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

被引:1
作者
Alkhalaf, Mohammad [1 ,2 ]
Zhang, Zhenyu [1 ]
Chang, Hui-Chen [3 ]
Wei, Wenxi [4 ]
Yin, Mengyang [5 ]
Deng, Chao [6 ]
Yu, Ping [1 ]
机构
[1] Univ Wollongong, Sch Comp & Informat Technol, Ctr Digital Transformat, Wollongong, NSW, Australia
[2] Qassim Univ, Sch Comp Sci, Buraydah, Saudi Arabia
[3] Western Sydney Univ, Sch Nursing & Midwifery, Penrith, NSW, Australia
[4] Univ Wollongong, Sch Nursing, Wollongong, NSW, Australia
[5] Opal Healthcare, Sydney, NSW, Australia
[6] Univ Wollongong, Sch Med Indigenous & Hlth Sci, Wollongong, NSW, Australia
关键词
Natural language processing; malnutrition; electronic health records; residential aged care; nursing home; NURSING-HOME RESIDENTS; CLINICAL NOTES; RISK-FACTORS; PREVALENCE; NUTRITION; ASSOCIATION; HOSPITALS; MORTALITY; QUALITY; ADULTS;
D O I
10.3233/THC-230229
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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. OBJECTIVE: This study explored the risk factors for malnutrition in structured and unstructured electronic health data. METHODS: Data of weight loss and malnutrition were extracted from the de-identified EHR records of a large aged care organization in Australia. A literature review was conducted to identify causative factors for malnutrition. Natural language processing (NLP) techniques were applied to progress notes to extract these causative factors. The NLP performance was evaluated by the parameters of sensitivity, specificity and F1-Score. RESULTS: The NLP methods were highly accurate in extracting the key data, values for 46 causative variables, from the free-text client progress notes. Thirty three percent (1,469 out of 4,405) of the clients were malnourished. The structured, tabulated data only recorded 48% of these malnourished clients, far less than that (82%) identified from the progress notes, suggesting the importance of using NLP technology to uncover the information from nursing notes to fully understand the health status of the vulnerable older people in residential aged care. CONCLUSION: This study identified 33% of older people suffered from malnutrition, lower than those reported in the similar setting in previous studies. Our study demonstrates that NLP technology is important for uncovering the key information about health risks for older people in residential aged care. Future research can apply NLP to predict other health risks for older people in this setting.
引用
收藏
页码:2267 / 2278
页数:12
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