Arrhythmia diagnosis of young martial arts athletes based on deep learning for smart medical care

2021 ◽  
Author(s):  
Jing Zhuang ◽  
Jianli Sun ◽  
Guoliang Yuan
Keyword(s):  
Deep Learning ◽  
Medical Care ◽  
Martial Arts

scholarly journals Emergency Department Online Patient-Caregiver Scheduling

2019 ◽  
Vol 33 ◽  
pp. 10013-10014
Author(s):  
Hanan Rosemarin ◽  
Ariel Rosenfeld ◽  
Sarit Kraus
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Medical Care ◽  
Real World ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Medical Personnel ◽  
Real World Data ◽  
The Right

Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED’s objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospitalspecific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED’s performance metrics.

scholarly journals A Deep Learning Model for Estimation of Patients with Undiagnosed Diabetes

2020 ◽  
Vol 10 (1) ◽  
pp. 421 ◽  
Author(s):  
Kwang Sun Ryu ◽  
Sang Won Lee ◽  
Erdenebileg Batbaatar ◽  
Jae Wook Lee ◽  
Kui Son Choi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Care ◽  
Smoking Status ◽  
Learning Model ◽  
Machine Learning Techniques ◽  
Undiagnosed Diabetes ◽  
Training Dataset ◽  
Family History Of Diabetes ◽  
Screening Model ◽  
Deep Learning Model

A screening model for undiagnosed diabetes mellitus (DM) is important for early medical care. Insufficient research has been carried out developing a screening model for undiagnosed DM using machine learning techniques. Thus, the primary objective of this study was to develop a screening model for patients with undiagnosed DM using a deep neural network. We conducted a cross-sectional study using data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2013–2016. A total of 11,456 participants were selected, excluding those with diagnosed DM, an age < 20 years, or missing data. KNHANES 2013–2015 was used as a training dataset and analyzed to develop a deep learning model (DLM) for undiagnosed DM. The DLM was evaluated with 4444 participants who were surveyed in the 2016 KNHANES. The DLM was constructed using seven non-invasive variables (NIV): age, waist circumference, body mass index, gender, smoking status, hypertension, and family history of diabetes. The model showed an appropriate performance (area under curve (AUC): 80.11) compared with existing previous screening models. The DLM developed in this study for patients with undiagnosed diabetes could contribute to early medical care.

scholarly journals Emergency Department Online Patient-Caregiver Scheduling

2019 ◽  
Vol 33 ◽  
pp. 695-701 ◽  
Author(s):  
Hanan Rosemarin ◽  
Ariel Rosenfeld ◽  
Sarit Kraus
Keyword(s):  
Emergency Department ◽  
Deep Learning ◽  
Medical Care ◽  
Real World ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Medical Personnel ◽  
Real World Data ◽  
The Right

Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED's objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospital-specific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED's performance metrics.

scholarly journals Team Coverage, On-Field Care and Injuries in Karate

2021 ◽  
Keyword(s):  
Prospective Study ◽  
Medical Care ◽  
Martial Arts ◽  
Special Focus ◽  
Severe Injuries ◽  
Karate Athletes

In this article, the most important aspects of team coverage of Karate athletes with special focus on medical care of competitions are highlighted. A proposition of what a doctor’s case for martial arts competition should contain is given. According to the Swiss accident insurances, extremities are most frequently seen. During competitions, in our own series, over 70% of injuries are mild and do not need further treatment. However, the physician in charge of a tournament needs to be prepared also for severe injuries. The authors are currently conducting an ongoing, prospective study on injuries during Swiss national competitions since 2011.

scholarly journals Visualizing veins from color skin images using convolutional neural networks

2020 ◽  
Vol 13 (04) ◽  
pp. 2050020
Author(s):  
Chaoying Tang ◽  
Yimin Yuan ◽  
Shuhang Xia ◽  
Gehua Ma ◽  
Biao Wang
Keyword(s):  
Deep Learning ◽  
Medical Care ◽  
Near Infrared ◽  
Venous Access ◽  
Color Transfer ◽  
Auxiliary Equipment ◽  
Transfer Scheme ◽  
Medical Treatments ◽  
Vein Access ◽  
Optimization Parameters

Intravenous cannulation is the most important phase in medical practices. Currently, limited literature is available about visibility of veins and the characteristics of patients associated with difficult intravenous access. In modern medical treatment, a major challenge is locating veins for patients who have difficult venous access. Presently, some products of vein locators are available in the market to improve vein access, but they need auxiliary equipment such as near infrared (NIR) illumination and camera, which add weight and cost to the devices, and cause inconveniences to daily medical care. In this paper, a vein visualization algorithm based on the deep learning method was proposed. Based on a group of synchronous RGB/NIR arm images, a convolutional neural network (CNN) model was designed to implement the mapping from RGB to NIR images, where veins can be detected from skin. The model has a simple structure and less optimization parameters. A color transfer scheme was also proposed to make the network adaptive to the images taken by smartphone in daily medical treatments. Comprehensive experiments were conducted on three datasets to evaluate the proposed method. Subjective and objective evaluations showed the effectiveness of the proposed method. These results indicated that the deep learning-based method can be used for visualizing veins in medical care applications.

scholarly journals A Deep Learning-Based Text Classification of Adverse Nursing Events

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenjing Lu ◽  
Wei Jiang ◽  
Na Zhang ◽  
Feng Xue
Keyword(s):  
Deep Learning ◽  
Health System ◽  
Medical Care ◽  
Text Classification ◽  
Rapid Development ◽  
Classification Model ◽  
Diagnosis And Treatment ◽  
Event Analysis ◽  
Classification Methods

Adverse nursing events occur suddenly, unpredictably, or unexpectedly during course of clinical diagnosis and treatment processes in the hospitals. These events adversely affect the patient’s diagnosis and treatment results and even increase the patient’s pain and burden. Additionally, It is high likely to cause accidents and disputes and affect normal medical work and personnel safety and is not conducive to the development of the health system. Due to the rapid development of modern medicine, health and safety of patients have become the most concerned issue in society and patient safety is an important part of medical care management. Research and events have shown that classified management of adverse nursing events, event analysis, and improvement measures are beneficial, specifically to the health system, to continuously improve the quality of medical care and reduce the occurrence of adverse nursing events. In the management of adverse nursing events, it is very important to categorize the text reports of adverse nursing events and divide these into different categories and levels. Traditional reports of adverse nursing events are mostly unstructured and simple data, often relying on manual classification, which is difficult to analyze. Furthermore, data is relatively inaccurate and practical reference significance is not obvious. In this paper, we have extensively evaluated various deep learning-based classification methods which are specifically designed for the healthcare systems. It becomes possible with the development of science and technology; text classification methods based on deep learning are gradually entering people’s field of vision. Additionally, we have proposed a text classification model for adverse nursing events in the health system. Experiments and data comparison test of both the proposed deep learning-based method and existing methods in the text classification of nursing adverse events effect are performed. These results show the exceptional performance of the proposed mechanism in terms of various evaluation metrics.

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