Literature DB >> 28423791

Medical Text Classification Using Convolutional Neural Networks.

Mark Hughes1, Irene Li1, Spyros Kotoulas1, Toyotaro Suzumura2.   

Abstract

We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.

Keywords:  Clinical text; convolutional neural network; semantic clinical classification; sentence classification

Mesh：

Year:  2017        PMID： 28423791

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN： 0926-9630


  20 in total

1.  Medical knowledge infused convolutional neural networks for cohort selection in clinical trials.

Authors:  Chi-Jen Chen; Neha Warikoo; Yung-Chun Chang; Jin-Hua Chen; Wen-Lian Hsu
Journal:  J Am Med Inform Assoc       Date:  2019-11-01       Impact factor: 4.497

2.  Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records.

Authors:  Tao Chen; Mark Dredze; Jonathan P Weiner; Hadi Kharrazi
Journal:  J Am Med Inform Assoc       Date:  2019-08-01       Impact factor: 4.497

3.  CATAN: Chart-aware temporal attention network for adverse outcome prediction.

Authors:  Zelalem Gero; Joyce C Ho
Journal:  IEEE Int Conf Healthc Inform       Date:  2021-10-15

4.  An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource.

Authors:  Mohammed Ibrahim; Susan Gauch; Omar Salman; Mohammed Alqahtani
Journal:  PeerJ Comput Sci       Date:  2021-08-09

5.  Comparison of Convolutional Neural Network Architectures and their Influence on Patient Classification Tasks Relating to Altered Mental Status.

Authors:  Kevin Gagnon; Tami L Crawford; Jihad Obeid
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2021-01-13

6.  A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance.

Authors:  Hongxia Lu; Louis Ehwerhemuepha; Cyril Rakovski
Journal:  BMC Med Res Methodol       Date:  2022-07-02       Impact factor: 4.612

7.  Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types.

Authors:  Kevin De Angeli; Shang Gao; Ioana Danciu; Eric B Durbin; Xiao-Cheng Wu; Antoinette Stroup; Jennifer Doherty; Stephen Schwartz; Charles Wiggins; Mark Damesyn; Linda Coyle; Lynne Penberthy; Georgia D Tourassi; Hong-Jun Yoon
Journal:  J Biomed Inform       Date:  2021-11-22       Impact factor: 8.000

8.  Deep learning detects and visualizes bleeding events in electronic health records.

Authors:  Jannik S Pedersen; Martin S Laursen; Thiusius Rajeeth Savarimuthu; Rasmus Søgaard Hansen; Anne Bryde Alnor; Kristian Voss Bjerre; Ina Mathilde Kjær; Charlotte Gils; Anne-Sofie Faarvang Thorsen; Eline Sandvig Andersen; Cathrine Brødsgaard Nielsen; Lou-Ann Christensen Andersen; Søren Andreas Just; Pernille Just Vinholt
Journal:  Res Pract Thromb Haemost       Date:  2021-05-05

9.  Limitations of Transformers on Clinical Text Classification.

Authors:  Shang Gao; Mohammed Alawad; M Todd Young; John Gounley; Noah Schaefferkoetter; Hong Jun Yoon; Xiao-Cheng Wu; Eric B Durbin; Jennifer Doherty; Antoinette Stroup; Linda Coyle; Georgia Tourassi
Journal:  IEEE J Biomed Health Inform       Date:  2021-09-03       Impact factor: 7.021

10.  Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.

Authors:  Wei-Hung Weng; Kavishwar B Wagholikar; Alexa T McCray; Peter Szolovits; Henry C Chueh
Journal:  BMC Med Inform Decis Mak       Date:  2017-12-01       Impact factor: 2.796
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