Implementing electronic health care predictive analytics: considerations and challenges.
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Acquisition, Analysis, and Sharing of Data in 2015 and Beyond: A Survey of the Landscape: A Conference Report From the American Heart Association Data Summit 2015Inclusion of dynamic clinical data improves the predictive performance of a 30-day readmission risk model in kidney transplantation.Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach.Pharmacy Practice, Education, and Research in the Era of Big Data: 2014-15 Argus Commission Report.Predicting all-cause readmissions using electronic health record data from the entire hospitalization: Model development and comparison.Big data analytics to improve cardiovascular care: promise and challenges.Integrating data from randomized controlled trials and observational studies to predict the response to pregabalin in patients with painful diabetic peripheral neuropathy.Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study.Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges.Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External ValidationNational Veterans Health Administration inpatient risk stratification models for hospital-acquired acute kidney injury.Participatory design of probability-based decision support tools for in-hospital nurses.Perioperative and ICU Healthcare Analytics within a Veterans Integrated System Network: a Qualitative Gap Analysis.Calibration drift in regression and machine learning models for acute kidney injury.Predictive Analytics to Support Real-Time Management in Pathology Facilities.Visual analytics in healthcare--opportunities and research challenges.Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment.Calibrating actigraphy to improve sleep efficiency estimates.Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data.
P2860
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P2860
Implementing electronic health care predictive analytics: considerations and challenges.
description
2014 nî lūn-bûn
@nan
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
2014年论文
@zh
2014年论文
@zh-cn
name
Implementing electronic health care predictive analytics: considerations and challenges.
@en
type
label
Implementing electronic health care predictive analytics: considerations and challenges.
@en
prefLabel
Implementing electronic health care predictive analytics: considerations and challenges.
@en
P2093
P1433
P1476
Implementing electronic health care predictive analytics: considerations and challenges.
@en
P2093
Marco Huesch
Nam Q Nguyen
Rachel E Patzer
Ruben Amarasingham
P304
P356
10.1377/HLTHAFF.2014.0352
P407
P577
2014-07-01T00:00:00Z