Mortality, morbidity, and risk factors in Taiwan, 1990–2017: findings from the Global Burden of Disease Study 2017

Wu, Yungang; Lo, Wei Cheng; Lu, Tsung Hsueh; Chang, Shu Sen; Lin, Hsien Ho; Chan, Chang-Chuan

doi:10.1016/j.jfma.2020.11.014

Cited by 12 publications

(7 citation statements)

References 32 publications

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“…Taiwan implemented the National Health Insurance (NHI) system in 1995, and it has a high coverage and utilization rate. However, the healthcare system in Taiwan is facing immense challenges due to rapid population aging [17]. Approximately 9996 people in Taiwan died from DM in 2019, with 2736 of the deaths being recorded to be among people 85 years of age and above [18].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients

Barsasella

Bah

Mishra³

et al. 2022

Medicina

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Background and Objectives: Taiwan is among the nations with the highest rates of Type 2 Diabetes Mellitus (T2DM) and Hypertension (HTN). As more cases are reported each year, there is a rise in hospital admissions for people seeking medical attention. This creates a burden on hospitals and affects the overall management and administration of the hospitals. Hence, this study aimed to develop a machine learning (ML) model to predict the Length of Stay (LoS) and mortality among T2DM and HTN inpatients. Materials and Methods: Using Taiwan’s National Health Insurance Research Database (NHIRD), this cohort study consisted of 58,618 patients, where 25,868 had T2DM, 32,750 had HTN, and 6419 had both T2DM and HTN. We analyzed the data with different machine learning models for the prediction of LoS and mortality. The evaluation was done by plotting descriptive statistical graphs, feature importance, precision-recall curve, accuracy plots, and AUC. The training and testing data were set at a ratio of 8:2 before applying ML algorithms. Results: XGBoost showed the best performance in predicting LoS (R2 0.633; RMSE 0.386; MAE 0.123), and RF resulted in a slightly lower performance (R2 0.591; RMSE 0.401; MAE 0.027). Logistic Regression (LoR) performed the best in predicting mortality (CV Score 0.9779; Test Score 0.9728; Precision 0.9432; Recall 0.9786; AUC 0.97 and AUPR 0.93), closely followed by Ridge Classifier (CV Score 0.9736; Test Score 0.9692; Precision 0.9312; Recall 0.9463; AUC 0.94 and AUPR 0.89). Conclusions: We developed a robust prediction model for LoS and mortality of T2DM and HTN inpatients. Linear Regression showed the best performance for LoS, and Logistic Regression performed the best in predicting mortality. The results showed that ML algorithms can not only help healthcare professionals in data-driven decision-making but can also facilitate early intervention and resource planning.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients

Barsasella

Bah

Mishra³

et al. 2022

Medicina

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“…2 Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China. 3 Institute of Intelligent Systems, Deakin University, Burwood, Australia. 4 The Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.…”

Section: Supplementary Informationmentioning

confidence: 99%

Interpretable CNN for ischemic stroke subtype classification with active model adaptation

Zhang

Wang

Pei

et al. 2022

BMC Med Inform Decis Mak

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Background TOAST subtype classification is important for diagnosis and research of ischemic stroke. Limited by experience of neurologist and time-consuming manual adjudication, it is a big challenge to finish TOAST classification effectively. We propose a novel active deep learning architecture to classify TOAST. Methods To simulate the diagnosis process of neurologists, we drop the valueless features by XGB algorithm and rank the remaining ones. Utilizing active learning framework, we propose a novel causal CNN, in which it combines with a mixed active selection criterion to optimize the uncertainty of samples adaptively. Meanwhile, KL-focal loss derived from the enhancement of Focal loss by KL regularization is introduced to accelerate the iterative fine-tuning of the model. Results To evaluate the proposed method, we construct a dataset which consists of totally 2310 patients. In a series of sequential experiments, we verify the effectiveness of each contribution by different evaluation metrics. Experimental results show that the proposed method achieves competitive results on each evaluation metric. In this task, the improvement of AUC is the most obvious, reaching 77.4. Conclusions We construct a backbone causal CNN to simulate the neurologist process of that could enhance the internal interpretability. The research on clinical data also indicates the potential application value of this model in stroke medicine. Future work we would consider various data types and more comprehensive patient types to achieve fully automated subtype classification.

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“…Moreover, the Asian population is susceptible to the repercussions of obesity at lower body weights than other populations [2]. A high body mass index (BMI) is one of the leading five risk factors for disability-adjusted life years in Taiwan [3]. Consequently, BMI categories for Asian populations are adjusted to lower figures [4] with a BMI of 27 and over being considered obese in Taiwan [5] in comparison to the World Health Organisation's cut off of 30.…”

Section: Introductionmentioning

confidence: 99%

Healthcare Service Providers’ Perspectives on Sociocultural Aspects Affecting Weight Management Activities Amongst People with Obesity in Taiwan—A Qualitative Study

Leu,

Huang,

Chen

et al. 2024

Nutrients

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The prevalence of obesity and morbid obesity in Taiwan has risen sharply in recent decades, as in other parts of the world, necessitating urgent action to prevent and curb its detrimental effects. Asian populations are susceptible to the repercussions of obesity at a lower body weight. A higher BMI is associated with more frequent outpatient visits, in-hospital admissions, higher medical costs, and a lower quality of life. However, effective weight management approaches are unlikely to be maintained in the long term without assimilation into daily lifestyle practices. This qualitative study, based on semi-structured interviews with 14 doctors, dieticians, and nurses who work to control the weight of people with obesity, explored and identified multilevel barriers in the context of daily life to improve the efficacy and execution of weight management strategies. They considered diets, physical activity, and sleep as key weight management activities. The cultural and psychosocial aspects of daily life were observed to have an impact upon weight management, particularly family conflicts due to cultural dynamics and socially and culturally reinforced food practices. To improve population weight, less-recognised aspects need to be addressed alongside the inclusion of mental health specialists in weight management protocols and policy interventions to minimise obesogenic practices and create environments conducive to weight management.

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Mortality, morbidity, and risk factors in Taiwan, 1990–2017: findings from the Global Burden of Disease Study 2017

Cited by 12 publications

References 32 publications

A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients

A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients

Interpretable CNN for ischemic stroke subtype classification with active model adaptation

Healthcare Service Providers’ Perspectives on Sociocultural Aspects Affecting Weight Management Activities Amongst People with Obesity in Taiwan—A Qualitative Study

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