Development of Keyword Trend Prediction Models for Obesity Before and After the COVID-19 Pandemic Using RNN and LSTM: Analyzing the News Big Data of South Korea

Eom, Gayeong; Byeon, Haewon

doi:10.3389/fpubh.2022.894266

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…First, in the model of intelligent department recommendations, based on the long text characteristic of patient-physician communication, due to the advantage of processing the context semantic relationship ( 38 ), we use the LSTM algorithm to construct an intelligent department recommendation model. Compared with TextCNN, random forest, KNN, and SVM algorithms, the LSTM model has a certain advantage in department recommendation.…”

Section: Discussionmentioning

confidence: 99%

An intelligent medical guidance and recommendation model driven by patient-physician communication data

Liu

Huang

et al. 2023

Front. Public Health

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Based on the online patient-physician communication data, this study used natural language processing and machine learning algorithm to construct a medical intelligent guidance and recommendation model. First, based on 16,935 patient main complaint data of nine diseases, this study used the word2vec, long-term and short-term memory neural networks, and other machine learning algorithms to construct intelligent department guidance and recommendation model. Besides, taking ophthalmology as an example, it also used the word2vec, TF-IDF, and cosine similarity algorithm to construct an intelligent physician recommendation model. Furthermore, to recommend physicians with better service quality, this study introduced the information amount of physicians' feedback to the recommendation evaluation indicator as the text and voice service quality. The results show that the department guidance model constructed by long-term and short-term memory neural networks has the best effect. The precision is 82.84%, and the F1-score is 82.61% in the test set. The prediction effect of the LSTM model is better than TextCNN, random forest, K-nearest neighbor, and support vector machine algorithms. In the intelligent physician recommendation model, under certain parameter settings, the recommendation effect of the hybrid recommendation model based on similar patients and similar physicians has certain advantages over the model of similar patients and similar physicians.

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Section: Discussionmentioning

confidence: 99%

An intelligent medical guidance and recommendation model driven by patient-physician communication data

Liu

Huang

et al. 2023

Front. Public Health

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“…In this study, soda intake was a major risk factor for adolescent obesity. The social distancing restrictions after COVID-19 changed dietary life such as an increase in food delivery and instant food intake (18)(19)(20)(21). Four out of 10 Palestinian adolescents gained weight due to increased consumption of soda, fried dishes, and sweets (18).…”

Section: Discussionmentioning

confidence: 99%

Predicting South Korean adolescents vulnerable to obesity after the COVID-19 pandemic using categorical boosting and shapley additive explanation values: A population-based cross-sectional survey

Byeon

2022

Front. Pediatr.

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ObjectiveThis study identified factors related to adolescent obesity during the COVID-19 pandemic by using machine learning techniques and developed a model for predicting high-risk obesity groups among South Korean adolescents based on the result.Materials and methodsThis study analyzed 50,858 subjects (male: 26,535 subjects, and female: 24,323 subjects) between 12 and 18 years old. Outcome variables were classified into two classes (normal or obesity) based on body mass index (BMI). The explanatory variables included demographic factors, mental health factors, life habit factors, exercise factors, and academic factors. This study developed a model for predicting adolescent obesity by using multiple logistic regressions that corrected all confounding factors to understand the relationship between predictors for South Korean adolescent obesity by inputting the seven variables with the highest Shapley values found in categorical boosting (CatBoost).ResultsIn this study, the top seven variables with a high impact on model output (based on SHAP values in CatBoost) were gender, mean sitting hours per day, the number of days of conducting strength training in the past seven days, academic performance, the number of days of drinking soda in the past seven days, the number of days of conducting the moderate-intensity physical activity for 60 min or more per day in the past seven days, and subjective stress perception level.ConclusionTo prevent obesity in adolescents, it is required to detect adolescents vulnerable to obesity early and conduct monitoring continuously to manage their physical health.

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“…TF-IDF is the most commonly used weighting algorithm and is widely used in keyword extraction and topic classification [11]. LDA topic modeling is an algorithm that is useful for extracting latent topics from big data consisting of text [12]. LDA (Latent Dirichlet Allocation) is one of the most common topic modeling methods, contributing to the extraction of coherent topics from data [13].…”

Section: B Analysis Methodsmentioning

confidence: 99%

Comparative Analysis of Telemedicine in Media Coverage Pre- and Post-COVID-19 using Unsupervised Latent Dirichlet Topic Modeling

Byeon

2024

IJACSA

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Telemedicine, driven by technology, has become a game-changer in healthcare, with the COVID-19 pandemic amplifying its significance by necessitating remote healthcare solutions. This study explores the evolution of telemedicine through news big data analysis. Our research encompassed a vast dataset from 51 media outlets (total 28,372 articles), including national and regional dailies, economic newspapers, broadcasters, and professional journals. Using LDA analysis, we delved into pre-and post-pandemic telemedicine trends comprehensively. A crucial revelation was the prominence of "medical law" in telemedicine discussions, underscoring the need for legal reforms. Keywords like "artificial intelligence" and "big data" underscored technology's pivotal role. Post-pandemic, keywords like "COVID-19," "online healthcare," and "telemedicine" surged, reflecting the pandemic's impact on remote healthcare reliance. These keywords' increased frequency highlights the pandemic's transformative influence. This study stresses addressing healthcare's legal constraints and maximizing technology's potential. To seamlessly integrate telemedicine, policy support and institutional backing are imperative. In summary, telemedicine's rise, propelled by COVID-19, signifies a healthcare paradigm shift. This study sheds light on its trajectory, emphasizing legal reforms, tech innovation, and pandemicinduced changes. The post-pandemic era must prioritize informed policy decisions for telemedicine's effective and accessible implementation.

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Development of Keyword Trend Prediction Models for Obesity Before and After the COVID-19 Pandemic Using RNN and LSTM: Analyzing the News Big Data of South Korea

Cited by 6 publications

References 29 publications

An intelligent medical guidance and recommendation model driven by patient-physician communication data

An intelligent medical guidance and recommendation model driven by patient-physician communication data

Predicting South Korean adolescents vulnerable to obesity after the COVID-19 pandemic using categorical boosting and shapley additive explanation values: A population-based cross-sectional survey

Comparative Analysis of Telemedicine in Media Coverage Pre- and Post-COVID-19 using Unsupervised Latent Dirichlet Topic Modeling

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