elicitation of probabilities in Bayesian networks. In: Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, 2000. p. 617-25.
46. Nikiforou S. Selection of clinical trials: Knowledge representation and acquisition. Master's thesis, Department of Computer Science and Engineering, University of South Florida, 2002.
47. Crama Y, Hammer PL. Boolean functions. Berlin, Germany: Springer-Verlag; 2001.
48. Kenneth RH. Discrete mathematics and its applications. 3rd ed. New York, NY: McGraw-Hill; 1988.