Property:Dct:description

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A

Add Health +The National Longitudinal Study of Adolescent to Adult Health (Add Health) is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health is re-interviewing cohort members in a Wave V follow-up from 2016-2018 to collect social, environmental, behavioral, and biological data with which to track the emergence of chronic disease as the cohort moves through their fourth decade of life. Add Health combines longitudinal survey data on respondents’ social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood, and the fifth wave of data collection continues this biological data expansion.  +
Add Health v0 +See Summary Level.  +
Add Health v0 ds +See Summary Level.  +

C

CPRD +CPRD is a database capturing inpatient and outpatient claims from the United Kingdom’s National Health Service. The online database, also called CPRD GOLD, contains patient registration information and all care events that general practitioners (GPs) have chosen to record in a singular EHR database as part of their usual medical practice. Information held includes records of clinical events (medical diagnoses), referrals to specialists and secondary care settings, prescriptions issued in primary care, records of immunizations/vaccinations, diagnostic testing, lifestyle information (e.g. smoking and alcohol status), and all other types of care administered as part of routine GP practice. Data in the online system are enhanced by the addition of central mortality data (date and causes of death) as well as certain key data from Hospital Episode Statistics (HES- hospitalized patients). Pharmacy claims are also linked to the inpatient and outpatient data. Finally, the following consented data may be available for some patients: clinical trial information, bio-samples, and patient reported outcomes. Data is collected through electronic health records and encompasses over 64 million patients and more than 25 years. The primary care population in CRPD is expected to grow over time. The database contains an open cohort for both practices and patients. All data is linked to patients and physicians so additional information to confirm diagnoses or conduct surveys is possible. Data is indexed based on NHS number, which reduces the likelihood of duplicates in the system.  +
CPRD v0 +See Summary Level.  +
CPRD v0 ds +See Summary Level.  +
ChestX-ray +The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosisof many lung diseases. A tremendous number of X-rayimaging studies accompanied by radiological reports areaccumulated and stored in many modern hospitals’ Pic-ture Archiving and Communication Systems (PACS). Onthe other side, it is still an open question how this typeof hospital-size knowledge database containing invaluableimaging informatics (i.e., loosely labeled) can be used to fa-cilitate the data-hungry deep learning paradigms in build-ing truly large-scale high precision computer-aided diagno-sis (CAD) systems. chest X-ray database,namely “ChestX-ray8”, which comprises 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels (where each image canhave multi-labels), from the associated radiological reportsusing natural language processing. Importantly, we demon-strate that these commonly occurring thoracic diseases canbe detected and even spatially-located via a unified weakly-supervised multi-label image classification and disease lo-calization framework, which is validated using our proposeddataset. Although the initial quantitative results are promis-ing as reported, deep convolutional neural network based“reading chest X-rays” (i.e., recognizing and locating thecommon disease patterns trained with only image-level la-bels) remains a strenuous task for fully-automated high pre-cision CAD systems. <br /> <br /> ChestX-ray14: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (PDF Download Available). Available from: https://www.researchgate.net/publication/320068322_ChestX-ray14_Hospital-scale_Chest_X-ray_Database_and_Benchmarks_on_Weakly-Supervised_Classification_and_Localization_of_Common_Thorax_Diseases [accessed Mar 25 2018].  +
ChestX-ray14 +The NIH Clinical Center recently released over 100,000 anonymized chest x-ray images and their corresponding data to the scientific community. The release will allow researchers across the country and around the world to freely access the datasets and increase their ability to teach computers how to detect and diagnose disease. Ultimately, this artificial intelligence mechanism can lead to clinicians making better diagnostic decisions for patients.   +
ChestX-ray14 ds +Background & Motivation: Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past, and especially in recent deep learning work on Tuberculosis (TB) classification. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study. This is evident from where the performance deep neural networks for thorax disease recognition is severely limited by the availability of only 4143 frontal view images (Openi is the previous largest publicly available chest X-ray dataset to date).In this database, we provide an enhanced version (with 6 more disease categories and more images as well) of the dataset used in the recent work which is approximately 27 times of the number of frontal chest x-ray images . Our dataset is extracted from the clinical PACS database at National Institutes of Health Clinical Center and consists of ~60% of all frontal chest x-rays in the hospital. Therefore we expect this dataset is significantly more representative to the real patient population distributions and realistic clinical diagnosis challenges, than any previous chest x-ray datasets. Of course, the size of our dataset, in terms of the total numbers of images and thorax disease frequencies, would better facilitate deep neural network training . Refer to on the details of how the dataset is extracted and image labels are mined through natural language processing (NLP).  +
Clinformatics +Optum is a clinically rich US health care claims database used to conduct research studies. Optum accesses a comprehensive, large, and robust proprietary healthcare database of Optum’s parent company. The Optum database contains health care claims from May 2000 to December 2014, covering more than 60 million people, including inpatient and outpatient claims, pharmacy claims, and laboratory results. It was acquired in 2014 as a joint partnership of LDI, CCEB, and the Scheie Eye Institute, and it is housed on HSRDC.<br />Optum™ Clinformatics™ Data Mart offers more richly detailed longitudinal information — faster — than any other product on the market. All of the data is statistically certified as de-identified by an independent third party. Even more, it’s backed by the unmatched experience of our expert marketing analytics team.<br />This powerful data set provides you with licensed access to a nexus of information that can support your organization’s analysis requirements, including:<br />• Medical claims<br />• Pharmacy claims<br />• Lab analyte results<br />• Administrative data  +
Clinformatics v7.0.4 +See Summary Level.  +
Clinformatics v7.0.4 db +See Summary Level.  +

H

HCUP +The Healthcare Cost and Utilization Project (HCUP) includes the largest collection of longitudinal hospital care data in the United States. HCUP products including State and nationwide databases, software and online tools, and reports.  +
HCUP v0 +See Summay Level.  +
HCUP v0 ds +It broadly covers data regarding Inpatients, Emergency Department, Ambulatory services.  +
Humedica +Humedica, has recently been bought by Optum and is a clinical informatics company that has a database containing information for 18.5 million patients across the US. In 2012 it had 4,956,264 unique patients.<br /> Humedica contains an extract of clinical, operational, and financial data from client EHRs and other IT systems across the continuum of care. Data available includes structured data(e.g., diagnoses, procedures, prescriptions, lab results) and unstructured data (e.g., treatment rationale) using natural language processing. This patient-based EHR database is representative of the United States within 10% of census data. Inpatient, outpatient, and pharmacy claims are available. Data are indexed by patient from the provider EHR records.  +
Humedica v0 +See Summary Level.  +
Humedica v0 db +See Summary Level.  +

K

KID +The KID is the largest publicly available all payer pediatric inpatient care database in the United states.  +
KID v0 +See Summay Level.  +
KID v0 ds +The patients under the age of 21 are considered as children and it covers kids inpatient sample.  +

M

MDS +Part of the federally mandated process for clinical assessment of all residents in Medicare and Medicaid certified nursing homes. This process provides a comprehensive assessment of each resident's functional capabilities and helps nursing home staff identify health problems.  +
MDS 3.0 +See Summary Level.  +
MDS 3.0 ds +See Summary Level.  +
MIMIC +MIMIC is an openly available dataset developed by the MIT Lab for Computational Physiology, comprising deidentified health data associated with >40,000 critical care patients. It includes demographics, vital signs, laboratory tests, medications, and more.  +
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