• 한국병원경영학회지
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• 제28권3호
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• pp.1-14
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• 2023

Purposes: It is very important to establish a clinical data warehouse based on a common data model to offset the different data characteristics of each medical institution and for drug surveillance. This study attempted to establish a clinical data warehouse for Dankook university hospital for drug surveillance, and to derive the main items necessary for development. Methodology/Approach: This study extracted the electronic medical record data of Dankook university hospital tracked for 9 years from 2013 (2013.01.01. to 2021.12.31) to build a clinical data warehouse. The extracted data was converted into the Observational Medical Outcomes Partnership Common Data Model (Version 5.4). Data term mapping was performed using the electronic medical record data of Dankook university hospital and the standard term mapping guide. To verify the clinical data warehouse, the use of angiotensin receptor blockers and the incidence of liver toxicity were analyzed, and the results were compared with the analysis of hospital raw data. Findings: This study used a total of 670,933 data from electronic medical records for the Dankook university clinical data warehouse. Excluding the number of overlapping cases among the total number of cases, the target data was mapped into standard terms. Diagnosis (100% of total cases), drug (92.1%), and measurement (94.5%) were standardized. For treatment and surgery, the insurance EDI (electronic data interchange) code was used as it is. Extraction, conversion and loading were completed. R language-based conversion and loading software for the process was developed, and clinical data warehouse construction was completed through data verification. Practical Implications: In this study, a clinical data warehouse for Dankook university hospitals based on a common data model supporting drug surveillance research was established and verified. The results of this study provide guidelines for institutions that want to build a clinical data warehouse in the future by deriving key points necessary for building a clinical data warehouse.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.267-275
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• 2023

This paper presents the development of specialized software for annotating volume-of-interest on ¹⁸F-FDG PET/CT images with the goal of facilitating the studies and diagnosis of head and neck cancer (HNC). To achieve an efficient annotation process, we employed the SE-Norm-Residual Layer-based U-Net model. This model exhibited outstanding proficiency to segment cancerous regions within ¹⁸F-FDG PET/CT scans of HNC cases. Manual annotation function was also integrated, allowing researchers and clinicians to validate and refine annotations based on dataset characteristics. Workspace has a display with fusion of both PET and CT images, providing enhance user convenience through simultaneous visualization. The performance of deeplearning model was validated using a Hecktor 2021 dataset, and subsequently developed semi-automatic annotation functionalities. We began by performing image preprocessing including resampling, normalization, and co-registration, followed by an evaluation of the deep learning model performance. This model was integrated into the software, serving as an initial automatic segmentation step. Users can manually refine pre-segmented regions to correct false positives and false negatives. Annotation images are subsequently saved along with their corresponding ¹⁸F-FDG PET/CT fusion images, enabling their application across various domains. In this study, we developed a semi-automatic annotation software designed for efficiently generating annotated lesion images, with applications in HNC research and diagnosis. The findings indicated that this software surpasses conventional tools, particularly in the context of HNC-specific annotation with ¹⁸F-FDG PET/CT data. Consequently, developed software offers a robust solution for producing annotated datasets, driving advances in the studies and diagnosis of HNC.

• 한국CDE학회논문집
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• 제15권4호
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• pp.297-305
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• 2010

Medical image acquisition techniques such as CT and MRI have disadvantages in that the numerous time and efforts are needed. Furthermore, a great amount of radiation exposure is an inherent proberty of the CT imaging technique, a number of side-effects are expected from such method. To improve such conventional methods, a number of novel methods that can obtain 3D medical images from a few X-ray images, such as algebraic reconstruction technique (ART), have been developed. Such methods deform a generic model of the internal body part and fit them into the X-ray images to obtain the 3D model; the initial shape, therefore, affects the entire fitting process in a great deal. From this fact, we propose a novel method that can generate a 3D vertebraic generic model based on the statistical database of CT scans in this study. Moreover, we also discuss a method to generate patient-tailored generic model using the facts obtained from the statistical analysis. To do so, the mesh topologies of CT-scanned 3D vertebra models are modified to be identical to each other, and the database is constructed based on them. Furthermore, from the results of a statistical analysis on the database, the tendency of shape distribution is characterized, and the modeling parameters are extracted. By using these modeling parameters for generating the patient-tailored generic model, the computational speed and accuracy of ART can greatly be improved. Furthermore, although this study only includes an application to the C1 (Atlas) vertebra, the entire framework of our method can be applied to other body parts generally. Therefore, it is expected that the proposed method can benefit the various medical imaging applications.

