![]() Thus ICD-10 is used to classify data recorded under headings such as 'diagnosis', 'reason for admission', 'conditions treated', and 'reason for consultation'." ICD-10 also classifies signs, symptoms, abnormal findings, complaints, and social circumstances that may appear in a health record. ![]() The scope of ICD-10 is described in WHO's Volume 2 as follows: "The ICD-10 is primarily designed for the classification of diseases and injuries designated as a formal diagnosis. The Morphology of Neoplasms, which is a nomenclature of codes designed for use in conjunction with Chapter 11 Neoplasms, is out of scope for this ICD-10 map. Situation with explicit context Concept.id 243796009 and descendantsĪll chapters of ICD-10 are considered within scope for this ICD-10 map.Event Concept.id 272379006 and descendants.Clinical finding (disorders and findings) Concept.id 404684003 and descendants.Instead, the ICD-10 map will link a SNOMED CT source concept to the ICD-10 code which contains the meaning of the SNOMED CT concept as conceptualized by ICD-10.Īll pre-coordinated concepts issued by SNOMED International within the current international release of SNOMED CT with active status within the following SNOMED CT domains may be mapped: Due to differences in granularity, purpose and rubrics, assignment of a mapping equivalence between the SNOMED CT source and ICD-10 target code is usually not appropriate. Only domains of SNOMED CT which overlap in meaning with those of ICD-10 will be mapped. ICD-10 was created to classify a clinical concept by defining the classes (or 'buckets' of meaning) which contain the concept within the universe of ICD-10 classes. ICD-10 is a classification of diseases and related health problems with granularity of definition that has been chosen to provide utility for purposes of epidemiology and statistical reporting of mortality and morbidity. SNOMED CT supports a model of meaning which specifies correct attributes and value sets for each domain of meaning. Each concept is defined by a set of attribute-value pairs (relationships) which uniquely define it distinct from all other concepts. SNOMED CT is a comprehensive reference terminology that supports both general and highly specific concepts. The granularity and purpose of ICD-10 is different from that of SNOMED CT. The ICD-10 map is a link directed from the source SNOMED CT concept to the target ICD-10 statistical classification. Our MERRL using regularized A2C and SAC achieves up to -99.7 perplexity decrease (-43.4\% relatively) in language modeling, +25.0 accuracy increase (+40.0\% relatively) in sentiment analysis, and +5.0 F1 score increase (+30.8\% relatively) in named entity recognition over various domains, demonstrating strong generalization power on unknown test sets.This mapping is a tabular, knowledge-based cross-link from SNOMED CT to ICD-10 in which the most accurate ICD-10 target code or codes that best represents the SNOMED CT concept are linked. Our approach selects training samples that maximize information uncertainty measured by entropy, including observation entropy like empirical Shannon entropy, Min-entropy, R\'enyi entropy, and prediction entropy using mutual information, to cover more possible queries that may appear in unknown worlds. Because conventional data selection methods select training samples based on the test domain knowledge and not on real life data, they frequently fail in unknown domains like patent and Twitter. Abstract: We introduce our Maximum-Entropy Rewarded Reinforcement Learning (MERRL) framework that selects training data for more accurate Natural Language Processing (NLP).
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