• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.146-157
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• 2024

Despite numerous air mattresses marketed to prevent Pressure Ulcers (PU), none have fully succeeded due to residual pressure surpassing critical levels. We introduces an innovative medical bed system aiming at complete PU prevention. This system employs a unique 4-bar link mechanism, moving keys up and down to manage body pressure. Each of the 17 keys integrates a sensor controller, reading pressure from 10 sensors. By regulating motor input, we maintain body pressure below critical levels. Keys are equipped with a servo drive and sensor controller, linked to the main controller via two CAN series. Using fuzzy or PI/IP controllers, we adjust keys to minimize total error, dispersing body pressure and ensuring comfort. In case of controller failure, keys alternate swiftly, preventing ulcer development. Through experimental tests under varied conditions, the fuzzy controller with tailored membership functions demonstrated swift performance. PI control showed rapid convergence, while IP control exhibited slower convergence and oscillations near zero error. Our specialized medical robot bed, incorporating 4-bar links and pressure sensors, underwent testing with three controllers-fuzzy, PI, and IP-showcasing their effectiveness in keeping body pressure below critical ulcer levels. Experimental results validate the proposed approach's efficacy, indicating potential for complete PU prevention.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5513-5518
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• 2013

IFN-${\gamma}$ plays an indirect anti-cancer role through the immune system but may have direct negative effects on cancer cells. It regulates the viability of gastric cancer cells, so we examined whether it affects their proliferation and how that might be brought about. We exposed AGS, HGC-27 and GES-1 gastric cancer cell lines to IFN-${\gamma}$ and found significantly reduced colony formation ability. Flow cytometry revealed no effect of IFN-${\gamma}$ on apoptosis of cell lines and no effect on cell aging as assessed by ${\beta}$-gal staining. Microarray assay revealed that IFN-${\gamma}$ changed the mRNA expression of genes related to the cell cycle and cell proliferation and migration, as well as chemokines and chemokine receptors, and immunity-related genes. Finally, flow cytometry revealed that IFN-${\gamma}$ arrested the cells in the G1/S phase. IFN-${\gamma}$ may slow proliferation of some gastric cancer cells by affecting the cell cycle to play a negative role in the development of gastric cancer.

• Perinatology
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• v.29 no.4
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• pp.189-194
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• 2018

Congenital neuroblastoma is a rare disease. Placental metastasis is extremely rare and poor prognosis has been reported in neonates. Mirror syndrome could occur in mother with placental metastasis with possibilities of hypertension and edema. We report a case of detection of left suprarenal mass in fetus at $31^{+5}weeks^{\prime}$ gestation. Mother presented with palpitation, edema, headache, and hypertension. Maternal 24 hours urine vanillylmandelic acid (VMA) and normetanephrine (NME) level at 34 weeks' gestation were elevated. Consequently, emergent cesarean section was done. Based on abdominal ultrasonography and whole body magnetic resonance imaging, left adrenal tumor with liver metastasis was suspected. Neuroblastoma was confirmed by liver and placenta biopsy. Chemotherapy was started with Pediatric Oncology Group 9243 at day 7 and changed into Children's Oncology Group 3961 due to cholestasis and poor response during 2nd cycle. Plasma exchange was done for aggravated direct hyperbilirubinemia. The baby expired at 73 days due to multi-organ failure. Maternal symptoms were completely resolved in 2 weeks after delivery along with normalization of the elevated level of VMA and NME. We report a first case of mirror syndrome in Korean mother and fetus resulting from metastatic congenital neuroblastoma to placenta.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.453-458
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• 2024

Optical Character Recognition is the technology that recognizes text in images and converts them into digital format. Deep learning-based OCR is being used in many industries with large quantities of recorded data due to its high recognition performance. To improve medical services, deep learning-based OCR was actively introduced by the medical industry. In this paper, we discussed trends in OCR engines and medical OCR and provided a roadmap for development of medical OCR. By using natural language processing on detected text data, current medical OCR has improved its recognition performance. However, there are limits to the recognition performance, especially for non-standard handwriting and modified text. To develop advanced medical OCR, databaseization of medical data, image pre-processing, and natural language processing are necessary.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.9-21
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• 2021

The study aims to identify convergence and trends in technology-based patent data for the biohealth sector in IP5 countries (KR, EP, JP, US, CN) and present the direction of development in that industry. We used patent co-classification analysis-based network analysis and TF-IDF-based text mining as the principal methodology to understand the current state of technology convergence. As a result, the technology convergence cluster in the biohealth industry was derived in three forms: (A) Medical device for treatment, (B) Medical data processing, and (C) Medical device for biometrics. Besides, as a result of trend analysis based on technology convergence results, it is analyzed that Korea is likely to dominate the market with patents with high commercial value in the future as it is derived as a market leader in (B) medical data processing. In particular, the field is expected to require technology convergence activation policies and R&D support strategies for the technology as the possibility of medical data utilization by domestic bio-health companies expands, along with the policy conversion of the "Data 3 Act" passed by the National Assembly in January 2019.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.520-522
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• 2022

의료 인공지능은 특정 진단에서 높은 정확도를 보이지만 모델의 신뢰성 문제로 인해 활발하게 쓰이지 못하고 있다. 이에 따라 인공지능 모델의 진단에 대한 원인 설명의 필요성이 대두되었고 설명가능한 의료 인공지능에 관한 연구가 활발히 진행되고 있다. 하지만 MRI 등 의료 영상 인공지능 분야에서 주로 진행되고 있으며, 이미지 형태가 아닌 전자의무기록 데이터 (Electronic Health Record, EHR) 를 기반으로 한 모델의 설명가능성 연구는 EHR 데이터 자체의 복잡성 때문에 활발하게 진행 되지 않고 있다. 본 논문에서는 전자의무기록 데이터인 MIMIC-III (Medical Information Mart for Intensive Care) 를 전처리 및 그래프로 표현하고, GCT (Graph Convolutional Transformer) 모델을 학습시켰다. 학습 후, 어텐션 흐름 그래프를 시각화해서 모델의 예측에 대한 직관적인 설명을 제공한다.

• Analytical Science and Technology
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• v.9 no.2
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• pp.192-197
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• 1996

The purpose of this study is to develop new V-free Ti alloys which have good mechanical properties and corrosion resistance. Although pure Ti has an excellent biocompatibility and corrosion resistance in body, it is inferior to Ti alloys in mechanical properties, and Ti-6Al-4V which has good mechanical properties was known to be cytotoxic due to the alloying element V. New Ti based alloys which do not contain the toxic metallic components were developed by the alloy design technique. Their corrosion and mechanical characteristics were compared with pure Ti and Ti-6Al-4V in this study. The results showed that Ti-20Zr-3Nb-3Ta-0.2Pd-1In and Ti-5AI-4Zr-2.5Mo exhibit good mechanical oroperties and an excellent corrosion resistance in 0.9% NaCl solution.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.