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Abstract Despite the increasing use of lung ultrasound (LUS) in the evaluation of respiratory disease, operators’ competence constrains its effectiveness. We developed a deep-learning (DL) model for multi-label classification using LUS and validated its performance and efficacy on inter-reader variability. We retrospectively collected LUS and labeled as normal, B-line, consolidation, and effusion from patients undergoing thoracentesis at a tertiary institution between January 2018 and January 2022. The development and internal testing involved 7580 images from January 2018 and December 2020, and the model’s performance was validated on a temporally separated test set (n = 985 images collected after January 2021) and two external test sets (n = 319 and 54 images). Two radiologists interpreted LUS with and without DL assistance and compared diagnostic performance and agreement. The model demonstrated robust performance with AUCs: 0.93 (95% CI 0.92–0.94) for normal, 0.87 (95% CI 0.84–0.89) for B-line, 0.82 (95% CI 0.78–0.86) for consolidation, and 0.94 (95% CI 0.93–0.95) for effusion. The model improved reader accuracy for binary discrimination (normal vs. abnormal; reader 1: 87.5–95.6%, p = 0.004; reader 2: 95.0–97.5%, p = 0.19), and agreement (k = 0.73–0.83, p = 0.01). In conclusion, the DL-based model may assist interpretation, improving accuracy and overcoming operator competence limitations in LUS.

Abstract BackgroundThe bidirectional encoder representations from transformers (BERT) model has attracted considerable attention in clinical applications, such as patient classification and disease prediction. However, current studies have typically progressed to application development without a thorough assessment of the model’s comprehension of clinical context. Furthermore, limited comparative studies have been conducted on BERT models using medical documents from non–English-speaking countries. Therefore, the applicability of BERT models trained on English clinical notes to non-English contexts is yet to be confirmed. To address these gaps in literature, this study focused on identifying the most effective BERT model for non-English clinical notes. ObjectiveIn this study, we evaluated the contextual understanding abilities of various BERT models applied to mixed Korean and English clinical notes. The objective of this study was to identify the BERT model that excels in understanding the context of such documents. MethodsUsing data from 164,460 patients in a South Korean tertiary hospital, we pretrained BERT-base, BERT for Biomedical Text Mining (BioBERT), Korean BERT (KoBERT), and Multilingual BERT (M-BERT) to improve their contextual comprehension capabilities and subsequently compared their performances in 7 fine-tuning tasks. ResultsThe model performance varied based on the task and token usage. First, BERT-base and BioBERT excelled in tasks using classification ([CLS]) token embeddings, such as document classification. BioBERT achieved the highest F1F1 ConclusionsThis study highlighted the effectiveness of various BERT models in a multilingual clinical domain. The findings can be used as a reference in clinical and language-based applications.

Abstract This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moderate traumatic brain injury (TBI). It aims to predict the risk of ICH progression using initial CT scans and identify clinical factors associated with this progression. A retrospective analysis of TBI patients between January 2010 and December 2021 was performed, focusing on initial CT evaluations and demographic, comorbid, and medical history data. ICH was categorized into intraparenchymal hemorrhage (IPH), petechial hemorrhage (PH), and subarachnoid hemorrhage (SAH). Within our study cohort, we identified a 22.2% progression rate of ICH among 650 TBI patients. The Random Forest algorithm identified variables such as petechial hemorrhage (PH) and countercoup injury as significant predictors of ICH progression. The XGBoost algorithm, incorporating key variables identified through SHAP values, demonstrated robust performance, achieving an AUC of 0.9. Additionally, an individual risk assessment diagram, utilizing significant SHAP values, visually represented the impact of each variable on the risk of ICH progression, providing personalized risk profiles. This approach, highlighted by an AUC of 0.913, underscores the model’s precision in predicting ICH progression, marking a significant step towards enhancing TBI patient management through early identification of ICH progression risks.

