A brief introduction to the FUTang Updating medical REcords (FUTURE) database.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang X
PROVIDER: S-EPMC8458714 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:electronic Medical Records & Genomics (eMERGE)
| PRJNA158491 | ENA
Project description:Capgras delusion is scientifically important but most commonly reported as single case studies. Studies analysing large clinical records databases focus on common disorders but none have investigated rare syndromes.Identify cases of Capgras delusion and associated psychopathology, demographics, cognitive function and neuropathology in light of existing models.Combined computational data extraction and qualitative classification using 250 000 case records from South London and Maudsley Clinical Record Interactive Search (CRIS) database.We identified 84 individuals and extracted diagnosis-matched comparison groups. Capgras was not 'monothematic' in the majority of cases. Most cases involved misidentified family members or close partners but others were misidentified in 25% of cases, contrary to dual-route face recognition models. Neuroimaging provided no evidence for predominantly right hemisphere damage. Individuals were ethnically diverse with a range of psychosis spectrum diagnoses.Capgras is more diverse than current models assume. Identification of rare syndromes complements existing 'big data' approaches in psychiatry.V.B. is supported by a Wellcome Trust Seed Award in Science (200589/Z/16/Z) and the UCLH NIHR Biomedical Research Centre. S.W. is supported by a Wellcome Trust Strategic Award (WT098455MA). Q.D. has received a grant from King's Health Partners.© The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.
| S-EPMC5541249 | biostudies-other
Project description:The Electronic Medical Records and Genomics Network is a National Human Genome Research Institute-funded consortium engaged in the development of methods and best practices for using the electronic medical record as a tool for genomic research. Now in its sixth year and second funding cycle, and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from electronic medical records can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and health-care informatics, particularly for electronic phenotyping, genome-wide association studies, genomic medicine implementation, and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here, we describe the evolution, accomplishments, opportunities, and challenges of the network from its inception as a five-group consortium focused on genotype-phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting toward the implementation of genomic medicine.
| S-EPMC3795928 | biostudies-literature
Project description:Patterns of disease co-occurrence that deviate from statistical independence may represent important constraints on biological mechanism, which sometimes can be explained by shared genetics. In this work we study the relationship between disease co-occurrence and commonly shared genetic architecture of disease. Records of pairs of diseases were combined from two different electronic medical systems (Columbia, Stanford), and compared to a large database of published disease-associated genetic variants (VARIMED); data on 35 disorders were available across all three sources, which include medical records for over 1.2 million patients and variants from over 17,000 publications. Based on the sources in which they appeared, disease pairs were categorized as having predominant clinical, genetic, or both kinds of manifestations. Confounding effects of age on disease incidence were controlled for by only comparing diseases when they fall in the same cluster of similarly shaped incidence patterns. We find that disease pairs that are overrepresented in both electronic medical record systems and in VARIMED come from two main disease classes, autoimmune and neuropsychiatric. We furthermore identify specific genes that are shared within these disease groups.
| S-EPMC4846031 | biostudies-literature
Project description:Machine learning (ML) techniques have been widely used to address mental health questions. We discuss two main aspects of ML in psychiatry in this paper, that is, supervised learning and unsupervised learning. Examples are used to illustrate how ML has been implemented in recent mental health research.
| S-EPMC7003370 | biostudies-literature
Project description:The disease known as cerebral cavernous malformations mostly occurs in the central nervous system, and their typical histological presentations are multiple lumen formation and vascular leakage at the brain capillary level, resulting in disruption of the blood-brain barrier. These abnormalities result in severe neurological symptoms such as seizures, focal neurological deficits and hemorrhagic strokes. CCM research has identified 'loss of function' mutations of three ccm genes responsible for the disease and also complex regulation of multiple signaling pathways including the WNT/?-catenin pathway, TGF-? and Notch signaling by the ccm genes. Although CCM research is a relatively new and small scientific field, as CCM research has the potential to regulate systemic blood vessel permeability and angiogenesis including that of the blood-brain barrier, this field is growing rapidly. In this review, I will provide a brief overview of CCM pathogenesis and function of ccm genes based on recent progress in CCM research. [BMB Reports 2016; 49(5): 255-262].
