| Title |
Evaluating predictive modeling algorithms to assess patient eligibility for clinical trials from routine data
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, December 2013
|
| 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
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Austria | 1 | 25% |
| India | 1 | 25% |
| United States | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| Unknown | 69 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| 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 % |
|---|---|---|
| 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% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 December 2013.
All research outputs
#7,437,164
of 22,736,112 outputs
Outputs from BMC Medical Informatics and Decision Making
#762
of 1,985 outputs
Outputs of similar age
#92,346
of 306,960 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#27
of 45 outputs
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