• Asian Pacific Journal of Cancer Prevention
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• 제15권12호
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• pp.4839-4842
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• 2014

Background: Although the Cox's proportional hazard model is the popular approach for survival analysis to investigate significant risk factors of cancer patient survival, it is not appropriate in the case of log-term disease free survival. Recently, cure rate models have been introduced to distinguish between clinical determinants of cure and variables associated with the time to event of interest. The aim of this study was to use a cure rate model to determine the clinical associated factors for cure rates of patients with breast cancer (BC). Materials and Methods: This prospective cohort study covered 305 patients with BC, admitted at Shahid Faiazbakhsh Hospital, Tehran, during 2006 to 2008 and followed until April 2012. Cases of patient death were confirmed by telephone contact. For data analysis, a non-mixed cure rate model with Poisson distribution and negative binomial distribution were employed. All analyses were carried out using a developed Macro in WinBugs. Deviance information criteria (DIC) were employed to find the best model. Results: The overall 1-year, 3-year and 5-year relative survival rates were 97%, 89% and 74%. Metastasis and stage of BC were the significant factors, but age was significant only in negative binomial model. The DIC also showed that the negative binomial model had a better fit. Conclusions: This study indicated that, metastasis and stage of BC were identified as the clinical criteria for cure rates. There are limited studies on BC survival which employed these cure rate models to identify the clinical factors associated with cure. These models are better than Cox, in the case of long-term survival.

• Asian Pacific Journal of Cancer Prevention
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• 제15권7호
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• pp.3331-3333
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• 2014

Background: The results of previous researches that analyzed the association between genetic polymorphisms of transcription factor-7-like 2 (TCF7L2, rs7903146) and polycystic ovary syndrome (PCOS) were conflicting. Current systematic analysis was conducted to re-explore this association using updated materials. Materials and Methods: The PubMed database was used for data collection and the final search was conducted on January 3, 2014. For TCF7L2 rs7903146, a non-signficiant slight increase in risk of PCOS development was observed under three genetic models (dominant model: OR=1.06, 95%CI: 0.93-1.21, p>0.05; recessive model: OR=1.12, 95%CI: 0.87-1.43, p> 0.05; homozygous model: OR=1.14, 95%CI: 0.87-1.47, p>0.05). In the subgroup analyses in Asian group, allele susceptibility of PCOS was calculated (allele model: OR=1.00, 95%CI: 0.74-1.35, p>0.05; dominant model: OR=0.98, 95%CI: 0.71-1.35, p>0.05; recessive model: OR=1.79, 95%CI: 0.33-9.84, p>0.05; homozygous model: OR=1.78, 95%CI: 0.32-9.80, p>0.05), the differences were again not statistically significant. Conclusions: The findings of this systemic analysis suggest that the polymorphism of TCF7L2 rs7903146 may not be associated with the susceptibility to PCOS.

• 한국인공지능학회지
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• 제11권4호
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• pp.9-13
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• 2023

In this study, we explored the potential of integrating interactive AI callbot technology into the medical consultation domain as part of a broader service development initiative. Aimed at enhancing patient satisfaction, the AI callbot was designed to efficiently address queries from hospitals' primary users, especially the elderly and those using phone services. By incorporating an AI-driven callbot into the hospital's customer service center, routine tasks such as appointment modifications and cancellations were efficiently managed by the AI Callbot Agent. On the other hand, tasks requiring more detailed attention or specialization were addressed by Human Agents, ensuring a balanced and collaborative approach. The deep learning model for voice recognition for this study was based on the Transformer model and fine-tuned to fit the medical field using a pre-trained model. Existing recording files were converted into learning data to perform SSL(self-supervised learning) Model was implemented. The ANN (Artificial neural network) neural network model was used to analyze voice signals and interpret them as text, and after actual application, the intent was enriched through reinforcement learning to continuously improve accuracy. In the case of TTS(Text To Speech), the Transformer model was applied to Text Analysis, Acoustic model, and Vocoder, and Google's Natural Language API was applied to recognize intent. As the research progresses, there are challenges to solve, such as interconnection issues between various EMR providers, problems with doctor's time slots, problems with two or more hospital appointments, and problems with patient use. However, there are specialized problems that are easy to make reservations. Implementation of the callbot service in hospitals appears to be applicable immediately.

• Archives of Plastic Surgery
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• 제33권5호
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• pp.606-611
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• 2006

Purpose: Animal models of a chronic wound are yet to be fully developed, and animal studies on this subject has yet to take place. The purpose of this study is to create the foundation for research on chronic wound healing based on a swine model, the most similar to that of a human. Methods: Three female 2-3 month old 'yolkshires' were used. Total of eight full thickness skin defects, $6{\times}3cm$ sized, were created on the back of each pigs. Three groups were created for comparison; Group I (n=4) was left as they were after full skin thickness excision, while the excised tissues of Group II (n=3) were turned inside out and sutured so that the epidermis would come in contact with the fascia. Group III (n=3) were excised full skin thickness in depth and silicone blocks were implanted in them. Dressing was not practised so that the wounds would be vulnerable to infection. Results: In Group III, the skin contraction rate was the least among the three groups for each three weeks of observation respectively. Also during the three weeks, bacteral colonization was at the highest among the comparison. On the third week, inflammatory cells were still active, but the generations of epidermis and collagen synthesis were detected minimally. Conclusion: The Group III was relatively the most similar model of chronic wounds. and modification of the silicone blocks, could provide us with a very effective chronic skin wound model similar to human.

