Extracting Symptoms of Agitation in Dementia from Free-Text Nursing Notes Using Advanced Natural Language Processing.

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.

A Five-Step Workflow to Manually Annotate Unstructured Data into Training Dataset for Natural Language Processing.

Natural Language Processing (NLP) is a powerful technique for extracting valuable information from unstructured electronic health records (EHRs). However, a prerequisite for NLP is the availability of high-quality annotated datasets. To date, there is a lack of effective methods to guide the research effort of manually annotating unstructured datasets, which can hinder NLP performance.

Machine Learning Model to Extract Malnutrition Data from Nursing Notes.

Malnutrition is a severe health problem that is prevalent in older people residing in residential aged care facilities. Recent advancements in machine learning have made it possible to extract key insight from electronic health records. To date, few researchers applied these techniques to classify nursing notes automatically.

Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia.

Emotional and mood disturbances are common in people with dementia. Non-pharmacological interventions are beneficial for managing these disturbances. However, effectively applying these interventions, particularly in the person-centred approach, is a complex and knowledge-intensive task.

Malnutrition and its contributing factors for older people living in residential aged care facilities: Insights from natural language processing of aged care records.

Background: Malnutrition is a serious health risk facing older people living in residential aged care facilities. Aged care staff record observations and concerns about older people in electronic health records (EHR), including free-text progress notes. These insights are yet to be unleashed.

Developing an Intuitive Graph Representation of Knowledge for Nonpharmacological Treatment of Psychotic Symptoms in Dementia.

Applying person-centered, nonpharmacological interventions to manage psychotic symptoms of dementia is promoted for health care professionals, particularly gerontological nurses, who are responsible for care of older adults in nursing homes. A is a graph consisting of a set of concepts that are linked together by their interrelationship and has been widely used as a formal representation of domain knowledge in health. However, there is lack of a knowledge graph for nonpharmacological treatment of psychotic symptoms in dementia.

Agitation Prevalence in People With Dementia in Australian Residential Aged Care Facilities: Findings From Machine Learning of Electronic Health Records.

Using a suite of artificial intelligence technologies, the current study sought to determine the prevalence of agitated behaviors in people with dementia in residential aged care facilities (RACFs) in Australia. Computerized natural language processing allowed extraction of agitation instances from the free-text nursing progress notes, a component of electronic health records in RACFs. In total, 59 observable agitated behaviors were found.

Developing an ontology for representing the domain knowledge specific to non-pharmacological treatment for agitation in dementia.

Introduction: A large volume of clinical care data has been generated for managing agitation in dementia. However, the valuable information in these data has not been used effectively to generate insights for improving the quality of care. Application of artificial intelligence technologies offers us enormous opportunities to reuse these data.