Introduction: Precision medicine is an important milestone toward the attainment of personalized medicine. A learning health system (LHS) may facilitate the evidence collection and knowledge generation process for disease-based research and for the diagnosis, classification, or treatment of each disease subtype to improve patient care.
Methods: The LHS design and implementation used by Taichung Veterans General Hospital (TCVGH) in Taiwan for their newly funded precision medicine research, a dementia registry study, was modeled from an LHS developed at the National Institutes of Health in the United States. This Clinical Informatics and Management System (CIMS), including its subsystems, facilitates and enhances operations associated with the institutional review board, clinical research data collection and study management, the hospital biobank, and the participating health research centers to support their precision medicine research aimed at improving patient care.
Results: The implementation of a shared-design, full-cycle LHS with an enhanced CIMS, combined with hospital-based real-world data marts, has made the TCVGH dementia registry study a reality. The research data, including clinical assessment and genomics analysis information collected in CIMS, combined with data marts, are the foundation of the TCVGH dementia registry for outcome analyses. These high-quality datasets are useful for clinical validation, new hypotheses, and knowledge generation, leading to new clinical recommendations or guidelines for better patient treatment and care. The cyclic data flow supports the full-cycle LHS for TCVGH's dementia research to improve the care of elderly patients.
Conclusions: Knowledge generation requires high-quality research and health care datasets. While the details of LHS implementation methods in the United States and Taiwan may differ slightly, the LHS concept design and basic system architecture, with improved CIMSs, were proven feasible. As a result, learning health processes in support of translational research and the potential for improvement in patient care were significantly facilitated.
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http://dx.doi.org/10.1002/lrh2.10071 | DOI Listing |
J Clin Sleep Med
January 2025
Univ. Bordeaux, CNRS, SANPSY, UMR 6033, F-33000 Bordeaux, France.
Study Objectives: Both the (ICSD) and the sleep-wake disorders section of the (DSM) emphasize the importance of clinical judgment in distinguishing the normal from the pathological in sleep medicine. The fourth edition of the DSM (DSM-IV, 1994) introduced the clinical significance criterion (CSC) to standardize this judgment and enhance diagnostic reliability.
Methods: This review conducts a theoretical and historical content analysis of CSC presence, frequency, and formulation in the diagnostic criteria of sleep disorders.
BMC Musculoskelet Disord
January 2025
Department of Orthopedics, Peking University Third Hospital, No. 49. North Garden Street, Hai Dian District, Beijing, 100191, People's Republic of China.
Background: For degenerative lumbar scoliosis (DLS), prior studies mainly focused on the preoperative relationship between spinopelvic parameters and health-related quality of life (HRQoL), lacking an exhaustive evaluation of the postoperative situation. Therefore, the postoperative parameters most closely bonded with clinical outcomes has not yet been well-defined in DLS patients. The objective of this study was to comprehensively assess the correlation between radiographic parameters and HRQoL before and after surgery, and to identified the most valuable spinopelvic parameters for postoperative curative effect.
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January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
View Article and Find Full Text PDFSci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFNat Commun
January 2025
Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Gut microbiota disruptions after allogeneic hematopoietic cell transplantation (alloHCT) are associated with increased risk of acute graft-versus-host disease (aGVHD). We designed a randomized, double-blind placebo-controlled trial to test whether healthy-donor fecal microbiota transplantation (FMT) early after alloHCT reduces the incidence of severe aGVHD. Here, we report the results from the single-arm run-in phase which identified the best of 3 stool donors for the randomized phase.
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