• Asian Pacific Journal of Cancer Prevention
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• 제15권1호
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• pp.441-447
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• 2014

Background: Multi-state models are appropriate for cancer studies such as gastrectomy which have high mortality statistics. These models can be used to better describe the natural disease process. But reaching that goal requires making assumptions like Markov and homogeneity with time. The present study aims to investigate these hypotheses. Materials and Methods: Data from 330 patients with gastric cancer undergoing surgery at Iran Cancer Institute from 1995 to 1999 were analyzed. To assess Markov assumption and time homogeneity in modeling transition rates among states of multi-state model, Cox-Snell residuals, Akaikie information criteria and Schoenfeld residuals were used, respectively. Results: The assessment of Markov assumption based on Cox-Snell residuals and Akaikie information criterion showed that Markov assumption was not held just for transition rate of relapse (state 1 ${\rightarrow}$ state 2) and for other transition rates - death hazard without relapse (state 1 ${\rightarrow}$ state 3) and death hazard with relapse (state 2 ${\rightarrow}$ state 3) - this assumption could also be made. Moreover, the assessment of time homogeneity assumption based on Schoenfeld residuals revealed that this assumption - regarding the general test and each of the variables in the model- was held just for relapse (state 1 ${\rightarrow}$ state 2) and death hazard with a relapse (state 2 ${\rightarrow}$ state 3). Conclusions: Most researchers take account of assumptions such as Markov and time homogeneity in modeling transition rates. These assumptions can make the multi-state model simpler but if these assumptions are not made, they will lead to incorrect inferences and improper fitting.

• 한국콘텐츠학회논문지
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• 제10권10호
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• pp.246-254
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• 2010

오늘날 국내외적으로 의료정보에 대한 관심과 요구가 나날이 증가함에 따라 의료현장에서 이를 전문적으로 취급하고 관리할 의료정보 전문가 양성이 중요한 현안이 되었다. 그러나 국내 대학에서는 아직까지 변화하는 의료정보 교육환경에 적합한 교수 학습 방법을 마련하지 못하고 있다. 이러한 상황에서 웹기반 PBL 수업이 대안이 될 수 있다. 본 논문에서는 의료정보 교육을 위해 웹 기반 PBL 교수 학습 콘텐츠 개발 모형을 제시하였다. 제시된 웹 기반 PBL 모형은 기존의 PBL에 LMS를 활용한 온라인 학습활동을 추가한 방식이며, 한 학기동안 수업에 적용한 결과 강의평가 객관식 점수가 4.64로 나타나 전년도의 강의식 수업 평가인 4.17보다 0.47점 더 향상되었고, 학생들의 서술적 평가 역시 매우 긍정적으로 나타났다. 또한 학습자 개인블로그의 댓글과 트랙백의 횟수를 조사한 결과 학습자간 상호작용이 활발히 이루어졌음을 확인할 수 있었다.

• Journal of Korean Neurosurgical Society
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• 제40권3호
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• pp.180-185
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• 2006

Objective : Middle cerebral artery occlusion[MCAO] has widely been used to produce ischemic brain lesions. The lesions induced by MCAO tend to be variable in size because of the variance in the collateral blood supply found in the mouse brain. To establish a less invasive and reproducible focal ischemia model in mice, we modified the technique used for rat photo thrombosis model. Methods : Male C57BL/6 mice were subjected to focal cerebral ischemia by photothrombosis of cortical microvessels. Cerebral infarction was produced by intraperitoneal injection of Rose Bengal, a photosensitive dye and by focal illumination through the skull. Motor impairment was assessed by the accelerating rotarod and staircase tests. The brain was perfusion-fixed for histological determination of infarct volume four weeks after stroke. Results : The lesion was located in the frontal and parietal cortex and the underlying white matter was partly affected. A relatively constant infarct volume was achieved one month after photothrombosis. The presence of the photothrombotic lesion was associated with severe impairment of the motor performance measured by the rotarod and staircase tests. Conclusion : Photothrombotic infarction in mice is highly reproducible in size and location. This procedure can provide a simple method to produce cerebral infarction in a unilateral motor cortex lesion. In addition, it can provide a suitable model for study of potential neuroprotective and therapeutic agents in human stroke.