about
Personalized respiratory medicine: exploring the horizon, addressing the issues. Summary of a BRN-AJRCCM workshop held in Barcelona on June 12, 2014R-U policy frontiers for health data de-identificationEHDViz: clinical dashboard development using open-source technologiesBig Data Technologies: New Opportunities for Diabetes ManagementBig data and clinical research: focusing on the area of critical care medicine in mainland ChinaBig data in oncologic imaging.Cost-of-illness studies based on massive data: a prevalence-based, top-down regression approach.No big data without small data: learning health care systems begin and end with the individual patient.Data breaches of protected health information in the United StatesCustomization of a Severity of Illness Score Using Local Electronic Medical Record Data.Personalized mortality prediction driven by electronic medical data and a patient similarity metric.Utilizing Big Data for Public Health.Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association.Data that drive: Closing the loop in the learning hospital system.Precision Health Economics and Outcomes Research to Support Precision Medicine: Big Data Meets Patient Heterogeneity on the Road to Value.Neuroinflammation - using big data to inform clinical practice.Disaggregating asthma: Big investigation versus big data.Preprocessing structured clinical data for predictive modeling and decision support. A roadmap to tackle the challenges.Data science, learning, and applications to biomedical and health sciences.Big data and clinical research: perspective from a clinician.Laboratory tests as short-term correlates of stroke.Real World Data in Adaptive Biomedical Innovation: A Framework for Generating Evidence Fit for Decision-Making.Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.Nine Principles of Semantic HarmonizationEvaluation of Healthcare Interventions and Big Data: Review of Associated Data Issues.IMPROVING THE EFFECTIVENESS AND EFFICIENCY OF EVIDENCE PRODUCTION FOR HEALTH TECHNOLOGY ASSESSMENT.Rapid identification of slow healing wounds.Setting the vision: applied patient-reported outcomes and smart, connected digital healthcare systems to improve patient-centered outcomes prediction in critical illness.Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era.Identifying health outcomes in healthcare databases.Translating Big Data into Smart Data for Veterinary Epidemiology.Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.DECISION-MAKING ALIGNED WITH RAPID-CYCLE EVALUATION IN HEALTH CARE.From adaptive licensing to adaptive pathways: delivering a flexible life-span approach to bring new drugs to patients.Observation Status or Inpatient Admission: Impact of Patient Disposition on Outcomes and Utilization Among Emergency Department Patients With Chest Pain.Development and validation of a predictive model for detection of colorectal cancer in primary care by analysis of complete blood counts: a binational retrospective studyComparing high-dimensional confounder control methods for rapid cohort studies from electronic health records.Community-acquired pneumonia (CAP) hospitalizations and deaths: is there a role for quality improvement through inter-hospital comparisons?Regularized Regression Versus the High-Dimensional Propensity Score for Confounding Adjustment in Secondary Database Analyses.Health services research: building capacity to meet the needs of the health care system.
P2860
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P2860
description
2014 nî lūn-bûn
@nan
2014 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Learning from big health care data.
@ast
Learning from big health care data.
@en
type
label
Learning from big health care data.
@ast
Learning from big health care data.
@en
prefLabel
Learning from big health care data.
@ast
Learning from big health care data.
@en
P356
P1476
Learning from big health care data.
@en
P2093
Sebastian Schneeweiss
P304
P356
10.1056/NEJMP1401111
P407
P577
2014-06-01T00:00:00Z