Extracting Symptoms of Agitation in Dementia from Free-Text Nursing Notes Using Advanced Natural Language Processing...19th World Congress on Medical and Health Informatics, July 8-12, 2023, New South Wales, Australia

Nursing staff record observations about older people under their care in free-text nursing notes. These notes contain older people's care needs, disease symptoms, frequency of symptom occurrence, nursing actions, etc. Therefore, it is vital to develop a technique to uncover important data from these...

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Publicado en:Studies in Health Technology & Informatics Vol. 310; pp. 700 - 705
Autores principales: VITHANAGE, Dinithi, Yunshu ZHU, Zhenyu ZHANG, Chao DENG, Mengyang YIN, Ping YU
Formato: proceedings research Journal Article
Publicado: Sage Publications Inc. 2023
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2023
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        atl: Extracting Symptoms of Agitation in Dementia from Free-Text Nursing Notes Using Advanced Natural Language Processing...19th World Congress on Medical and Health Informatics, July 8-12, 2023, New South Wales, Australia
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        au:
          VITHANAGE, Dinithi
          Yunshu ZHU
          Zhenyu ZHANG
          Chao DENG
          Mengyang YIN
          Ping YU
        affil: Center for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Wollongong, Australia
      sug:
        subj:
          Natural Language Processing Utilization
          Agitation Symptoms
          Dementia Diagnosis
          Nursing Records
          Gerontologic Care
          Congresses and Conferences Australia
          Australia
          Deep Learning Methods
          Transfer (Psychology)
          Residential Care
          Comparative Studies
          Nursing Models, Theoretical
          Human
          Aged
          Aged: 65+ years
      ab: Nursing staff record observations about older people under their care in free-text nursing notes. These notes contain older people's care needs, disease symptoms, frequency of symptom occurrence, nursing actions, etc. Therefore, it is vital to develop a technique to uncover important data from these notes. This study developed and evaluated a deep learning and transfer learning-based named entity recognition (NER) model for extracting symptoms of agitation in dementia from the nursing notes. We employed a Clinical BioBERT model for word embedding. Then we applied bidirectional long-short-term memory (BiLSTM) and conditional random field (CRF) models for NER on nursing notes from Australian residential aged care facilities. The proposed NER model achieves satisfactory performance in extracting symptoms of agitation in dementia with a 75% F1 score and 78% accuracy. We will further develop machine learning models to recommend the optimal nursing actions to manage agitation.
      pubtype: Academic Journal
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        research
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      ougenre: Article
    language: English
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