Abstract One of the artificial intelligence applications in the biomedical field is knowledge-intensive question-answering. As domain expertise is particularly crucial in this field, we propose a method for efficiently infusing biomedical knowledge into pretrained language models, ultimately targeting biomedical question-answering. Transferring all semantics of a large knowledge graph into the entire model requires too many parameters, increasing computational cost and time. We investigate an efficient approach that leverages adapters to inject Unified Medical Language System knowledge into pretrained language models, and we question the need to use all semantics in the knowledge graph. This study focuses on strategies of partitioning knowledge graph and either discarding or merging some for more efficient pretraining. According to the results of three biomedical question answering finetuning datasets, the adapters pretrained on semantically partitioned group showed more efficient performance in terms of evaluation metrics, required parameters, and time. The results also show that discarding groups with fewer concepts is a better direction for small datasets, and merging these groups is better for large dataset. Furthermore, the metric results show a slight improvement, demonstrating that the adapter methodology is rather insensitive to the group formulation.

Objectives In the era of the Fourth Industrial Revolution, where an ecosystem is being developed to enhance the quality of healthcare services by applying information and communication technologies, systematic and sustainable data management is essential for medical institutions. In this study, we assessed the data management status and emerging concerns of three medical institutions, while also examining future directions for seamless data management. Methods To evaluate the data management status, we examined data types, capacities, infrastructure, backup methods, and related organizations. We also discussed challenges, such as resource and infrastructure issues, problems related to government regulations, and considerations for future data management. Results Hospitals are grappling with the increasing data storage space and a shortage of management personnel due to costs and project termination, which necessitates countermeasures and support. Data management regulations on the destruction or maintenance of medical records are needed, and institutional consideration for secondary utilization such as long-term treatment or research is required. Government-level guidelines for facilitating hospital data sharing and mobile patient services should be developed. Additionally, hospital executives at the organizational level need to make efforts to facilitate the clinical validation of artificial intelligence software. Conclusions This analysis of the current status and emerging issues of data management reveals potential solutions and sets the stage for future organizational and policy directions. If medical big data is systematically managed, accumulated over time, and strategically monetized, it has the potential to create new value.

Abstract Although several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 30 days of discharge; the model is based on a common data model and considers weather and air quality factors, and can be easily extended to multiple hospitals. We developed and compared four tree-based machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine (GBM). Above all, GBM showed the highest AUC performance of 75.1 in the clinical model, while the clinical and W-score model showed the best performance of 73.9 for musculoskeletal diseases. Further, PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. In addition, external validation has confirmed that the model based on weather and air quality factors has transportability to adapt to other hospital systems.

Abstract Contradictory findings exist about association of angiotensin-converting enzyme inhibitor (ACEi) and angiotensin receptor blocker (ARB) with lung cancer development. This was a retrospective observational cohort study that used data from 7 hospitals in Korea, converted to the Observational Medical Outcomes Partnership Common Data Model. The primary outcome was occurrence of lung cancer. A total of 207,794 patients across the 7 databases was included in the final analysis; 33,230 (16%) were prescribed ACEi and 174,564 (84%) were prescribed ARB. Crude analysis adjusted for sex and age showed higher incidence of lung cancer in the ACEi group compared to the ARB group (hazard ratio [HR], 1.46; 95% confidence rate [CI], 1.08–1.97). After propensity-score matching, 30,445 pairs were generated, and there was no difference in incidence of lung cancer between the two groups (HR, 0.93; 95% CI, 0.64–1.35). Patients prescribed ACEi showed no difference in incidence of lung cancer development compared to those using ARB. This finding provides evidence on the association between ACEi and occurrence of lung cancer.

One major challenge associated with lung cancer organoids (LCOs) is their predominant derivation from surgical specimens of patients with early-stage lung cancer. However, patients with advanced lung cancer, who are in need of chemotherapy, often cannot undergo surgery. Therefore, there is an urgent need to successfully generate LCOs from biopsy specimens. Conventional lung biopsy techniques, such as transthoracic needle biopsy and forceps biopsy, only yield small amounts of lung tissue, resulting in a low success rate for culturing LCOs from biopsy samples. Furthermore, potential complications, like bleeding and pneumothorax, make it difficult to obtain sufficient tissue. Another critical issue is the overgrowth of normal lung cells in later passages of LCO culture, and the optimal culture conditions for LCOs are yet to be determined. To address these limitations, we attempted to create LCOs from cryobiopsy specimens obtained from patients with lung cancer (n = 113). Overall, the initial success rate of establishing LCOs from cryobiopsy samples was 40.7% (n = 46). Transbronchial cryobiopsy enables the retrieval of significantly larger amounts of lung tissue than bronchoscopic forceps biopsy. Additionally, cryobiopsy can be employed for peripheral lesions, and it is aided via radial endobronchial ultrasonography. This study significantly improved the success rate of LCO culture and demonstrated that the LCOs retained characteristics that resembled the primary tumors. Single-cell RNA sequencing confirmed high cancer cell purity in early passages of LCOs derived from patients with advanced lung cancer. Furthermore, the three-dimensional structure and intracellular components of LCOs were characterized using three-dimensional holotomography. Finally, drug screening was performed using a specialized micropillar culture system with cryobiopsy-derived LCOs. LCOs derived from cryobiopsy specimens offer a promising solution to the critical limitations of conventional LCOs. Cryobiopsy can be applied to patients with lung cancer at all stages, including those with peripheral lesions, and can provide sufficient cells for LCO generation. Therefore, we anticipate that cryobiopsy will serve as a breakthrough strategy for the clinical application of LCOs in all stages of lung cancer.

