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Predicting of anaphylaxis in big data EMR by exploring machine learning approaches.
Segura-Bedmar, Isabel; Colón-Ruíz, Cristobal; Tejedor-Alonso, Miguél Ángel; Moro-Moro, Mar.
Afiliação
  • Segura-Bedmar I; Computer Science Department, University Carlos III of Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain. Electronic address: isegura@inf.uc3m.es.
  • Colón-Ruíz C; Computer Science Department, University Carlos III of Madrid, Avenida de la Universidad 30, 28911 Leganés, Madrid, Spain.
  • Tejedor-Alonso MÁ; Allergy Unit, Hospital Universitario Fundación, Avenida Budapest 1, 28922 Alcorcón, Madrid, Spain; Medicine and Surgery Department, Universidad Rey Juan Carlos, 28922 Alcorcón, Madrid, Spain.
  • Moro-Moro M; Allergy Unit, Hospital Universitario Fundación, Avenida Budapest 1, 28922 Alcorcón, Madrid, Spain.
J Biomed Inform ; 87: 50-59, 2018 11.
Article em En | MEDLINE | ID: mdl-30266231
ABSTRACT
Anaphylaxis is a life-threatening allergic reaction that occurs suddenly after contact with an allergen. Epidemiological studies about anaphylaxis are very important in planning and evaluating new strategies that prevent this reaction, but also in providing a guide to the treatment of patients who have just suffered an anaphylactic reaction. Electronic Medical Records (EMR) are one of the most effective and richest sources for the epidemiology of anaphylaxis, because they provide a low-cost way of accessing rich longitudinal data on large populations. However, a negative aspect is that researchers have to manually review a huge amount of information, which is a very costly and highly time consuming task. Therefore, our goal is to explore different machine learning techniques to process Big Data EMR, lessening the needed efforts for performing epidemiological studies about anaphylaxis. In particular, we aim to study the incidence of anaphylaxis by the automatic classification of EMR. To do this, we employ the most widely used and efficient classifiers in text classification and compare different document representations, which range from well-known methods such as Bag Of Words (BoW) to more recent ones based on word embedding models, such as a simple average of word embeddings or a bag of centroids of word embeddings. Because the identification of anaphylaxis cases in EMR is a class-imbalanced problem (less than 1% describe anaphylaxis cases), we employ a novel undersampling technique based on clustering to balance our dataset. In addition to classical machine learning algorithms, we also use a Convolutional Neural Network (CNN) to classify our dataset. In general, experiments show that the most classifiers and representations are effective (F1 above 90%). Logistic Regression, Linear SVM, Multilayer Perceptron and Random Forest achieve an F1 around 95%, however linear methods have considerably lower training times. CNN provides slightly better performance (F1 = 95.6%).
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Informática Médica / Redes Neurais de Computação / Registros Eletrônicos de Saúde / Aprendizado de Máquina / Anafilaxia Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Informática Médica / Redes Neurais de Computação / Registros Eletrônicos de Saúde / Aprendizado de Máquina / Anafilaxia Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article