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MIMIC-III, a freely accessible critical care database
TLDR
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Expand
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AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017
TLDR
The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9–61 s) ECG recordings performed by patients. Expand
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Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database*
Objective:We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population ofExpand
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Automated de-identification of free-text medical records
TLDR
We describe an automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc. Expand
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Active reliable multicast
TLDR
We present a novel loss recovery scheme that utilizes intermediate routers to reduce both NACKs and repair traffic. Expand
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Methods of Blood Pressure Measurement in the ICU*
Objective:Minimal clinical research has investigated the significance of different blood pressure monitoring techniques in the ICU and whether systolic vs. mean blood pressures should be targeted inExpand
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A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction
TLDR
We used a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort. Expand
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Risk Stratification of ICU Patients Using Topic Models Inferred from Unstructured Progress Notes
TLDR
We propose a novel approach for ICU patient risk stratification by combining the learned "topic" structure of clinical concepts (represented by UMLS codes) extracted from the unstructured nursing notes with physiologic data (from SAPS-I) for hospital mortality prediction. Expand
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Evaluating Reinforcement Learning Algorithms in Observational Health Settings
TLDR
We present a conceptual starting point for clinical and computational researchers to ask the right questions when designing and evaluating algorithms for new ways of treating patients. Expand
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Representation Learning Approaches to Detect False Arrhythmia Alarms from ECG Dynamics
TLDR
We propose using a Supervised Denoising Autoencoder (SDAE) to detect false alarms using a low-dimensional representation of ECG dynamics learned by minimizing a combined reconstruction and classification loss. Expand
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