Abstract Background Artificial intelligence (AI) research is highly dependent on the nature of the data available. With the steady increase of AI applications in the medical field, the demand for quality medical data is increasing significantly. We here describe the development of a platform for providing and sharing digital pathology data to AI researchers, and highlight challenges to overcome in operating a sustainable platform in conjunction with pathologists. Methods Over 3000 pathological slides from five organs (liver, colon, prostate, pancreas and biliary tract, and kidney) in histologically confirmed tumor cases by pathology departments at three hospitals were selected for the dataset. After digitalizing the slides, tumor areas were annotated and overlaid onto the images by pathologists as the ground truth for AI training. To reduce the pathologists’ workload, AI-assisted annotation was established in collaboration with university AI teams. Results A web-based data sharing platform was developed to share massive pathological image data in 2019. This platform includes 3100 images, and 5 pre-processing algorithms for AI researchers to easily load images into their learning models. Discussion Due to different regulations among countries for privacy protection, when releasing internationally shared learning platforms, it is considered to be most prudent to obtain consent from patients during data acquisition. Conclusions Despite limitations encountered during platform development and model training, the present medical image sharing platform can steadily fulfill the high demand of AI developers for quality data. This study is expected to help other researchers intending to generate similar platforms that are more effective and accessible in the future.

The steadily increasing number of medical images places a tremendous burden on doctors, who toned to read and write reports. If an image captioning model could generate drafts of the reports from the corresponding images, the workload of doctors would be reduced, thereby saving time and expenses. The aim of this study was to develop a chest x-ray image captioning model that considers the differences between patient images and normal images, and uses hierarchical long short-term memory (LSTM) or a transformer as a decoder to generate reports. We investigated which feature representation method was the most appropriate for capturing the differences. The feature representations differed in terms of whether global average pooling was used for the visual feature vectors and how the feature difference vectors were generated. Experiments were conducted on two datasets using the proposed models and recent captioning models (X-LAN and X-Transformer). BLEU, METEOR, ROUGE-L, and CIDEr were used as evaluation metrics. The best model for most metric scores was the multi-difference non-average-pooling transformer model, which uses the transformer decoder, does not use global average pooling for the visual feature vectors, and applies the element-wise product. The transformer decoder was found to be more suitable than hierarchical LSTM. Furthermore, for models that do not condense features with global average pooling, the element-wise product was observed to be more effective than subtraction in expressing the feature differences.

BackgroundThe emergency department (ED) triage system to classify and prioritize patients from high risk to less urgent continues to be a challenge. ObjectiveThis study, comprising 80,433 patients, aims to develop a machine learning algorithm prediction model of critical care outcomes for adult patients using information collected during ED triage and compare the performance with that of the baseline model using the Korean Triage and Acuity Scale (KTAS). MethodsTo predict the need for critical care, we used 13 predictors from triage information: age, gender, mode of ED arrival, the time interval between onset and ED arrival, reason of ED visit, chief complaints, systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, body temperature, oxygen saturation, and level of consciousness. The baseline model with KTAS was developed using logistic regression, and the machine learning model with 13 variables was generated using extreme gradient boosting (XGB) and deep neural network (DNN) algorithms. The discrimination was measured by the area under the receiver operating characteristic (AUROC) curve. The ability of calibration with Hosmer–Lemeshow test and reclassification with net reclassification index were evaluated. The calibration plot and partial dependence plot were used in the analysis. ResultsThe AUROC of the model with the full set of variables (0.833-0.861) was better than that of the baseline model (0.796). The XGB model of AUROC 0.861 (95% CI 0.848-0.874) showed a higher discriminative performance than the DNN model of 0.833 (95% CI 0.819-0.848). The XGB and DNN models proved better reclassification than the baseline model with a positive net reclassification index. The XGB models were well-calibrated (Hosmer-Lemeshow test; P>.05); however, the DNN showed poor calibration power (Hosmer-Lemeshow test; P

