Computer based extraction of phenoptypic features of human congenital anomalies from the digital literature with natural language processing techniques.
Identifieur interne : 000172 ( Ncbi/Checkpoint ); précédent : 000171; suivant : 000173Computer based extraction of phenoptypic features of human congenital anomalies from the digital literature with natural language processing techniques.
Auteurs : Gökhan Karakülah [Turquie] ; O Uz Dicle [Turquie] ; Ozgün Ko Aner [Turquie] ; Asl Suner [Turquie] ; Ça Da Can Birant [Turquie] ; Tolga Berber [Turquie] ; Sezin Canbek [Turquie]Source :
- Studies in health technology and informatics [ 0926-9630 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
- diagnostic : Malformations.
- organisation et administration : Systèmes d'aide à la décision clinique.
- Fouille de données, Humains, Intelligence artificielle, Malformations, Medical Subject Headings, Phénotype, PubMed, Périodiques comme sujet, Traitement du langage naturel.
English descriptors
- KwdEn :
- Artificial Intelligence, Congenital Abnormalities (classification), Congenital Abnormalities (diagnosis), Data Mining (methods), Decision Support Systems, Clinical (organization & administration), Humans, Medical Subject Headings, Natural Language Processing, Periodicals as Topic (classification), Periodicals as Topic (statistics & numerical data), Phenotype, PubMed (classification), PubMed (statistics & numerical data).
- MESH :
- classification : Congenital Abnormalities, Periodicals as Topic, PubMed.
- diagnosis : Congenital Abnormalities.
- methods : Data Mining.
- organization & administration : Decision Support Systems, Clinical.
- statistics & numerical data : Periodicals as Topic, PubMed.
- Artificial Intelligence, Humans, Medical Subject Headings, Natural Language Processing, Phenotype.
Abstract
The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, ,it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies.
PubMed: 25160250
Affiliations:
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pubmed:25160250Le document en format XML
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<front><div type="abstract" xml:lang="en">The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, ,it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies.</div>
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