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Study objective We aimed to train and validate the time to on-scene return of spontaneous circulation prediction models using time-to-event analysis among out-of-hospital cardiac arrest patients. Methods Using a Korean population-based out-of-hospital cardiac arrest registry, we selected a total of 105,215 adults with presumed cardiac etiologies between 2013 and 2018. Patients from 2013 to 2017 and from 2018 were analyzed for training and test, respectively. We developed 4 time-to-event analyzing models (Cox proportional hazard [Cox], random survival forest, extreme gradient boosting survival, and DeepHit) and 4 classification models (logistic regression, random forest, extreme gradient boosting, and feedforward neural network). Patient characteristics and Utstein elements collected at the scene were used as predictors. Discrimination and calibration were evaluated by Harrell's C-index and integrated Brier score. Results Among the 105,215 patients (mean age 70 years and 64% men), 86,314 and 18,901 patients belonged to the training and test sets, respectively. On-scene return of spontaneous circulation was achieved in 5,240 (6.1%) patients in the former set and 1,709 (9.0%) patients in the latter. The proportion of emergency medical services (EMS) management was higher and scene time interval longer in the latter. Median time from EMS scene arrival to on-scene return of spontaneous circulation was 8 minutes for both datasets. Classification models showed similar discrimination and poor calibration power compared to survival models; Cox showed high discrimination with the best calibration (C-index [95% confidence interval]: 0.873 [0.865 to 0.882]; integrated Brier score at 30 minutes: 0.060). Conclusion Incorporating time-to-event analysis could lead to improved performance in prediction models and contribute to personalized field EMS resuscitation decisions.

Recent large-scale genome-wide association studies have identified common genetic variations that may contribute to the risk of amyotrophic lateral sclerosis (ALS). However, pinpointing the risk variants in noncoding regions and underlying biological mechanisms remains a major challenge. Here, we constructed a convolutional neural network model with a large-scale GWAS meta-analysis dataset to unravel functional noncoding variants associated with ALS based on their epigenetic features. After filtering and prioritizing of candidates, we fine-mapped two new risk variants, rs2370964 and rs3093720, on chromosome 3 and 17, respectively. Further analysis revealed that these polymorphisms are associated with the expression level of CX3CR1 and TNFAIP1, and affect the transcription factor binding sites for CTCF, NFATc1 and NR3C1. Our results may provide new insights for ALS pathogenesis, and the proposed research methodology can be applied for other complex diseases as well.

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, &lsquo, pop-ups&rsquo, 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 &lt, 0.001). We found doctors&rsquo, 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.

SummaryTissue regeneration involves a multi-step process composed of diverse cellular hierarchies and states that are also implicated in tissue dysfunction and pathogenesis. Here, we leveraged single-cell RNA sequencing analysis in combination within vivolineage tracing and organoid models to fine-map trajectories of alveolar lineage cells during injury repair and regeneration. We identified Damage-Associated Transient Progenitors (DATPs) as a distinct AT2-lineaged population arising during alveolar regeneration. Specifically, we found that IL-1β, secreted by interstitial macrophages, primes a subset ofIl1r1+AT2 cells for conversion into DATPs, via aHif1a-mediated glycolysis pathway, that are functional mediators for mature AT1 cell differentiation. Importantly, we show that chronic inflammation mediated by IL-1β prevents differentiation into AT1 cells, leading to aberrant accumulation of DATPs and impaired alveolar differentiation. Our step-wise fine-mapping of cell fate transitions demonstrates how the inflammatory niche impedes alveolar regeneration by directing stem cell fate behavior.

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 The emergency department (ED) triage system to classify and prioritize patients from high risk to less urgent continues to be a challenge. Objective This 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). Methods To 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. Results The 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 Conclusions Our study demonstrated that the performance of the XGB model using initial information at ED triage for predicting patients in need of critical care outperformed the conventional model with KTAS.