Abstract Background This paper presents a conditional random fields (CRF) method that enables the capture of specific high-order label transition factors to improve clinical named entity recognition performance. Consecutive clinical entities in a sentence are usually separated from each other, and the textual descriptions in clinical narrative documents frequently indicate causal or posterior relationships that can be used to facilitate clinical named entity recognition. However, the CRF that is generally used for named entity recognition is a first-order model that constrains label transition dependency of adjoining labels under the Markov assumption. Methods Based on the first-order structure, our proposed model utilizes non-entity tokens between separated entities as an information transmission medium by applying a label induction method. The model is referred to as precursor-induced CRF because its non-entity state memorizes precursor entity information, and the model’s structure allows the precursor entity information to propagate forward through the label sequence. Results We compared the proposed model with both first- and second-order CRFs in terms of their F1-scores, using two clinical named entity recognition corpora (the i2b2 2012 challenge and the Seoul National University Hospital electronic health record). The proposed model demonstrated better entity recognition performance than both the first- and second-order CRFs and was also more efficient than the higher-order model. Conclusion The proposed precursor-induced CRF which uses non-entity labels as label transition information improves entity recognition F1 score by exploiting long-distance transition factors without exponentially increasing the computational time. In contrast, a conventional second-order CRF model that uses longer distance transition factors showed even worse results than the first-order model and required the longest computation time. Thus, the proposed model could offer a considerable performance improvement over current clinical named entity recognition methods based on the CRF models.

ObjectivesClinical discharge summaries provide valuable information about patients' clinical history, which is helpful for the realization of intelligent healthcare applications. The documents tend to take the form of separate segments based on temporal or topical information. If a patient's clinical history can be seen as a consecutive sequence of clinical events, then each temporal segment can be seen as a snapshot, providing a certain clinical context at a specific moment. This study aimed to demonstrate a temporal segmentation method of Korean clinical narratives for identifying textual snapshots of patient history as a proof-of-a-concept.MethodsOur method uses pattern-based segmentation to approximate human recognition of the temporal or topical shifts in clinical documents. We utilized rheumatic patients' discharge summaries and transformed them into sequences of constituent chunks. We built 97 single pattern functions to denote whether a certain chunk has attributes that indicate that it can be a segment boundary. We manually defined the relationships between the pattern functions to resolve multiple pattern matchings and to make a final decision.ResultsThe algorithm segmented 30 discharge summaries and processed 1,849 decision points. Three human judges were asked whether they agreed with the algorithm's prediction, and the agreement percentage on the judges' majority opinion was 89.61%.ConclusionsAlthough this method is based on manually constructed rules, our findings demonstrate that the proposed algorithm can achieve fairly good segmentation results, and it may be the basis for methodological improvement in the future.

Abstract Background Pathology reports are written in free-text form, which precludes efficient data gathering. We aimed to overcome this limitation and design an automated system for extracting biomarker profiles from accumulated pathology reports. Methods We designed a new data model for representing biomarker knowledge. The automated system parses immunohistochemistry reports based on a “slide paragraph” unit defined as a set of immunohistochemistry findings obtained for the same tissue slide. Pathology reports are parsed using context-free grammar for immunohistochemistry, and using a tree-like structure for surgical pathology. The performance of the approach was validated on manually annotated pathology reports of 100 randomly selected patients managed at Seoul National University Hospital. Results High F-scores were obtained for parsing biomarker name and corresponding test results (0.999 and 0.998, respectively) from the immunohistochemistry reports, compared to relatively poor performance for parsing surgical pathology findings. However, applying the proposed approach to our single-center dataset revealed information on 221 unique biomarkers, which represents a richer result than biomarker profiles obtained based on the published literature. Owing to the data representation model, the proposed approach can associate biomarker profiles extracted from an immunohistochemistry report with corresponding pathology findings listed in one or more surgical pathology reports. Term variations are resolved by normalization to corresponding preferred terms determined by expanded dictionary look-up and text similarity-based search. Conclusions Our proposed approach for biomarker data extraction addresses key limitations regarding data representation and can handle reports prepared in the clinical setting, which often contain incomplete sentences, typographical errors, and inconsistent formatting.

