ABSTRACT
The global prevalence of prediabetes is expected to reach 8.3% (587 million people) by 2045, with 70% of people with prediabetes developing diabetes during their lifetimes. We aimed to classify community-dwelling adults with a high risk for prediabetes based on prediabetes-related symptoms and to identify their characteristics, which might be factors associated with prediabetes. We analyzed homecare nursing records (n = 26,840) of 1628 patients aged over 20 years. Using a natural language processing algorithm, we classified each nursing episode as either low-risk or high-risk for prediabetes based on the detected number and category of prediabetes-symptom words. To identify differences between the risk groups, we employed t-tests, chi-square tests, and data visualization. Risk factors for prediabetes were identified using multiple logistic regression models with generalized estimating equations. A total of 3270 episodes (12.18%) were classified as potentially high-risk for prediabetes. There were significant differences in the personal, social, and clinical factors between groups. Results revealed that female sex, age, cancer coverage as part of homecare insurance coverage, and family caregivers were significantly associated with an increased risk of prediabetes. Although prediabetes is not a life-threatening disease, uncontrolled blood glucose can cause unfavorable outcomes for other major diseases. Thus, medical professionals should consider the associated symptoms and risk factors of prediabetes. Moreover, the proposed algorithm may support the detection of individuals at a high risk for prediabetes. Implementing this approach could facilitate proactive monitoring and early intervention, leading to reduced healthcare expenses and better health outcomes for community-dwelling adults.
ABSTRACT
This study developed and validated a rule-based classification algorithm for prediabetes risk detection using natural language processing from home care nursing notes. First, we developed prediabetes-related symptomatic terms in English and Korean. Second, we used natural language processing to preprocess the notes. Third, we created a rule-based classification algorithm with 31 484 notes, excluding 315 instances of missing data. The final algorithm was validated by measuring accuracy, precision, recall, and the F1 score against a gold standard testing set (400 notes). The developed terms comprised 11 categories and 1639 words in Korean and 1181 words in English. Using the rule-based classification algorithm, 42.2% of the notes comprised one or more prediabetic symptoms. The algorithm achieved high performance when applied to the gold standard testing set. We proposed a rule-based natural language processing algorithm to optimize the classification of the prediabetes risk group, depending on whether the home care nursing notes contain prediabetes-related symptomatic terms. Tokenization based on white space and the rule-based algorithm were brought into effect to detect the prediabetes symptomatic terms. Applying this algorithm to electronic health records systems will increase the possibility of preventing diabetes onset through early detection of risk groups and provision of tailored intervention.
Subject(s)
Home Care Services , Prediabetic State , Humans , Prediabetic State/diagnosis , Natural Language Processing , Algorithms , Software , Electronic Health RecordsABSTRACT
This study aimed to examine the process of establishing a telenursing service for COVID-19 patients with mild or no symptoms admitted to a community treatment center (CTC). The process of establishing the service was reviewed, and the degree of satisfaction with the provided service was investigated based on the medical records the patients submitted at their discharge from the CTC. A total of 113 patients were admitted; the patients themselves entered the self-measured vital signs and symptoms of COVID-19 infection to the electronic questionnaires and mobile application. The nurses implemented remote nursing based on the patients' input data. The educational materials, including the video for self-measuring vital signs and the living guidelines, were prepared and arranged in advance. The telenursing protocol regarding the whole process from the patients' admission to their discharge was used and applied to five other CTCs. The non-contact counseling service's satisfaction and convenience scores were 4.65 points and 4.62 points, respectively, out of 5 points. The non-contact nursing counseling service played an important role in monitoring patients' medical conditions during the spread of COVID-19. This experience of establishing telenursing services to the CTC provides a clear direction to innovate healthcare services in future disasters.
