• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.73-84
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• 2022

Background : Lymphedema is a progressive disorder characterized by the impairment of lymph flow from tissues to the blood circulation system. This occurs as a result of damage to the lymphatic system. Complex decongestive therapy (CDT) is a multimodal, conservative therapeutic approach that is used for the management of lymphedema. CDT consists of a combination of compression therapy, manual lymphatic drainage, exercise, and skin care. Purpose : This study aimed to provide a review of available physical therapy interventions as well as general care guidelines for patients with lymphedema. Methods : The recommendations and guidelines for physical therapy management, medical management, and general information were reviewed from the following sources: 1) The American Physical Therapy Association, 2) The Norton School of Lymphatic Therapy, and 3) The International Society of Lymphology. This review contains general information, including the medical management and the importance of physical therapy in lymphedema. Physical therapy management should be based on an assessment of the patients' presenting impairments, including based on inclusion or exclusion of physical therapy interventions. This review also outlines a step-by-step approach that starts with disease diagnosis and progression all the way through to rehabilitation as an outpatient. Conclusion : Depending on the patients' journey to recovery and the requirement for rehabilitation, physical therapy interventions should focus on the patients' needs including pain, appearance, physical function and general rehabilitation. We hope that this review will provide information on evidence-based physical therapy and general care to patients with lymphedema.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.15-18
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• 2007

Identification of humans from multiple view points is an important task for surveillance and security purposes. For optimal performance the system should use the maximum information available from sensors. Multimodal biometric systems are capable of utilizing more than one physiological or behavioral characteristic for enrollment, verification, or identification. Since gait alone is not yet established as a very distinctive feature, this paper presents an approach to fuse face and gait for identification. In this paper we will use the single camera case i.e. both the face and gait recognition is done using the same set of images captured by a single camera. The aim of this paper is to improve the performance of the system by utilizing the maximum amount of information available in the images. Fusion is considered at decision level. The proposed algorithm is tested on the NLPR database.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.738-744
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• 2013

According to the cognitive science research, the interaction intent of humans can be estimated through an analysis of the representing behaviors. This paper proposes a novel methodology for reliable intention analysis of humans by applying this approach. To identify the intention, 8 behavioral features are extracted from the 4 characteristics in human-human interaction and we outline a set of core components for nonverbal behavior of humans. These nonverbal behaviors are associated with various recognition modules including multimodal sensors which have each modality with localizing sound source of the speaker in the audition part, recognizing frontal face and facial expression in the vision part, and estimating human trajectories, body pose and leaning, and hand gesture in the spatial part. As a post-processing step, temporal confidential reasoning is utilized to improve the recognition performance and integrated human model is utilized to quantitatively classify the intention from multi-dimensional cues by applying the weight factor. Thus, interactive robots can make informed engagement decision to effectively interact with multiple persons. Experimental results show that the proposed scheme works successfully between human users and a robot in human-robot interaction.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.521-526
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• 2015

Research on how to use the biological characteristics of human to confirm the identity of the individual is being actively conducted. Electrocardiogram(: ECG) based biometric system is difficult to counterfeit and does not cause skin irritation on the subject. It can be easily combined with conventional biometrics such as fingerprint and face recognition to give multimodal biometric systems. In this thesis, biometric identification method analysing ECG waveform characteristics from Discrete Wavelet Transform(DWT) coefficients is suggested. Feature selection is performed on the 9 coefficients of DWT using the correlation analysis. The verification is achieved by using the error back propagation neural networks. Using the proposed approach on 24 subjects of MIT-BIH QT Database, 98.88% verification rate has been obtained.

• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.661-672
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• 2008

In this paper an algorithm, based on extracting a certain target of an image, is proposed that is capable of performing bilevel thresholding of image with multimodal distribution. Each pixel in the image has a membership value which is used to denote the characteristic relationship between the pixel and its belonging region (i.e. the object or background). Using the membership values of image set, a new measurement, which simultaneously measures the measure of fuzziness and the conditional entropy of the image, is calculated. Then, thresholds are found by optimally minimizing calculated measurement. In addition, a fuzzy range is defined to improve the threshold values. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively extract the meaningful target from the input image. The resulting image can preserve the object region we target very well.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.sup1
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• pp.172-176
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• 2008