There are two kinds of hostilities on the Korean Peninsula: hostility between the U.S. and North Korea; hostility between the two Koreas. The nature of North Korea’s nuclear crisis is a mixture of those two hostilities. The crisis was exacerbated by misinterpretation and wishful thinking regarding its intentions. Another reason for North Korea’s nuclear crisis is the failure of the international community to speak with one voice on how to resolve it. Every country is different in its threat perceptions, national interests, and strategic calculations. In the grand scheme of things, however, the North Korea problem seems to be a strategic conflict between the U.S. and China. South Korea’s internal friction prevented any policy from being implemented effectively. It is not only unfair but unrealistic to handle the two hostilities separately. Any efforts to denuclearize North Korea should not undermine the security of South Korea. For example, the withdrawal of the U.S. forces from the Korean peninsula may be even worse for peace and stability on the Korean Peninsula than a nuclear North Korea, if it keeps the current political system and there is no fundamental change in inter-Korean relations. Like the front and rear wheels in an automobile, the U.S.–North Korean dialogue and inter-Korean dialogue began to operate as two driving forces for a breakthrough in the nuclear crisis. The wheels should be aligned with a strong U.S.–R.O.K. alliance. Then a multilateral format like the Six-Party Talks can resume for a sustainable peace and stability on the Korean Peninsula.

Background and objectives: The aim of this study is to describe the temporal change in alert override with a minimally interruptive clinical decision support (CDS) on a Next-Generation electronic medical record (EMR) and analyze factors associated with the change. Materials and Methods: The minimally interruptive CDS used in this study was implemented in the hospital in 2016, which was a part of the new next-generation EMR, Data Analytics and Research Window for Integrated kNowledge (DARWIN), which does not generate modals, ‘pop-ups’ but show messages as in-line information. The prescription (medication order) and alerts data from July 2016 to December 2017 were extracted. Piece-wise regression analysis and linear regression analysis was performed to determine the temporal change and factors associated with it. Results: Overall, 2,706,395 alerts and 993 doctors were included in the study. Among doctors, 37.2% were faculty (professors), 17.2% were fellows, and 45.6% trainees (interns and residents). The overall override rate was 61.9%. There was a significant change in an increasing trend at month 12 (p < 0.001). We found doctors’ positions and specialties, along with the number of alerts and medication variability, were significantly associated with the change. Conclusions: In this study, we found a significant temporal change of alert override. We also found factors associated with the change, which had statistical significance.

ObjectivesMachine learning systems can considerably reduce the time and effort needed by experts to perform new systematic reviews (SRs). This study investigates categorization models, which are trained on a combination of included and commonly excluded articles, which can improve performance by identifying high quality articles for new procedures or drug SRs.MethodsTest collections were built using the annotated reference files from 19 procedure and 15 drug systematic reviews. The classification models, using a support vector machine, were trained by the combined even data of other topics, excepting the desired topic. This approach was compared to the combination of included and commonly excluded articles with the combination of included and excluded articles. Accuracy was used for the measure of comparison.ResultsOn average, the performance was improved by about 15% in the procedure topics and 11% in the drug topics when the classification models trained on the combination of articles included and commonly excluded, were used. The system using the combination of included and commonly excluded articles performed better than the combination of included and excluded articles in all of the procedure topics.ConclusionsAutomatically rigorous article classification using machine learning can reduce the workload of experts when they perform systematic reviews when the topic-specific data are scarce. In particular, when the combination of included and commonly excluded articles is used, this system will be more effective.