The application of appropriate rules for drug–drug interactions (DDIs) could substantially reduce the number of adverse drug events. However, current implementations of such rules in tertiary hospitals are problematic as physicians are receiving too many alerts, causing high override rates and alert fatigue. We investigated the potential impact of Korean national DDI rules in a drug utilization review program in terms of their severity coverage and the clinical efficiency of how physicians respond to them. Using lists of high-priority DDIs developed with the support of the U.S. government, we evaluated 706 contraindicated DDI pairs released in May 2015. We evaluated clinical log data from one tertiary hospital and prescription data from two other tertiary hospitals. The measured parameters were national DDI rule coverage for high-priority DDIs, alert override rate, and number of prescription pairs. The coverage rates of national DDI rules were 80% and 3.0% at the class and drug levels, respectively. The analysis of the system log data showed an overall override rate of 79.6%. Only 0.3% of all of the alerts (n = 66) were high-priority DDI rules. These showed a lower override rate of 51.5%, which was much lower than for the overall DDI rules. We also found 342 and 80 unmatched high-priority DDI pairs which were absent in national rules in inpatient orders from the other two hospitals. The national DDI rules are not complete in terms of their coverage of severe DDIs. They also lack clinical efficiency in tertiary settings, suggesting improved systematic approaches are needed., Graphical 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. Electronic supplementary material The online version of this article (10.1186/s12911-018-0609-7) contains supplementary material, which is available to authorized users.

In Korea, there were 224,000 new cases of cancer and 75,334 deaths caused by cancer in 2013, which was three times more than the number of death caused by heart disease, the second leading cause of death. This study proposes a biomarker positivity analysis system based on clinical data, for personalized diagnosis and therapy of cancer. Data of 78,912 cases were obtained from immunopathology and surgical pathology reports. Data on sex, age, organ, diagnosis, and biomarkers were entered into a database. To verify the reliability of the clinical data, an additional 50,450 cases from positivity-related research papers were added. The proposed biomarker positivity analysis system makes it possible to extract and combine information for searching. The positivity values are in graphical and tabular format for ease of use. With a link to the internal network of the hospital, real-time pathology reports are available. Twenty-five pathology specialists are chosen as subjects to further confirm the reliability of this system; primary assessment results demonstrate a satisfaction level of 4.7 out of 5 and a concordance rate of 79% with positive data under the same conditions as reported in the literature. In the present study, analysis methods and platforms using large volumes of clinical and literature data are developed for cancer prognoses. It is expected that these tools will benefit both healthcare professionals and non-professionals involved in cancer diagnosis and treatment.

Background: Despite the advances in the diagnosis and treatment of heart failure (HF), the current hospital-oriented framework for HF management does not appear to be sufficient to maintain the stability of HF patients in the long term. The importance of self-care management is increasingly being emphasized as a promising long-term treatment strategy for patients with chronic HF. Objective: The objective of this study was to evaluate whether a new information communication technology (ICT)–based telehealth program with voice recognition technology could improve clinical or laboratory outcomes in HF patients. Methods: In this prospective single-arm pilot study, we recruited 31 consecutive patients with chronic HF who were referred to our institute. An ICT-based telehealth program with voice recognition technology was developed and used by patients with HF for 12 weeks. Patients were educated on the use of this program via mobile phone, landline, or the Internet for the purpose of improving communication and data collection. Using these systems, we collected comprehensive data elements related to the risk of HF self-care management such as weight, diet, exercise, medication adherence, overall symptom change, and home blood pressure. The study endpoints were the changes observed in urine sodium concentration (uNa), Minnesota Living with Heart Failure (MLHFQ) scores, 6-min walk test, and N-terminal prohormone of brain natriuretic peptide (NT-proBNP) as surrogate markers for appropriate HF management. Results: Among the 31 enrolled patients, 27 (87%) patients completed the study, and 10 (10/27, 37%) showed good adherence to ICT-based telehealth program with voice recognition technology, which was defined as the use of the program for 100 times or more during the study period. Nearly three-fourths of the patients had been hospitalized at least once because of HF before the enrollment (20/27, 74%); 14 patients had 1, 2 patients had 2, and 4 patients had 3 or more previous HF hospitalizations. In the total study population, there was no significant interval change in laboratory and functional outcome variables after 12 weeks of ICT-based telehealth program. In patients with good adherence to ICT-based telehealth program, there was a significant improvement in the mean uNa (103.1 to 78.1; P=.01) but not in those without (85.4 to 96.9; P=.49). Similarly, a marginal improvement in MLHFQ scores was only observed in patients with good adherence (27.5 to 21.4; P=.08) but not in their counterparts (19.0 to 19.7; P=.73). The mean 6-min walk distance and NT-proBNP were not significantly increased in patients regardless of their adherence. Conclusions: Short-term application of ICT-based telehealth program with voice recognition technology showed the potential to improve uNa values and MLHFQ scores in HF patients, suggesting that better control of sodium intake and greater quality of life can be achieved by this program. [JMIR Mhealth Uhealth 2017;5(10):e127]