| S-EPMC5070704 | biostudies-literature
Project description:Intergroup comparability is of paramount importance in clinical research since it is impossible to draw conclusions on a treatment if populations with different characteristics are compared. While an adequate randomization process in randomized controlled trials (RCTs) ensures a balanced distribution of subjects between groups, the distribution in observational prospective and retrospective studies may be influenced by many confounders. Propensity score (PS) is a statistical technique that was developed more than 30 years ago with the purpose of estimating the probability to be assigned to a group. Once evaluated, the PS could be used to adjust and balance the groups using different methods such as matching, stratification, covariate adjustment, and weighting. The validity of PS is strictly related to the confounders used in the model, and confounders that are either not identified or not available will produce biases in the results. RCTs will therefore continue to provide the highest quality of evidence, but PS allows fine adjustments on otherwise unbalanced groups, which will increase the strength and quality of observational studies.
| S-EPMC7403107 | biostudies-literature
Project description:In this research, we aimed to harness machine learning to predict the imminent risk of acute exacerbation in chronic obstructive pulmonary disease (AECOPD) patients. Utilizing retrospective data from electronic medical records of two Taiwanese hospitals, we identified 26 critical features. To predict 3- and 6-month AECOPD occurrences, we deployed five distinct machine learning algorithms alongside ensemble learning. The 3-month risk prediction was best realized by the XGBoost model, achieving an AUC of 0.795, whereas the XGBoost was superior for the 6-month prediction with an AUC of 0.813. We conducted an explainability analysis and found that the episode of AECOPD, mMRC score, CAT score, respiratory rate, and the use of inhaled corticosteroids were the most impactful features. Notably, our approach surpassed predictions that relied solely on CAT or mMRC scores. Accordingly, we designed an interactive prediction system that provides physicians with a practical tool to predict near-term AECOPD risk in outpatients.
| S-EPMC10993192 | biostudies-literature
Project description:ObjectiveThis study aimed to establish a pediatric lower respiratory tract infections (PLRTIs) database based on the structured electronic medical records (SEMRs), to provide a brief overview and the usage process of the SEMRs and the database.MethodsAll the medical information is recorded by a clinical information system developed by Eureka Systems Company. A plugin of the software was used to set the properties of items of the SEMR. Children with lower respiratory tract infections (LRTIs) who were admitted to the department of respiratory medicine of our hospital from May 2020 were included. PostgreSQL 13.1 software was used to construct the PLRTIs database.ResultsSeven kinds of SEMRs were established, and the admission record was the most important and complex among them. It was mainly composed of 10 parts, i.e., basic information, history of present illness, past history (without respiratory disease), past history of respiratory diseases, personal history, family history, physical examination, the score of LRTIs, auxiliary examination, and diagnosis. With the three-level doctor ward round, the recorded information of the SEMR would be checked repeatedly, thus guaranteeing the correctness of the information. The data of the SEMR and laboratory tests could be extracted directly from the hospital information system (HIS) by PostgreSQL 13.1 software with the specific structured query language (SQL) code. After manually checking the original records, the datasets were imported into PostgreSQL 13.1 software, and a simple PLRTIs database was constructed. According to the inclusion criteria of this study, a total of 1,184 children with LRTIs were included in this database from 1 May 2020 to 30 April 2021.ConclusionA series of SEMRs for PLRTIs were designed and used during the hospitalization. A simple PLRTIs database was established based on the SEMR. The SEMRs will provide complete and high-quality data on LRTIs in children.
| S-EPMC9243234 | biostudies-literature
Project description:Nicotine is a highly addictive drug found in tobacco that drives its continued use despite the harmful consequences. The initiation of nicotine abuse involves the mesolimbic dopamine system, which contributes to the rewarding sensory stimuli and associative learning processes in the beginning stages of addiction. Nicotine binds to neuronal nicotinic acetylcholine receptors (nAChRs), which come in a diverse collection of subtypes. The nAChRs that contain the α4 and β2 subunits, often in combination with the α6 subunit, are particularly important for nicotine's ability to increase midbrain dopamine neuron firing rates and phasic burst firing. Chronic nicotine exposure results in numerous neuroadaptations, including the upregulation of particular nAChR subtypes associated with long-term desensitization of the receptors. When nicotine is no longer present, for example during attempts to quit smoking, a withdrawal syndrome develops. The expression of physical withdrawal symptoms depends mainly on the α2, α3, α5, and β4 nicotinic subunits in the epithalamic habenular complex and its target regions. Thus, nicotine affects diverse neural systems and an array of nAChR subtypes to mediate the overall addiction process. This article is part of the special issue on 'Contemporary Advances in Nicotine Neuropharmacology'.
| S-EPMC7554201 | biostudies-literature