In many domains such as medicine, training data is in short supply. In such cases, external knowledge is often helpful in building predictive models. We propose a novel method to incorporate publicly available domain expertise to build accurate models. Specifically, we use word2vec models trained on a domain-specific corpus to estimate the relevance of each feature's text description to the prediction problem. We use these relevance estimates to rescale the features, causing more important features to experience weaker regularization. We apply our method to predict the onset of five chronic diseases in the next five years in two genders and two age groups. Our rescaling approach improves the accuracy of the model, particularly when there are few positive examples. Furthermore, our method selects 60 fewer features, easing interpretation by physicians. Our method is applicable to other domains where feature and outcome descriptions are available.
Another body of related work can be found in the field of text classification. Some authors used expert-coded ontologies such as the Unified Medical Language System (UMLS) to engineer and extract features from clinical text and used these features to identify patients with cardiovascular diseases ( @cite_2 @cite_3 ). In non-clinical settings, others have used ontology in the Open Directory Project ( @cite_7 ) and Wikipedia ( @cite_13 ).
@cite_13 @cite_7 @cite_3 @cite_2
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The use of domain ontologies is becoming increasingly popular in Medical Natural Language Processing Systems. A wide variety of knowledge bases in multiple languages has been integrated into the Unified Medical Language System (UMLS) to create a huge knowledge source that can be accessed with diverse lexical tools. MetaMap (and its java version MMTx) is a tool that allows extracting medical concepts from free text, but currently there not exists a Spanish version. Our ongoing research is centered on the application of biomedical concepts to cross-lingual text classification, what makes it necessary to have a Spanish MMTx available. We have combined automatic translation techniques with biomedical ontologies and the existing English MMTx to produce a Spanish version of MMTx. We have evaluated different approaches and applied several types of evaluation according to different concept representations for text classification. Our results prove that the use of existing translation tools such as Google Translate produce translations with a high similarity to original texts in terms of extracted concepts. While in the past taxonomic and ontological knowledge was traditionally produced by small groups of co-located experts, today the production of such knowledge has a radically different shape and form. For example, potentially thousands of health professionals, scientists, and ontology experts will collaboratively construct, evaluate and maintain the most recent version of the International Classification of Diseases (ICD-11), a large ontology of diseases and causes of deaths managed by the World Health Organization. In this work, we present a novel web-based tool — iCAT Analytics — that allows to investigate systematically crowd-based processes in knowledge-production systems. To enable such investigation, the tool supports interactive exploration of pragmatic aspects of ontology engineering such as how a given ontology evolved and the nature of changes, discussions and interactions that took place during its production process. While iCAT Analytics was motivated by ICD-11, it could potentially be applied to any crowd-based ontology-engineering project. We give an introduction to the features of iCAT Analytics and present some insights specifically for ICD-11. Medical terminologies and ontologies are important tools for natural language processing of health record narratives. To account for the variability of language use, synonyms need to be stored in a semantic resource as textual instantiations of a concept. Developing such resources manually is, however, prohibitively expensive and likely to result in low coverage. To facilitate and expedite the process of lexical resource development, distributional analysis of large corpora provides a powerful data-driven means of (semi-)automatically identifying semantic relations, including synonymy, between terms. In this paper, we demonstrate how distributional analysis of a large corpus of electronic health records – the MIMIC-II database – can be employed to extract synonyms of SNOMED CT preferred terms. A distinctive feature of our method is its ability to identify synonymous relations between terms of varying length. The 2010 i2b2 VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. @PARASPLIT These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.