Dental panoramic radiographs (DPRs) provide information required to potentially evaluate bone density changes through a textural and morphological feature analysis on a mandible. This study aims to evaluate the discriminating performance of deep convolutional neural networks (CNNs), employed with various transfer learning strategies, on the classification of specific features of osteoporosis in DPRs. For objective labeling, we collected a dataset containing 680 images from different patients who underwent both skeletal bone mineral density and digital panoramic radiographic examinations at the Korea University Ansan Hospital between 2009 and 2018. Four study groups were used to evaluate the impact of various transfer learning strategies on deep CNN models as follows: a basic CNN model with three convolutional layers (CNN3), visual geometry group deep CNN model (VGG-16), transfer learning model from VGG-16 (VGG-16_TF), and fine-tuning with the transfer learning model (VGG-16_TF_FT). The best performing model achieved an overall area under the receiver operating characteristic of 0.858. In this study, transfer learning and fine-tuning improved the performance of a deep CNN for screening osteoporosis in DPR images. In addition, using the gradient-weighted class activation mapping technique, a visual interpretation of the best performing deep CNN model indicated that the model relied on image features in the lower left and right border of the mandibular. This result suggests that deep learning-based assessment of DPR images could be useful and reliable in the automated screening of osteoporosis patients.

During the process of B cell development, transcription factors, such as E2A and Ebf1, have been known to play key roles. Although transcription factors and chromatin regulators work in concert to direct the expression of B lineage-specific genes, little is known about the involvement of regulators for chromatin structure during B lymphopoiesis. In this article, we show that deletion of Srg3/mBaf155, a scaffold subunit of the SWI/SNF-like BAF complex, in the hematopoietic lineage caused defects at both the common lymphoid progenitor stage and the transition from pre–pro-B to early pro-B cells due to failures in the expression of B lineage-specific genes, such as Ebf1 and Il7ra, and their downstream target genes. Moreover, mice that were deficient in the expression of Brg1, a subunit of the complex with ATPase activity, also showed defects in early B cell development. We also found that the expression of Ebf1 and Il7ra is directly regulated by the SWI/SNF-like BAF complex. Thus, our results suggest that the SWI/SNF-like BAF complex facilitates early B cell development by regulating the expression of B lineage-specific genes.

OBJECTIVES Measurement 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. METHODS In this study, LSI was utilized in an attempt to reduce the term vector space of clinical documents and newspaper editorials. RESULTS After 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. CONCLUSIONS Our 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.

Objectives Adoption 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. Methods The 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. Results Clinical 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. Conclusions Seoul 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.

MicroRNAs (miRNAs) have emerged as important regulators of the immune system. However, despite this prominence, our understanding of the function of miRNAs in the early hematopoietic stages is incomplete. In this study, we found that miR-139-5p negatively regulated the proliferation of hematopoietic stem cells and progenitor cells and that downregulation of miR-139-5p expression was associated with hematopoietic malignancy, such as chronic myeloid leukemia (CML). Knockdown of miR-139-5p resulted in myeloid-biased differentiation with expansion of myeloid progenitor cells. In contrast, miR-139-5p expression inhibited the proliferation of hematopoietic progenitors and resulted in the remission of a CML-like disease that is induced by breakpoint cluster region-Abelson (BCR-ABL) transformation. We also found that Brg1 is a functional target of miR-139-5p and that Brg1 is involved in BCR-ABL-induced leukemogenesis. Thus, our results identify miR-139-5p as a key regulator of cellular proliferation during early hematopoiesis and suggest that it is a potent antileukemic molecule.

One notable phenotypic change during the differentiation of immature thymocytes into either mature CD4 or CD8 single-positive lineages is the acquisition of a resistance to glucocorticoid (GC)-induced apoptosis. We have previously reported that SRG3 is critical in determining the sensitivity for the GC-induced apoptosis in developing thymocytes. We report here that Notch signaling downregulates the transcriptional activation of SRG3 through N-box and/or E-box elements on its promoter. RBP-J represses SRG3 transcription through the N-box motif. On the other hand, Deltex1 competitively inhibits the binding of p300 to E2A/HEB protein bound to the E-box elements and represses the SRG3 promoter activity. Moreover, enforced expression of Deltex1 restored double-positive (DP) thymocyte survival from the GC-induced apoptosis. Our results suggest that Notch signaling confers differentiating DP thymocytes resistance to GCs by regulating the SRG3 expression through Deltex1, and that Deltex1 and SRG3 may play a significant role during DP thymocyte maturation.

