Title :

Secondary use of healthcare structured data: the challenge of domain-knowledge based extraction of features

Author(s) :

Chazard, Emmanuel [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Ficheur, Gregoire [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Caron, Alexandre [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Lamer, Antoine [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Labreuche, Julien [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Cuggia, Marc [Auteur]
Université de Rennes [UR]
Genin, Michaël [Auteur]
221576|||Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS] (VALID)
Bouzille, Guillaume [Auteur]
Université de Rennes [UR]
Duhamel, Alain [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]

Journal title :

Studies in Health Technology and Informatics

Abbreviated title :

Stud Health Technol Inform

Volume number :

255

Pages :

15-19

Publication date :

2018-01

ISSN :

0926-9630

English keyword(s) :

Data reuse
feature extraction
data transformation

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

Secondary use of clinical structured data takes an important place in healthcare research. It was first described by Fayyad as "knowledge discovery in databases". Feature extraction is an important phase but received little ...
Show more >Secondary use of clinical structured data takes an important place in healthcare research. It was first described by Fayyad as "knowledge discovery in databases". Feature extraction is an important phase but received little attention. The objectives of this paper are: 1) to propose an updated representation of data reuse in healthcare, 2) to illustrate methods and objectives of feature extraction, and 3) to discuss the place of domain-specific knowledge. METHODS: an updated representation is proposed. Then, a case study consists of automatically identifying acute renal failure and discovering risk factors, by secondary use of structured data. Finally, a literature review published par Meystre et al. is analyzed. RESULTS: 1) we propose a description of data reuse in 5 phases. Phase 1 is data preprocessing (cleansing, linkage, terminological alignment, unit conversions, deidentification), it enables to construct a data warehouse. Phase 2 is feature extraction. Phase 3 is statistical and graphical mining. Phase 4 consists of expert filtering and reorganization of statistical results. Phase 5 is decision making. 2) The case study illustrates how time-dependent features can be extracted from laboratory results and drug administrations, using domain-specific knowledge. 3) Among the 200 papers cited by Meystre et al., the first and last authors were affiliated to health institutions in 74% (68% for methodological papers, and 79% for applied papers). CONCLUSIONS: features extraction has a major impact on success of data reuse. Specific knowledge-based reasoning takes an important place in feature extraction, which requires tight collaboration between computer scientists, statisticians, and health professionals.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

CHU Lille
Université de Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2019-12-09T18:20:12Z
2024-06-05T08:28:46Z