ObjectivesThe purpose of this study is to validate a method that uses multiple queries to create a set of relevance judgments used to indicate which documents are pertinent to each query when forming a biomedical test collection.MethodsThe aspect query is the major concept of this research; it can represent every aspect of the original query with the same informational need. Manually generated aspect queries created by 15 recruited participants where run using the BM25 retrieval model in order to create aspect query based relevance sets (QRELS). In order to demonstrate the feasibility of these QRELSs, The results from a 2004 genomics track run supported by the National Institute of Standards and Technology (NIST) were used to compute the mean average precision (MAP) based on Text Retrieval Conference (TREC) QRELSs and aspect-QRELSs. The rank correlation was calculated using both Kendall's and Spearman's rank correlation methods.ResultsWe experimentally verified the utility of the aspect query method by combining the top ranked documents retrieved by a number of multiple queries which ranked the order of the information. The retrieval system correlated highly with rankings based on human relevance judgments.ConclusionsSubstantial results were shown with high correlations of up to 0.863 (p < 0.01) between the judgment-free gold standard based on the aspect queries and the human-judged gold standard supported by NIST. The results also demonstrate that the aspect query method can contribute in building test collections used for medical literature retrieval.

ObjectivesAcquiring temporal information is important because knowledge in clinical narratives is time-sensitive. In this paper, we describe an approach that can be used to extract the temporal information found in Korean clinical narrative texts.MethodsWe developed a two-stage system, which employs an exhaustive text analysis phase and a temporal expression recognition phase. Since our target document may include tokens that are made up of both Korean and English text joined together, the minimal semantic units are analyzed and then separated from the concatenated phrases and linguistic derivations within a token using a corpus-based approach to decompose complex tokens. A finite state machine is then used on the minimal semantic units in order to find phrases that possess time-related information.ResultsIn the experiment, the temporal expressions within Korean clinical narratives were extracted using our system. The system performance was evaluated through the use of 100 discharge summaries from Seoul National University Hospital containing a total of 805 temporal expressions. Our system scored a phrase-level precision and recall of 0.895 and 0.919, respectively.ConclusionsFinding information in Korean clinical narrative is challenging task, since the text is written in both Korean and English and frequently omits syntactic elements and word spacing, which makes it extremely noisy. This study presents an effective method that can be used to aquire the temporal information found in Korean clinical documents.

ObjectivesThe purpose of this study was to investigate the effects of query expansion algorithms for MEDLINE retrieval within a pseudo-relevance feedback framework.MethodsA number of query expansion algorithms were tested using various term ranking formulas, focusing on query expansion based on pseudo-relevance feedback. The OHSUMED test collection, which is a subset of the MEDLINE database, was used as a test corpus. Various ranking algorithms were tested in combination with different term re-weighting algorithms.ResultsOur comprehensive evaluation showed that the local context analysis ranking algorithm, when used in combination with one of the reweighting algorithms - Rocchio, the probabilistic model, and our variants - significantly outperformed other algorithm combinations by up to 12% (paired t-test; p < 0.05). In a pseudo-relevance feedback framework, effective query expansion would be achieved by the careful consideration of term ranking and re-weighting algorithm pairs, at least in the context of the OHSUMED corpus.ConclusionsComparative experiments on term ranking algorithms were performed in the context of a subset of MEDLINE documents. With medical documents, local context analysis, which uses co-occurrence with all query terms, significantly outperformed various term ranking methods based on both frequency and distribution analyses. Furthermore, the results of the experiments demonstrated that the term rank-based re-weighting method contributed to a remarkable improvement in mean average precision.

ObjectivesMeasurement of similarities between documents is typically influenced by the sparseness of the term-document matrix employed. Latent semantic indexing (LSI) may improve the results of this type of analysis.MethodsIn this study, LSI was utilized in an attempt to reduce the term vector space of clinical documents and newspaper editorials.ResultsAfter applying LSI, document similarities were revealed more clearly in clinical documents than editorials. Clinical documents which can be characterized with co-occurring medical terms, various expressions for the same concepts, abbreviations, and typographical errors showed increased improvement with regards to a correlation between co-occurring terms and document similarities.ConclusionsOur results showed that LSI can be used effectively to measure similarities in clinical documents. In addition, correlation between the co-occurrence of terms and similarities realized in this study is an important positive feature associated with LSI.