To North Korea's big disappointment, President Bush was re-elected and started his second term on January 20, 2005. On February 10, a few weeks after President Bush's second inauguration, North Korea said it would not attend 6-Party talks aimed at ending its nuclear weapons program unless the United States dropped its “hostile” policy toward the North. Pyongyang also confirmed that it has manufactured an undisclosed number of nuclear weapons to counter this U.S. policy. Considering the conflicting interests between the United States and North Korea, it will be difficult to anticipate how the North Korean nuclear crisis will be resolved. However, the relations between the United States and North Korea seem to remain in a state of tense stagnation rather than falling into a catastrophic phase. North Korea does not expect any concessions from the United States in the 6-Party talks, which only serve as a means of putting pressure on North Korea. North Korea seems convinced that a military option is not likely. North Korea may want to close the door on the United States and concentrate on the stability of its internal affairs. In the meantime, it wants to strengthen its position by extracting more plutonium. The stagnation may continue until the U.S. has enough power to keep the North from continuing to develop its nuclear program, or North Korea has enough leverage to force the United States to make substantial concession to North Korea.

Healthcare information travels with patients and clinicians and therefore the need for information to be ubiquitously available is key to reliable patient care and reliable medical systems. We have implemented MobileNurse, a prototype point-of-care system using PDA. MobileNurse has four modules each of which performs: (1) patient information management; (2) medical order check; (3) nursing recording; and (4) nursing care plan. MobileNurse provides easy input interface and various outputs for nursing records. The system consists of PDAs and a mobile support system (MSS) which supports clinical data exchange between PDAs and hospital information system. Two synchronization modules have been developed to keep the patient data consistent between PDAs and MSS. Clinical trials were performed with six volunteered nurses. They tried MobileNurse for 1-day caring-simulated patients. According to the survey after the trials, most of volunteers agreed that MobileNurse is more helpful and convenient than other non-mobile care systems to check medical orders and retrieve the results of recent clinical tests at the bedside. Through the involvement, we found out that ease-to-use interface is the most critical successful factor for mobile patient care systems.

Managing hospitals in good financial status is not an easy matter, especially in South Korea, where strict regulations limit the increase of medical cost. In South Korea, medical treatment is regarded as an essential public benefit that should be provided fairly to all citizens. For this reason, the Korean government has strictly controlled medical costs. The annual inflation rate of medical costs is around 1.3%; this is usually achieved through heated discussions between the government and the medical sector. Thus, the low profit medical environment has driven the hospital atmosphere into competition for increasing numbers of patient visits. This is a race not for best practice but for survival in a market with tight profit margins. The health policy has shown positive effects in terms of nationwide spending on health, especially in comparison to the high costs in many other countries. However, the squeezing health policy in South Korea has driven health organizations to pursue a path that is not promising for the best practice.

Background Computerized physician order entry (CPOE) with clinical decision support is expected to deliver many benefits in terms of patient safety. The Leapfrog tool was developed to allow hospitals to assess their medication-safety related decision support. To explore the approach's generalizability, we examined its acceptability and feasibility through an evaluation using this tool in four Korean hospital systems. Methods Four hospitals with locally developed CPOE systems participated, and a cross-sectional study design was used with the approval of the Leapfrog Group and the institutional review board at each hospital site. The hospitals were tertiary and academic institutions with long experience of advanced CPOE. From January 21 to 28, 2014, web-based tests were conducted at each site following the given instructions, and the results were self-reported. We measured each system's response rate, category completion rate, and time to complete the evaluation. Additionally, we compared the evaluation results of the four systems with scores from five US systems, as was reported in another study. Results The four systems underwent the tests, and the overall category completion rates ranged from 67.9% to 75.5%. The times to finish the tests were tolerable and within the allowed test timeframe. One system did not pass the deception analysis, which checks for false positives, due to a conflict with another type of alert checking for the presence of a medical diagnosis for documentation purposes. The other three systems scored at the completed the evaluation stage, with scores ranging from 21.6% to 36.5%. Of the nine error categories, Drug-Allergy was an area of strength for all systems, whereas the categories of Therapeutic duplication, Drug-Labs, and Drug-Age were areas of weakness for all. In comparison with the US systems, gaps were identified, and further improvement is needed. Conclusions The acceptability of the CPOE evaluation tool was moderate, but the feasibility was sufficient to operate the test outside the US, and the results revealed many opportunities for improvement in the Korean systems, as was the case when this application was introduced in the US.

