A comprehensive EHR timeseries pre-training benchmark

A comprehensive EHR timeseries pre-training benchmark

Bibliographic Details
Main Authors:	McDermott, Matthew, Nestor, Bret, Kim, Evan, Zhang, Wancong, Goldenberg, Anna, Szolovits, Peter, Ghassemi, Marzyeh
Other Authors:	Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format:	Article
Language:	English
Published:	ACM 2022
Online Access:	https://hdl.handle.net/1721.1/143906

Similar Items

Representation learning in multi-dimensional clinical timeseries for risk and event prediction
by: Ghassemi, Marzyeh
Published: (2017)

A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data
by: Pimentel, Marco A. F., et al.
Published: (2017)

Semi-supervised biomedical translation with cycle Wasserstein regression GaNs
by: McDermott, Matthew, et al.
Published: (2020)

Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasing
by: Gowda, Sindhu, et al.
Published: (2022)

ORBDA: An openEHR benchmark dataset for performance assessment of electronic health record servers.
by: Douglas Teodoro, et al.
Published: (2018-01-01)

Comparison of Three Imputation Methods for Groundwater Level Timeseries
by: Mara Meggiorin, et al.
Published: (2023-02-01)

Experimental evaluation of baselines for forecasting social media timeseries
by: Kin Wai Ng, et al.
Published: (2023-03-01)

METHODS AND CHALLENGES IN TIMESERIES ANALYSIS OF VEGETATION IN THE GEOSPATIAL DOMAIN
by: A. Elia, et al.
Published: (2023-06-01)

A Hilbert-based method for processing respiratory timeseries
by: Samuel J. Harrison, et al.
Published: (2021-04-01)

Deep learning benchmarks on L1000 gene expression data
by: McDermott, Matthew B. A.(Matthew Brian Andrew)
Published: (2019)

Factors that Determine Comprehensive Categorical Classification of EHR Implementation Levels
by: Soumya Upadhyay, et al.
Published: (2021-06-01)

An Imputing Technique for Surface Water Extent Timeseries with Streamflow Discharges
by: Yue Yin, et al.
Published: (2024-01-01)

Designing Bivariate Auto-Regressive Timeseries with Controlled Granger Causality
by: Shohei Hidaka, et al.
Published: (2021-06-01)

Projecting Annual Rainfall Timeseries Using Machine Learning Techniques
by: Kyriakos Skarlatos, et al.
Published: (2023-02-01)

RITA: Group Attention is All You Need for Timeseries Analytics
by: Liang, Jiaming, et al.
Published: (2024)

Data-driven construction of visual query interface for timeseries data
by: Li, Yan
Published: (2021)

Ausgehandelte Ehre
by: Karl-Heinz Lingens
Published: (2018-01-01)

Modeling EHR with the openEHR approach: an exploratory study in China
by: Lingtong Min, et al.
Published: (2018-08-01)

Augmented EHR: Enrichment of EHR with Contents from Semantic Web Sources
by: Alejandro Mañas-García, et al.
Published: (2021-04-01)

Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients
by: Vipina K. Keloth, et al.
Published: (2023-02-01)

Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries
by: Javier Ruiz-Ramos, et al.
Published: (2020-09-01)

Fuzzy Algebraic Modeling of Spatiotemporal Timeseries’ Paradoxes in Cosmic Scale Kinematics
by: Lazaros Iliadis
Published: (2022-02-01)

OPERATIONAL APPLICATION OF THE LANDSAT TIMESERIES TO ADDRESS LARGE AREA LANDCOVER UNDERSTANDING
by: P. Scarth, et al.
Published: (2015-08-01)

Zuviel der Ehre!
by: Miloš Vec
Published: (2017-01-01)

Prediction using patient comparison vs. modeling: A case study for mortality prediction
by: Hoogendoorn, Mark, et al.
Published: (2017)

Machine learning COVID-19 detection from wearables
by: Bret Nestor, et al.
Published: (2023-04-01)

Evaluating ARIMA-neural network hybrid model performance in forecasting stationary timeseries
by: Seyedi, Seyed Navid, et al.
Published: (2014)

Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
by: Shan He, et al.
Published: (2022-08-01)

An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images
by: Jingtao Li, et al.
Published: (2020-12-01)

Accuracy and Reproducibility of Coral Sr/Ca SIMS Timeseries in Modern and Fossil Corals
by: Hussein R. Sayani, et al.
Published: (2022-09-01)

Unbiased and efficient sampling of timeseries reveals redundancy of brain network and gradient structure
by: Aditya Nanda, et al.
Published: (2023-07-01)

GEE TIMESERIES EXPLORER FOR QGIS – INSTANT ACCESS TO PETABYTES OF EARTH OBSERVATION DATA
by: P. Rufin, et al.
Published: (2021-08-01)

Classification of Fixed Point Network Dynamics from Multiple Node Timeseries Data
by: David Blaszka, et al.
Published: (2017-09-01)

Monitoring Subsidence Deformation of Suzhou Subway Using InSAR Timeseries Analysis
by: Xiaobo Xu, et al.
Published: (2021-01-01)

MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
by: Wang, Shirly, et al.
Published: (2022)

TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology
by: Christopher Y. S. Wong, et al.
Published: (2023-03-01)

ClustEHR: a tool for generating synthetic EHR data for unsupervised learning experiments.
by: Nonie Alexander, et al.
Published: (2022-08-01)

Die Ehre der Polizei und die Ehre der Polizisten ist zweierlei
by: Maximilian Steinbeis

REflex: Flexible Framework for Relation Extraction in Multiple Domains
by: Chauhan, Geeticka, et al.
Published: (2021)

Artificial Intelligence – the EHR savior?
by: Kashif Qureshi
Published: (2020-08-01)