Mangumgoldman0699
We propose the Lifelog Bigdata Platform as a sustainable digital healthcare system based on individual-centric lifelog datasets and describe the standardization of lifelog and clinical data in its full-cycle management system.
The Lifelog Bigdata Platform was developed by Yonsei Wonju Health System on the cloud to support digital healthcare and precision medicine. It consists of five core components data acquisition system, de-identification of individual information, lifelog integration, analyzer, and service. click here We designed a gathering system into a dedicated virtual machine to save lifelog or clinical outcomes and established standard guidelines for maintaining the quality of gathering procedures. We used standard integration keys to integrate the lifelog and clinical data. Metadata were generated from the data warehouse after loading combined or fragmented data on it. We analyzed the de-identified lifelog and clinical data using the lifelog analyzer to prevent and manage acute and chronic diseases through providing results of statistics on analysis.
The big data centers were built in four hospitals and seven companies for integrating lifelog and clinical data to develop the Lifelog Bigdata Platform. We integrated and loaded lifelog big data and clinical data for 3 years. In the first year, we uploaded 94 types of data on the platform with a total capacity of 221 GB.
The Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem.
The Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem.
Digital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM).
R-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristicge classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.
In this paper, we propose deep-learning methodology with which to enhance the mass differentiation performance of convolutional neural network (CNN)-based architecture.
We differentiated breast mass lesions from gray-scale X-ray mammography images based on regions of interest (ROIs). Our dataset comprised breast mammogram images for 150 cases of malignant masses from which we extracted the mass ROI, and we composed a CNN-based deep learning model trained on this dataset to identify ROI mass lesions. The test dataset was created by shifting some of the training data images. Thus, although both datasets were different, they retained a deep structural similarity. We then applied our trained deep-learning model to detect masses on 8-bit mammogram images containing malignant masses. The input images were preprocessed by applying a scaling parameter of intensity before being used to train the CNN model for mass differentiation.
The highest area under the receiver operating characteristic curve was 0.897 (Î 20).
Our results indicated that the proposed patch-wise detection method can be utilized as a mass detection and segmentation tool.
Our results indicated that the proposed patch-wise detection method can be utilized as a mass detection and segmentation tool.
This study was conducted to build a direction for government policies regarding strategies for the commercialization of digital therapeutics in Korea, as well as its globalization.
The study included 37 participants from the Korea Digital Health Industry Association (KODHIA). The data was based on a survey conducted in 2020 targeting employees of companies engaged in the digital health industry in Korea. Participants were asked about their involvement in product development of digital therapeutics and their opinion about the growing motivator for digital therapeutics in Korea and the global market.
According to our data, among subjects not involved in making digital therapeutics products, the main reason for not being involved was the lack of experts (73.9%) and difficulty in licensing (73.9%). Responses concerning the priority area in need of national support were R&D funding (43.2%), and the next was licensing guidance and simplifying regulations (24.3%). Possible difficulties of overseas market expansion were the unfamiliarity in digital therapeutics technology verification and licensing structures of foreign countries (73%), and concerns regarding the level of recognition of clinical trials and technology in Korea from overseas (70.3%). Overall, respondents were hesitant in starting a related business due to the lack of government support and the complexity of the regulation process. Moreover, concerns about global market entry were similar. Being unfamiliar with the novel process and worrying about the achievement despite existing challenges were the biggest drawback.
For the digital therapeutics industry to evolve domestically and internationally, government support and guidance are essential.
For the digital therapeutics industry to evolve domestically and internationally, government support and guidance are essential.
The study aimed to identify which digital biomarkers are collected and which specific devices are used according to vulnerable and susceptible individual characteristics in a living-lab setting.
A literature search, screening, and appraisal process was implemented using the Web of Science, Pubmed, and Embase databases. The search query included a combination of terms related to "digital biomarkers," "devices that collect digital biomarkers," and "vulnerable and susceptible groups." After the screening and appraisal process, a total of 37 relevant articles were obtained.
In elderly people, the main digital biomarkers measured were values related to physical activity. Most of the studies used sensors. The articles targeting children aimed to predict diseases, and most of them used devices that are simple and can induce some interest, such as wearable device-based smart toys. In those who were disabled, digital biomarkers that measured location-based movement for the purpose of diagnosing disabilities were widely used, and most were measured by easy-to-use devices that did not require detailed explanations. In the disadvantaged, digital biomarkers related to health promotion were measured, and various wearable devices, such as smart bands and headbands were used depending on the purpose and target.
As the digital biomarkers and devices that collect them vary depending on the characteristics of study subjects, researchers should pay attention not only to the purpose of the study but also the characteristics of study subjects when collecting and analyzing digital biomarkers from living labs.
As the digital biomarkers and devices that collect them vary depending on the characteristics of study subjects, researchers should pay attention not only to the purpose of the study but also the characteristics of study subjects when collecting and analyzing digital biomarkers from living labs.
Mobile applications are widely used in the healthcare market. This study aimed to determine whether exercise using a machine learning-based motion-detecting mobile exercise coaching application (MDMECA) is superior to video streaming-based exercise for improving quality of life and decreasing lower back pain.
The same 14-day daily workout program consisting of five exercises was performed by 104 participants using the MDMECA and another 72 participants using video streaming. The Medical Outcomes Study Short Form 36-Item Health Survey (SF-36) and lower back pain scores were assess as pre- and post-workout measurements. Scores for the treatment-satisfaction subscale of the visual analog scale (TS-VAS), intention to use a disease-oriented exercise program, intention to recommend the program to others, and available expenses for a disease-oriented exercise program were determined after the workout.
The MDMECA group showed a higher increase in SF-36 score (MDMECA, 9.10; control, 1.09;
<0.01) and a greater reduction in lower back pain score (MDMECA, -0.96; control, -0.26;
<0.01). Scores for TS-VAS, intention to use a disease-oriented exercise program, and intention to recommend the program to others were all higher (
<0.01) in the MDMECA group. However, the available expenses for a disease-oriented program were not significantly different between the two groups.
The MDMECA is more effective than video streaming-based exercise in increasing exercise adherence, improving QoL, and reducing lower back pain. MDMECAs could be promising tools of use to achieve better medical outcomes and higher treatment satisfaction.
The MDMECA is more effective than video streaming-based exercise in increasing exercise adherence, improving QoL, and reducing lower back pain. MDMECAs could be promising tools of use to achieve better medical outcomes and higher treatment satisfaction.In response to the global spread of coronavirus disease-2019 (COVID-19), many countries have expanded access to non-contact healthcare. This study aimed to investigate the current state of non-contact healthcare in developed countries before and after the outbreak of the COVID-19 pandemic, and examine the potential clinical and political implications applicable to Korea. Before the COVID-19 outbreak, non-contact healthcare was provided to a limited extent. However, given the surge in COVID-19 cases, countries have lifted the restrictions on non-contact healthcare by expanding eligibility to patients and providers and the range of services. Countries that were slow to implement non-contact healthcare before the pandemic experienced a paradigm shift. Non-contact healthcare has advantages in maintaining essential health services while protecting patients and providers from viral infections. In Korea, non-contact healthcare was regarded as a business sector, so it has not been formally discussed from a public health standpoint. Given this global urgency, discussions should begin surrounding how to best utilize non-contact healthcare, considering the values, safety, and efficacy from the perspective of continuity of patient care. Non-contact healthcare should shift to utilizing a patient-centered approach. The step-by-step strategic planning of non-contact healthcare is imperative for ensuring value, quality, equity, and safety of services.