Although renal hyperfiltration (RHF) or an abnormal increase in GFR has been associated with many lifestyles and clinical conditions, including diabetes, its clinical consequence is not clear. RHF is frequently considered to be the result of overestimating true GFR in subjects with muscle wasting. To evaluate the association between RHF and mortality, 43,503 adult Koreans who underwent voluntary health screening at Seoul National University Hospital between March of 1995 and May of 2006 with baseline GFR≥60 ml/min per 1.73 m(2) were followed up for mortality until December 31, 2012. GFR was estimated with the Chronic Kidney Disease Epidemiology Collaboration creatinine equation, and RHF was defined as GFR95th percentile after adjustment for age, sex, muscle mass, and history of diabetes and/or hypertension medication. Muscle mass was measured with bioimpedance analysis at baseline. During the median follow-up of 12.4 years, 1743 deaths occurred. The odds ratio of RHF in participants with the highest quartile of muscle mass was 1.31 (95% confidence interval [95% CI], 1.11 to 1.54) compared with the lowest quartile after adjusting for confounding factors, including body mass index. The hazard ratio of all-cause mortality for RHF was 1.37 (95% CI, 1.11 to 1.70) by Cox proportional hazards model with adjustment for known risk factors, including smoking. These data suggest RHF may be associated with increased all-cause mortality in an apparently healthy population. The possibility of RHF as a novel marker of all-cause mortality should be confirmed.

Cancer staging provides a basis not only for determining proper treatments for a patient but also for making future national-wide health plans. Despite its benefits, it is very difficult to obtain staging data because it is not commonly performed for all cancer patients or simply not collected. Moreover, it requires medical experts to do the analysis, incurring expensive cost. In this paper, we propose a method for semantic rule based determination of a cancer stage, which is considering a semantic type (e.g. body part, organ, or organ component). Compared to previous work, our work is unique in that we utilize radiology reports instead of pathological report since they are more available. Moreover, we argue that a rule-based approach is more suitable for cancer staging than machine learning because the international staging protocols specify certain conditions for determining stages. Since semantic type of words should be considered to determine the cancer stage and construct rules, we construct rules using MetaMap, which provides a meta-thesaurus of UMLS (Unified Medical Language System) for medical text. Based on our semantic rules, TNM (Tumor Nodes Metastasis) stages are determined for 275 reports of liver cancer. From our experiments, whole performance are highly incremented than machine learning approach.

Plasma protein adsorption onto an artificial surface is strongly influenced by not only the surface characteristics of materials, but also by the fluid dynamics inside the blood pump, and it would influence subsequent platelet adhesion or activation, which plays a major role in the initiation of thrombus formation at the blood-material interface in vivo. In vitro flow visualization of an electrohydraulic LVAD was performed by a video camera (CCD, Hitachi) and an image processor (PC VISION PLUS) with an IBM PC. The electrohydraulic LVADs were implanted in mongrel dogs of approximately 20 kg. The authors sectioned the blood contacted ventricle after animal death according to the level of shear rate. Because analysis of adsorbed protein might be influenced by the size of the ventricle segment, the number of segments was limited to eight per ventricle. Platelet adhesion and its morphology were observed by scanning electron microscopy (SEM). Adsorbed plasma proteins (fibrinogen, albumin, and IgG) on each segment were quantified by enzyme linked immunosorbent assay (ELISA). The specimens were soaked in 2% (wt/vol) SDS/PBS for 2 days and the released protein concentration assessed. A well developed large vortex was observed at the center of the artificial ventricle. Polyurethane blood pumps displayed different degrees of protein adsorption and subsequent platelet adhesion on each segment.

This study presents overall of information extraction (IE) for SNUH (Seoul National University Hospital) radiology reports coexisted Korean and English using Concept Node (CN) definition which is a case frame as extraction rule. The following steps are performed: design conceptual model by terminology exploration, create a CN definition based on syntactic relationship between two major semantic entities analyzed with sentences by a domain expert and implement automatic IE system using CN definition. Main purposes is to investigate whether syntactic and semantic analysis technique using extraction rule (CN) is effective for typical Korean medical text in mixed two different languages.

