ArticleInternational journal of environmental research and public health2019
Comparison of Word Embeddings for Extraction from Medical Records.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence.Materials (Basel, Switzerland) · 2024Review
- Development of a patients' satisfaction analysis system using machine learning and lexicon-based methods.BMC health services research · 2023Article
- FindICI: Using machine learning to detect linguistic inconsistencies between code and natural language descriptions in infrastructure-as-code.Empirical software engineering · 2022Article
- A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records.PloS one · 2021Article
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2 authors.
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Abstract
This paper is an extension of the work originally presented in the 16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. Despite using electronic medical records, free narrative text is still widely used for medical records. To make data from texts available for decision support systems, supervised machine learning algorithms might be successfully applied. In this work, we developed and compared a prototype of a medical data extraction system based on different artificial neural network architectures to process free medical texts in the Russian language. Three classifiers were applied to extract entities from snippets of text. Multi-layer perceptron (MLP) and convolutional neural network (CNN) classifiers showed similar results to all three embedding models. MLP exceeded convolutional network on pipelines that used the embedding model trained on medical records with preliminary lemmatization. Nevertheless, the highest F-score was achieved by CNN. CNN slightly exceeded MLP when the biggest word2vec model was applied (F-score 0.9763).
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