• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.4
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• pp.209-214
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• 2011

This study suggests a Healthcare System for elderly and disabled who have mobility impairment and use a wheelchair for long time. Seating long time in a wheelchair without reducing pressure causes high risk of developing pressure sores. Pressure sores come with great deal of pain and often lead to develop complication. Not only it takes time and effort to treat pressure sores but also increases medical expenses. Therefore, we will develop a device to help to prevent pressure sores by measuring pressure distribution while seating in a wheelchair and wirelessly send information to user device to check pressure distribution in real time. The equipment to measure body pressure is composed of FSR sitting mat which is a sensor measuring part and an user terminal which is a monitoring part. The designed mat is matrix formed FSR sensor to measure pressure. The sensor send measured data to the controller which is connected to the end of the mat, and then the collected data are sent to an user terminal through a bluetooth. Developing a pressure monitoring system will help to prevent those who have mobility impairment to manage pressure sores and furthermore relieve their burden of medical expenses.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.254-266
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• 2021

The Internet of Medical Things (IoMTs) are to be considered an investment and an improvement to respond effectively and efficiently to patient needs, as it reduces healthcare costs, provides the timely attendance of medical responses, and increases the quality of medical treatment. However, IoMT devices face exposure from several security threats that defer in function and thus can pose a significant risk to how private and safe a patient's data is. This document works as a comprehensive review of modern approaches to achieving security within the Internet of Things. Most of the papers cited here are used been carefully selected based on how recently it has been published. The paper highlights some common attacks on IoMTs. Also, highlighting the process by which secure authentication mechanisms can be achieved on IoMTs, we present several means to detect different attacks in IoMTs

• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2341-2347
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• 2021

Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a^99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.4
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• pp.145-152
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• 2019

Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.4
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• pp.65-74
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• 2022

Purpose - With the increasing medical demands of the public, the development of future Internet medical care has come to represent a major problem. Therefore, the purpose of this study is to discuss future development strategies for Interne medical care while taking China's Internet hospitals as an example case. Research design, data, and methodology - This study conducted a case study of China's Internet hospitals to summarize the fundamental problems faced by Internet hospitals and propose future development strategies to overcome these problems for Internet medical care. Result - Although Internet hospitals have been regarded as the ultimate product of Internet medical care, from the perspective of the government, medical institutions, platforms builders and maintainers, and patients, they still face some basic issues. Conclusion - This study concludes that the government and medical institutions play an important role in the future development of Internet medical care and suggests that the government should make overall plans for the policies and standards and should play the main role in enhancing the public trust in Internet medical care, while medical institutions should take steps such as seizing policy opportunities, driving online and offline collaborations, and constructing suitable evaluation systems to promote the development of Internet medical care.

• Journal of Sensor Science and Technology
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• v.30 no.3
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• pp.170-174
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• 2021

As the rising attention to the medical and healthcare issue, Bio-MEMS (Micro electro mechanical systems) platform such as bio sensor, cell culture system, and microfluidics device has been studied extensively. Bio-MEMS platform mostly has high resolution structure made by biocompatible material such as polydimethylsiloxane (PDMS). In addition, three dimension structure has been applied to the bio-MEMS. Lithography can be used to fabricate complex structure by multiple process, however, non-rectangular cross section can be implemented by introducing optical apparatus to lithography technic. X-ray lithography can be used even for sub-micron scale. Here in, we demonstrated lines with round shape cross section using the tilted gold absorber which was deposited on the oblique structure as the X-ray mask. This structure was used as a mold for PDMS. Molded PDMS was applied to the cell culture platform. Moreover, molded PDMS was bonded to flat PDMS to utilize to the sub-micro channel. This work has potential to the large area bio-MEMS.

• Journal of Information Processing Systems
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• v.18 no.3
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• pp.302-310
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• 2022

Efficient use of limited blood products is becoming very important in terms of socioeconomic status and patient recovery. To predict the appropriateness of patient-specific transfusions for the intensive care unit (ICU) patients who require real-time monitoring, we evaluated a model to predict the possibility of transfusion dynamically by using the Medical Information Mart for Intensive Care III (MIMIC-III), an ICU admission record at Harvard Medical School. In this study, we developed an explainable machine learning to predict the possibility of red blood cell transfusion for major medical diseases in the ICU. Target disease groups that received packed red blood cell transfusions at high frequency were selected and 16,222 patients were finally extracted. The prediction model achieved an area under the ROC curve of 0.9070 and an F1-score of 0.8166 (LightGBM). To explain the performance of the machine learning model, feature importance analysis and a partial dependence plot were used. The results of our study can be used as basic data for recommendations related to the adequacy of blood transfusions and are expected to ultimately contribute to the recovery of patients and prevention of excessive consumption of blood products.

• International Journal of Contents
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• v.17 no.4
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• pp.1-15
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• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.1-7
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• 2022

The number of patients with many complications grows with the increase of aging population. As the elders and severely ill patients spend most of their time in bed, it leads to Pressure Injuries (PI) such as bedsores. Unfortunately, there is no method to automatically detect changes in patient's posture which leads to the need for a caregiver every set of times when the patient needs to be moved. Many studies are conducted to solve this inefficient problem. Yet, these studies require costly devices or use methods that disturb patient's sleeping environment. Those methods are mostly hard to implement in practice due to these reasons. We propose a method to detect posture using a three-axis acceleration sensor from the wrist band. We developed a wearable watch that measures sleep-related data. We analyzed 40 people's sleep data with a wearable module and watch to measure their postures such as supine, left-side, and right-side. Then, we compared the classified posture from the watch with the wearable module and achieved 90% accuracy. Therefore, we concluded that only by using the wearable watch, we can detect the sleeping position without any new equipment or system to diagnose the patients without discomfort during their daily lives.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3991-4007
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• 2022

In the public healthcare, a computational system that can automatically and efficiently detect and classify falls from a video sequence has significant potential. With the advancement of deep learning, which can extract temporal and spatial information, has become more widespread. However, traditional 3D CNNs that usually adopt shallow networks cannot obtain higher recognition accuracy than deeper networks. Additionally, some experiences of neural network show that the problem of gradient explosions occurs with increasing the network layers. As a result, an enhanced three-dimensional ResNet-based method for fall detection (3D-ERes-FD) is proposed to directly extract spatio-temporal features to address these issues. In our method, a 50-layer 3D residual network is used to deepen the network for improving fall recognition accuracy. Furthermore, enhanced residual units with four convolutional layers are developed to efficiently reduce the number of parameters and increase the depth of the network. According to the experimental results, the proposed method outperformed several state-of-the-art methods.