An online database system for remote monitoring of artificial heart patient has been developed. It is important for the patient with artificial heart implant to be discharged from the hospital after proper stabilization period for better and fast recovery and quality of life. The patients with artificial heart or mechanical circulatory support device are currently discharged after 1-2 months of hospitalization period. But, reliable and practical continuous remote monitoring systems for this kind of patients with life support device are still under study. The authors have developed a database system for this purpose that consists of a portable monitoring terminal, a database for continuous recording of patient and device status, and a web-based data access system with which the clinicians can access the real-time registering of patient data and past history data as well. The system has been tested with data generation emulators installed on remote sites for simulation study and in 2 cases of actual animal experiment conducted at remote facilities. The system showed acceptable functionality and reliability. The results are expected to be a good base for practical use of the system. A machine intelligence based automatic data analysis module is to be included for better practicality.

This study aims to describe the basic features of Electronic Medical Records at the Seoul National University Hospital and Seoul National University Bundang Hospital and to discuss the process we developed and adopted the system. We also aim to suggest potential risks and success factors in our processes. Methods: Seoul National University Hospital, a tertiary teaching hospital with 100-year-old history, 1000 medical staffs, and 1700 in-patient beds has successfully adopted Electronic Medical Records system from October 2004 and runs very well for more than one year. Our system is fully integrated with Computerized Physician's Order Entry (CPOE) and Picture Archiving and Communication System (PACS). Results: We identified that the key step for the successful adoption of the full system was to overcome physicians' resistance to their use of Electronic Medical Records and to help their earlier accommodation to new practice environment. We then found that five important success factors were the clinical leadership, adoption strategy, young doctors' participation, outsourcing of the department of information technology and the accumulated domain knowledge. Our experience shows it is important to expose young medical staffs to the change before the main Electronic Medical Records system opens and "patient-centered" was the most important concept to make these reform processes successful. Conclusion: Development and adoption of Electronic Medical Records at large teaching hospital are not easy but are very important and powerful tool for patient-centered medical practice.

OBJECTIVE: The standard vocabularies need to cover a diverse and enriched field of medical content, thereby facilitating semantic information retrieval, clinical decision support and efficient care delivery. SNOMED-CT(Systematized Nomenclature of Human and Veterinary Medicine-Clinical Term) is a comprehensive and precise clinical reference terminology that provides unsurpassed clinical content and expressivity for clinical documentation and reporting. To investigate whether the SNOMED-CT can serve this function in Seoul National University Hospital(SNUH) environment, we evaluated the coverage of SNOMED-CT as compared with clinical terms in the discharge summary at SNUH. METHODS: We tested for discordance of clinical terms between SNUH discharge summary and those from SNOMED-CT. We extracted 9,554 concepts from 1,000 discharge summaries. From these concepts, we obtained 3,545 unique concepts which are normalized to map with SNOMED-CT. These normalized terms are mapped to concepts of SNOMED-CT with semi-automatic method. RESULTS: We found a degree of concordance between SNOMED-CT and the clinical terms used in the discharge summary. Approximately, 89% of medical terms in the discharge summary are matched and 11% of the concepts are not mapped to those of SNOMED-CT. CONCLUSION: Through this study, we confirmed that SNOMED-CT is appropriate reference terminology in SNUH environment.

Summary Tissue regeneration is a multi-step process mediated by diverse cellular hierarchies and states that are also implicated in tissue dysfunction and pathogenesis. Here we leveraged single-cell RNA sequencing in combination with in vivo lineage tracing and organoid models to finely map the trajectories of alveolar-lineage cells during injury repair and lung regeneration. We identified a distinct AT2-lineage population, damage-associated transient progenitors (DATPs), that arises during alveolar regeneration. We found that interstitial macrophage-derived IL-1β primes a subset of AT2 cells expressing Il1r1 for conversion into DATPs via a HIF1α-mediated glycolysis pathway, which is required for mature AT1 cell differentiation. Importantly, chronic inflammation mediated by IL-1β prevents AT1 differentiation, leading to aberrant accumulation of DATPs and impaired alveolar regeneration. Together, this stepwise mapping to cell fate transitions shows how an inflammatory niche controls alveolar regeneration by controlling stem cell fate and behavior., Graphical Abstract, Highlights • Injury-induced IL-1β signaling promotes differentiation of AT2 cells into AT1 cells • AT2 cells differentiate into AT1 cells via cell states of primed AT2 and DATPs • HIF1α signaling controls AT2-DAPT conversion and is essential for AT1 differentiation • Chronic inflammation impairs maturation of AT1 cells, Choi et al. show that inflammatory signals play a crucial role during alveolar regeneration after injury. They define stepwise differentiation trajectories for AT2 cells into AT1 cells via damage-associated transient progenitors (DATPs) and find that unresolved inflammation stalls transition of DATPs into mature AT1 cells.