Title |
Evaluating predictive modeling algorithms to assess patient eligibility for clinical trials from routine data
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Published in |
BMC Medical Informatics and Decision Making, December 2013
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DOI | 10.1186/1472-6947-13-134 |
Pubmed ID | |
Authors |
Felix Köpcke, Dorota Lubgan, Rainer Fietkau, Axel Scholler, Carla Nau, Michael Stürzl, Roland Croner, Hans-Ulrich Prokosch, Dennis Toddenroth |
Abstract |
The necessity to translate eligibility criteria from free text into decision rules that are compatible with data from the electronic health record (EHR) constitutes the main challenge when developing and deploying clinical trial recruitment support systems. Recruitment decisions based on case-based reasoning, i.e. using past cases rather than explicit rules, could dispense with the need for translating eligibility criteria and could also be implemented largely independently from the terminology of the EHR's database. We evaluated the feasibility of predictive modeling to assess the eligibility of patients for clinical trials and report on a prototype's performance for different system configurations. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Austria | 1 | 25% |
India | 1 | 25% |
United States | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 75% |
Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 3 | 4% |
Unknown | 69 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 18 | 25% |
Researcher | 11 | 15% |
Student > Master | 10 | 14% |
Student > Doctoral Student | 5 | 7% |
Other | 5 | 7% |
Other | 11 | 15% |
Unknown | 12 | 17% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 20 | 28% |
Computer Science | 17 | 24% |
Social Sciences | 4 | 6% |
Agricultural and Biological Sciences | 3 | 4% |
Nursing and Health Professions | 2 | 3% |
Other | 9 | 13% |
Unknown | 17 | 24% |