FDG PET has been used as a diagnostic tool for localization of seizure focus for last 2-3 decades. In this article, the clinical usefulness of FDG PET in the management of patients with epilepsy has been reviewed, which provided the evidences to justify the medicare reimbursement for FDG PET in management of patients with epilepsy. Literature review demonstrated that FDG PET provides an important information in localization of seizure focus and determination whether a patients is a surgical candidate or not. FDG PET has been reported to have high diagnostic performance in localization of seizure focus in neocortical epilepsy as well as temporal lobe epilepsy regardless of the presence of structural lesion on MRI. Particularly, FDG PET can provide the additional information when the results from standard diagnositic modality such as interictal or video-monitored EEG, and MRI are inconclusive or discordant, and make to avoid invasive study. Furthermore, the presence of hypometabolism and extent of metabolic extent has been reported as an important predictor for seizure free outcome. However, studies suggested that more accurate localization and better surgical outcome could be expected with multimodal approach by combination of EEG, MRI, and functional studies using FDG PET or perfusion SPECT rather than using a single diagnostic modality in management of patients with epilepsy. Complementary use of FDG PET in management of epilepsy is worth for good surgical outcome in epilepsy patients.

• Physical Therapy Rehabilitation Science
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• v.10 no.2
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• pp.185-196
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• 2021

Objective: Falling is one of main accident to facilitate the physical injuries in order adults. The purpose of the systematic review was to determine the effects of postural balance training whether the recovery of falls in elderly with normal physical function or not throughout summing the selected studies quantitatively. Design: A systematic review Methods: MEDLINE and other four databases were searched up to April 20, 2021 and randomized controlled trials (RCTs) evaluating postural balance approaches on fall risks in elderly. The researched studies excluded the double studies, titles and abstract, and finally full-reported study. The selected RCTs studies were extracted characteristics of the studies and summary of results based on PICOS-SD (population, intervention, comparison, outcomes, and setting- study design) model to synthesize the papers qualitatively. Results: The review involved 22 RCT reports with 4,847 community older adults aged 65 years or over. Nineteen of the selected RCT studies reported dual or multimodal exercises show the beneficial effect for older adults compared to one-type treatment or no intervention. All of selected showed low risk in the selection, attrition, and reporting bias. However, detection bias showed low risk at 75% records of the involved RCTs and performance bias was low risk at only three records. Conclusions: The results of the systematic review propose that a standardized therapeutic approach and the intensity are needed for improving risk of falls in older adults.

• Journal of Digestive Cancer Research
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• v.3 no.1
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• pp.17-20
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• 2015

Pancreatic cancer is an aggressive tumor and only 10-20% patients are considered candidates for curative resection at diagnosis. While surgery remains the only chance for cure, prognosis is poor even after surgery due to high rate of recurrence. A complementary chemotherapy and radiotherapy in a multimodal approach has been attempted to improved prognosis after surgery. Since adjuvant chemotherapy has yielded an only modest outcome improvement, various neoadjuvant approaches with chemotherapy, chemoradiation, or chemotherapy followed by chemoradiation have been attempted. In this article, current knowledge of the various neoadjuvant approaches for pancreatic cancer will be reviewed and the role of the neoadjuvant strategies will be discussed.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.103-114
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• 2021

A conceptual model can be used to manage complexity in both the design and implementation phases of the system development life cycle. Such a model requires a firm grasp of the abstract principles on which a system is based, as well as an understanding of the high-level nature of the representation of entities and processes. In this context, models can have distinct architectural characteristics. This paper discusses model multiplicity (e.g., unified modeling language [UML]), model singularity (e.g., object-process methodology [OPM], thinging machine [TM]), and a heterogeneous model that involves multiplicity and singularity. The basic idea of model multiplicity is that it is not possible to present all views in a single representation, so a number of models are used, with each model representing a different view. The model singularity approach uses only a single unified model that assimilates its subsystems into one system. This paper is concerned with current approaches, especially in software engineering texts, where multimodal UML is introduced as the general-purpose modeling language (i.e., UML is modeling). In such a situation, we suggest raising the issue of multiplicity versus singularity in modeling. This would foster a basic appreciation of the UML advantages and difficulties that may be faced during modeling, especially in the educational setting. Furthermore, we advocate the claim that a multiplicity of views does not necessitate a multiplicity of models. The model singularity approach can represent multiple views (static, behavior) without resorting to a collection of multiple models with various notations. We present an example of such a model where the static representation is developed first. Then, the dynamic view and behavioral representations are built by incorporating a decomposition strategy interleaved with the notion of time.