Automatic Selection of Clinical Trials Based on A Semantic Web Approach.
Recruitment of patients in clinical trials is nowadays preoccupying, as the inclusion rate is particularly low. The main identified factors are the multiplicity of open clinical trials, the high number and complexity of eligibility criteria, and the additional workload that a systematic search of the clinical trials a patient could be enrolled in for a physician. The principal objective of the ASTEC project is to automate the prescreening phase during multidisciplinary meetings (MDM). This paper presents the evaluation of a computerized recruitment support systems (CRSS) based on semantic web approach. The evaluation of the system was based on data collected retrospectively from a 6 month period of MDM in Urology and on 4 clinical trials of prostate cancer. The classification performance of the ASTEC system had a precision of 21%, recall of 93%, and an error rate equal to 37%. Missing data was the main issue encountered. The system was designed to be both scalable to other clinical domains and usable during MDM process.
INSERM, U1099, Rennes, F-35000, France.
Marc
M
Cuggia
INSERM, U1099, Rennes, F-35000, France.
Boris
B
Campillo-Gimenez
INSERM, U1099, Rennes, F-35000, France.
Guillaume
G
Bouzille
INSERM, U1099, Rennes, F-35000, France.
Paolo
P
Besana
INSERM, U1099, Rennes, F-35000, France.
Wassim
W
Jouini
LERTIM -Université de la méditerannée.
Jean-Charles
JC
Dufour
CRLCC Eugène Marquis.
Oussama
O
Zekri
SIB.
Isabelle
I
Gibaud
ENOVACOM, Université de Rennes 1.
Cyril
C
Garde
CHU Rennes, CIC Inserm 1414, Rennes, F-35000, France.
Regis
R
Duvauferier
D002986:Clinical Trials as Topic / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records / Q000145:classification*; D004608:Eligibility Determination / Q000379:methods; D005602:France; D020407:Internet; D000069550:Machine Learning; D009323:Natural Language Processing; D018579:Patient Selection; D012660:Semantics; D018875:Vocabulary, Controlled