Sallingvillarreal0044

The pseudonymized PIDs (P-PIDs) along with other protected health information is further de-identified using POSDA.

A sample of 250 PIDs pseudonymized by O-CAPP were selected and successfully validated. Of those, 125 PIDs that were pseudonymized by the nightly automated process were validated by multiple clinical trial investigators (CTIs). For the other 125, CTIs validated radiologic image pseudonymization by API request based on the provided PID and P-PID mappings.

We developed a novel approach of an ondemand pseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participant data without compromising patient privacy.

We developed a novel approach of an ondemand pseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participant data without compromising patient privacy.

We incorporated the Korean Electronic Data Interchange (EDI) vocabulary into Observational Medical Outcomes Partnership (OMOP) vocabulary using a semi-automated process. The goal of this study was to improve the Korean EDI as a standard medical ontology in Korea.

We incorporated the EDI vocabulary into OMOP vocabulary through four main steps. First, we improved the current classification of EDI domains and separated medical services into procedures and measurements. Second, each EDI concept was assigned a unique identifier and validity dates. Third, we built a vertical hierarchy between EDI concepts, fully describing child concepts through relationships and attributes and linking them to parent terms. Finally, we added an English definition for each EDI concept. We translated the Korean definitions of EDI concepts using Google.Cloud.Translation.V3, using a client library and manual translation. We evaluated the EDI using 11 auditing criteria for controlled vocabularies.

We incorporated 313,431 concepts from the EDI to the OMOP Standardized Vocabularies. For 10 of the 11 auditing criteria, EDI showed a better quality index within the OMOP vocabulary than in the original EDI vocabulary.

The incorporation of the EDI vocabulary into the OMOP Standardized Vocabularies allows better standardization to facilitate network research. Our research provides a promising model for mapping Korean medical information into a global standard terminology system, although a comprehensive mapping of official vocabulary remains to be done in the future.

The incorporation of the EDI vocabulary into the OMOP Standardized Vocabularies allows better standardization to facilitate network research. Our research provides a promising model for mapping Korean medical information into a global standard terminology system, although a comprehensive mapping of official vocabulary remains to be done in the future.

Many deep learning-based predictive models evaluate the waveforms of electrocardiograms (ECGs). Because deep learning-based models are data-driven, large and labeled biosignal datasets are required. Most individual researchers find it difficult to collect adequate training data. selleck We suggest that transfer learning can be used to solve this problem and increase the effectiveness of biosignal analysis.

We applied the weights of a pretrained model to another model that performed a different task (i.e., transfer learning). We used 2,648,100 unlabeled 8.2-second-long samples of ECG II data to pretrain a convolutional autoencoder (CAE) and employed the CAE to classify 12 ECG rhythms within a dataset, which had 10,646 10-second-long 12-lead ECGs with 11 rhythm labels. We split the datasets into training and test datasets in an 82 ratio. To confirm that transfer learning was effective, we evaluated the performance of the classifier after the proposed transfer learning, random initialization, and two-dimensional transfer learning as the size of the training dataset was reduced. All experiments were repeated 10 times using a bootstrapping method. The CAE performance was evaluated by calculating the mean squared errors (MSEs) and that of the ECG rhythm classifier by deriving F1-scores.

The MSE of the CAE was 626.583. The mean F1-scores of the classifiers after bootstrapping of 100%, 50%, and 25% of the training dataset were 0.857, 0.843, and 0.835, respectively, when the proposed transfer learning was applied and 0.843, 0.831, and 0.543, respectively, after random initialization was applied.

Transfer learning effectively overcomes the data shortages that can compromise ECG domain analysis by deep learning.

Transfer learning effectively overcomes the data shortages that can compromise ECG domain analysis by deep learning.

Medical health monitoring generally refers to two important aspects of health, namely, physical and mental health. Physical health can be measured through the basic parameters of normal values of vital signs, while mental health can be known from the prevalence of mental and emotional disorders, such as stress. Currently, the medical devices that are generally used to measure these two aspects of health are still separate, so they are less effective than they might be otherwise. To overcome this problem, we designed and realized a device that can measure stress levels through vital signs of the body, namely, heart rate, oxygen saturation, body temperature, and galvanic skin response (GSR).

The sensor fusion method is used to process data from multiple sensors, so the output that shows the stress level and health status of vital signs can be more accurate and precise.

Based on the results of testing, this device is able to show the health status of vital signs and stress levels within ±20 seconds, with the accuracies of body temperature measurements, oxygen saturation, and GSR of 97.227%, 99.4%, and 98.6%, respectively.

A device for the measurement of stress levels and vital signs based on sensor fusion has been successfully designed and realized in accordance with the expected functions and specifications.

A device for the measurement of stress levels and vital signs based on sensor fusion has been successfully designed and realized in accordance with the expected functions and specifications.

