منابع مشابه
GALEN Based Formal Representation of ICD10
OBJECTIVES The main objective is to create a knowledge-intensive coding support tool for the International Classification of Diseases (ICD10), which is based on formal representation of ICD10 categories. Beyond this task the resulting ontology could be reused in various ways. Decidability is an important issue for computer-assisted coding; consequently the ontology should be represented in desc...
متن کاملIdentifying anatomical concepts associated with ICD10 diseases
Unlike recent biomedical terminologies, the International Classification of Diseases (ICD) does not state any explicit associations between a given disease and the corresponding anatomical structure(s). As a consequence, clinical repositories coded with ICD cannot be searched by anatomical structure. The objective of this work is to find associations between diseases from ICD10 and anatomical s...
متن کاملCrowdsourcing for ICD10 Code to Concept Relationships
In this work we leverage crowdsourcing in connection with machine learning techniques to validate candidate ICD10 Code to UMLS concept relationships that we generate. Our immediate use is in natural language understanding and machine learning approaches to automatically code electronic health record documents with ICD codes. Beyond auto-coding, the relationships will aid a wide variety of futur...
متن کاملPreparing Disease Surveillance Systems for ICD10
Introduction The compliance date for the ICD9-ICD10 transition is October 1, 2015. The hospitals have started the ICD9-ICD10 transition. However, not all data providers will transition the data at the same time. In order to facilitate some coherence to the data during this transition period, user interface and data processing functionalities have been developed in ESSENCE to allow usage of both...
متن کاملLIMSI ICD10 coding Experiments on CépiDC Death Certificate Statements
We describe LIMSI experiments in ICD10 coding of death certificate statements with the CépiDc dataset of the CLEF eHealth 2016 Track 2. We tested a classifier with humanly-interpretable output, based on IR-style ranking of candidate ICD10 diagnoses. A tf.idf-weighted bagof-feature vector was built for each training set code by merging all the statements found for this code in the training data....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: British Journal of Psychiatry
سال: 1999
ISSN: 0007-1250,1472-1465
DOI: 10.1192/bjp.175.6.587