ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2020
Comparison of Convolutional Neural Network Architectures and their Influence on Patient Classification Tasks Relating to Altered Mental Status.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
With the pervasiveness of Electronic Health Records in many hospital systems, the application of machine learning techniques to the field of health informatics has become much more feasible as large amounts of data become more accessible. In our experiment, we evaluated several different convolutional neural network architectures that are typically used in text classification tasks. We then tested those models based on 1,113 histories of present illness. (HPI) notes. This data was run over both sequential and multi-channel architectures, as well as a structure that implemented attention methods meant to focus the model on learning the influential data points within the text. We found that the multi-channel model performed the best with an accuracy of 92%, while the attention and sequential models performed worse with an accuracy of 90% and 89% respectively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.