ObjectivesAdoption of hospital information systems offers distinctive advantages in healthcare delivery. First, implementation of consolidated hospital information system in Seoul National University Hospital led to significant improvements in quality of healthcare and efficiency of hospital management.MethodsThe hospital information system in Seoul National University Hospital consists of component applications: clinical information systems, clinical research support systems, administrative information systems, management information systems, education support systems, and referral systems that operate to generate utmost performance when delivering healthcare services.ResultsClinical information systems, which consist of such applications as electronic medical records, picture archiving and communication systems, primarily support clinical activities. Clinical research support system provides valuable resources supporting various aspects of clinical activities, ranging from management of clinical laboratory tests to establishing care-giving procedures.ConclusionsSeoul National University Hospital strives to move its hospital information system to a whole new level, which enables customized healthcare service and fulfills individual requirements. The current information strategy is being formulated as an initial step of development, promoting the establishment of next-generation hospital information system.

The purpose of this study was to compare fracture resistances of K3 nickel-titanium files made from different thermal treatments. K3 (SybronEndo, Orange, CA), K3XF (SybronEndo), and experimentally heat treated K3 (K3H) were used. For the cyclic fatigue test, the samples were rotated with up-and-down motion in the artificial canal with the curvature of 60 degrees until the fracture occurred. The number of cycles to fracture (NCF) was measured. For the torsional fracture test, the samples were tightly bound and rotated until the fracture occurred. Elastic modulus (EM), ultimate torsional strength (UTS), and angle of rotation to fracture (ARF) were measured. The results were statistically analyzed by one-way ANOVA. The NCF of K3H was higher than those of K3 and K3XF (P

Inflammation plays a crucial role in tissue injury, repair and disease, orchestrating a complex interplay of immune responses and cellular processes. Recent studies have uncovered the intricate connection between inflammation and stem cell dynamics, shedding light on the central role of stem cells in tissue regeneration. This Review highlights the significance of inflammation in shaping epithelial stem cell dynamics and its implications for tissue repair, regeneration and aging. We explore the multifaceted interactions between inflammation and stem cells, focusing on how inflammatory signals affect stem cell behavior and fate as well as the remodeling of their niche in the respiratory tract. We also discuss the concept of 'inflammatory memory' in epithelial stem cells, where prior inflammatory stimuli endow these cells with enhanced regenerative potential and confer long-lasting protective mechanisms for maintaining tissue integrity and function. Furthermore, we review the impact of cell senescence induced by inflammation on tissue regeneration and aging, delving into the molecular mechanisms underlying the modulation of signaling pathways, epigenetic modifications and cellular crosstalk. Understanding these dynamic processes not only deepens our knowledge of tissue homeostasis and repair but also holds profound implications for regenerative medicine strategies aimed at preventing pulmonary diseases. [ABSTRACT FROM AUTHOR]

Background Infections following postmastectomy implant-based breast reconstruction (IBR) can compromise surgical outcomes and lead to significant morbidity. This study aimed to discern the timing of infections in two-stage IBR and associated risk factors. Method A review of electronic health records was conducted on 1096 breasts in 1058 patients undergoing two-stage IBR at Seoul National University Hospital (2015-2020). Infections following the first-stage tissue expander (TE) insertion and second-stage TE exchange were analyzed separately, considering associated risk factors. Results Over a median follow-up of 53.5 months, infections occurred in 2.9% (32/1096) after the first stage and 4.1% (44/1070) after the second stage. Infections following the first-stage procedure exhibited a bimodal distribution across time, while those after the second-stage procedure showed a unimodal pattern. When analyzing risk factors for infection after the first-stage procedure, axillary lymph node dissection (ALND) was associated with early (= 7 weeks) infection, while both ALND and obesity were independent predictors of late (>7 weeks) infection. For infections following the second-stage procedure, obesity, postmastectomy radiotherapy, a history of expander infection, ALND, and the use of textured implants were identified as independent risk factors. Postmastectomy radiotherapy was related to non-salvaged outcomes after infection following both stages. Conclusion Infections following first and second-stage IBR exhibit distinct timelines reflecting different pathophysiology. Understanding these timelines and associated risk factors will inform patient selection for IBR and aid in tailored postoperative surveillance planning. These findings contribute to refining patient suitability for IBR and optimizing personalized postoperative care strategies. [ABSTRACT FROM AUTHOR]