The objective of this study was to introduce the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), to describe use cases of SNOMED CT with the barriers and facilitators, and finally, to propose strategies for adopting and implementing SNOMED CT in Korea as a member of SNOMED International.

We reviewed a collection of SNOMED CT documents, such as introductory materials, practical guides, technical specifications, and reference materials provided by SNOMED International and the literature on SNOMED CT published by researchers to identify use cases of SNOMED CT with barriers and facilitators. We also surveyed the attendees of SNOMED CT education and training series offered by the Korea Human Resource Development Institute for Health and Welfare to identify perceived barriers to adopting SNOMED CT in Korea.

We identified the barriers and facilitators to adopt SNOMED CT experienced by international terminology experts and prospective Korean users. They were related to governance and infrastructure, support services for use, education and training programs, use cases, and vendor capability to implement SNOMED CT. Based on these findings, we identified strategies for adopting and implementing SNOMED CT in Korea. They included the establishment of SNOMED CT management infrastructure, the development of SNOMED CT education and training programs for various user groups, the provision of support services for SNOMED CT use, and the development of SNOMED CT use cases.

These strategies for the adoption and implementation of SNOMED CT need to be executed step by step.

These strategies for the adoption and implementation of SNOMED CT need to be executed step by step.

To evaluate treatment adherence and predictors of drug discontinuation among patients with inflammatory arthritis receiving bDMARDs within the first 100 days after the announcement of COVID-19 pandemic.

A total of 1871 patients recorded in TReasure registry for whom advanced therapy was prescribed for rheumatoid arthritis (RA) or spondyloarthritis (SpA) within the 3 months (6-9 months for rituximab) before the declaration of COVID-19 pandemic were evaluated and 1394 (74.5%) responded the survey performed by phone call. Patients? data regarding demographic, clinical characteristics and disease activity before the pandemic were recorded. The patients were inquired for the diagnosis of COVID-19, the rate of continuation on bDMARDs, the reasons for treatment discontinuation, if any, and the current general disease activity (visual analog scale, [VAS]).

A total of 1,394 patients (493 RA [47.3% on anti-TNF] patients and 901 SpA [90.0% on anti-TNF] patients) were included. Overall, 2.8% of the patients had symptoms suggesting COVID-19, and 2 (0.15%) patients had polymerase chain reaction (PCR) confirmed COVID-19. Overall, 18.1% of all patients (13.8% of the RA and 20.5% of the SpA; p=0.003) discontinued their bDMARDs. In the SpA group, the patients who discontinued bDMARDs were younger (40 [21-73] vs. 44 years [20-79]; p=0.005) and had higher general disease activity; however, no difference was relevant for RA patients.

Although the COVID-19 was quite uncommon in the first 100 days of the pandemic, nearly one-fifth of the patients discontinued bDMARDs within this period. Long-term effects of the pandemic should be monitored.

Although the COVID-19 was quite uncommon in the first 100 days of the pandemic, nearly one-fifth of the patients discontinued bDMARDs within this period. Long-term effects of the pandemic should be monitored.AAbstract Background/aim Hospital-acquired acute kidney injury (HA-AKI) may commonly develop in Covid-19 patients and is expected to have higher mortality. There is little comparative data looking for the effect of HA-AKI on mortality of chronic kidney disease (CKD) patients and a control group of general population suffering from Covid-19.

HA-AKI development was assessed in a group of stage 3-5 CKD patients and control group without CKD among adult patients hospitalized for Covid-19. The role of AKI development on the outcome (in-hospital mortality and admission to the intensive care unit [ICU]) of patients with and without CKD was compared.

Among 621 hospitalized patients (age 60 [IQR47-73]), women 44.1%), AKI developed in 32.5% of the patients, as stage 1 in 84.2%, stage 2 in 8.4% and, stage 3 in 7.4%. AKI developed in 48.0 % of CKD patients, whereas in 17.6% of patients without CKD. CKD patients with HA-AKI had the highest mortality rate of 41.1 % compared to 14.3 % of patients with HA-AKI but no CKD (p<0.001). However, patients with AKI+non-CKD had similar rates of ICU admission, mechanical ventilation, and death rate as patients with CKD without AKI. Adjusted mortality risk of the AKI+non-CKD group (HR 9.0, 95%CI1.9-44.2) and AKI+CKD group (HR 7.9, 95%CI1.9-33.3) were significantly higher than non-AKI+non-CKD group.

AKI frequently develops in hospitalized patients due to Covid-19 and is associated with high mortality. HA-AKI has worse outcome whether it develops in patients with or without CKD, but the worst outcome was seen in AKI+CKD patients.

AKI frequently develops in hospitalized patients due to Covid-19 and is associated with high mortality. HA-AKI has worse outcome whether it develops in patients with or without CKD, but the worst outcome was seen in AKI+CKD patients.