Výsledky vyhledávání - "Jeanne M. Huddleston"

Prediction and Inference With Missing Data in Patient Alert Systems

Autor: Nicholas Chia, Santiago Romero-Brufau, Rickey E. Carter, John R. Bergquist, Curtis B. Storlie, Jeanne M. Huddleston, Terry M. Therneau

Publikováno v: Journal of the American Statistical Association. 115:32-46

We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-ICU patients using ~200 variables (vitals, lab results, assessments, ...). There are several missing predictor values for most patien

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70af33be55cd096027e427b087739b00
https://doi.org/10.1080/01621459.2019.1604359

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Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS)

Autor: Matthew G. Johnson, Daniel Whitford, Santiago Romero-Brufau, Joel Hickman, Jeanne M. Huddleston, Terry M. Therneau, James M. Naessens, Bruce W. Morlan

Publikováno v: J Am Med Inform Assoc

Objective We aimed to develop a model for accurate prediction of general care inpatient deterioration. Materials and Methods Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model train

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::101a9050b922560d5bdddd9a88f10281
https://pubmed.ncbi.nlm.nih.gov/33638343

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Optimization and Simulation of Orthopedic Spine Surgery Cases at Mayo Clinic

Autor: Hari Balasubramanian, Jeanne M. Huddleston, Thomas R. Rohleder, Paul M. Huddleston, Asli Ozen, Yariv N. Marmor

Publikováno v: Manufacturing & Service Operations Management. 18:157-175

Spine surgeries tend to be lengthy (mean time of 4 hours) and highly variable (with some surgeries lasting 18 hours or more). This variability along with patient preferences driving scheduling decisions resulted in both low operating room (OR) utiliz

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ea1e1144a6ee9b68cfff9aead6133d5e
https://doi.org/10.1287/msom.2015.0564

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Recent Temporal Pattern Mining for Septic Shock Early Prediction

Autor: Min Chi, Muge Capan, Ryan Arnold, Julie S. Ivy, Jeanne M. Huddleston, Farzaneh Khoshnevisan

Publikováno v: ICHI

Sepsis is a leading cause of in-hospital death over the world and septic shock, the most severe complication of sepsis, reaches a mortality rate as high as 50%. Early diagnosis and treatment can prevent most morbidity and mortality. In this work, Rec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::792b64bdb760c85e20dd4d8bb641b32d
https://doi.org/10.1109/ichi.2018.00033

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Early Diagnosis and Prediction of Sepsis Shock by Combining Static and Dynamic Information Using Convolutional-LSTM

Autor: Jeanne M. Huddleston, Ryan Arnold, Muge Capan, Yuan Zhangy, Chen Lin, Min Chi, Julie S. Ivy

Publikováno v: ICHI

Deep neural network models, especially Long Short Term Memory (LSTM), have shown great success in analyzing Electronic Health Records (EHRs) due to their ability to capture temporal dependencies in time series data. In this paper, we proposed a gener

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::45c2268a628a91716b21322bebda936c
https://doi.org/10.1109/ichi.2018.00032

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Identifying patients at risk of inhospital death or hospice transfer for early goals of care discussions in a US referral center: the HELPS model derived from retrospective data

Autor: Daniel Whitford, Dennis M. Manning, Jeanne M. Huddleston, Kevin J. Whitford, Santiago Romero-Brufau

Publikováno v: BMJ Open

Objective Create a score to identify patients at risk of death or hospice placement who may benefit from goals of care discussion earlier in the hospitalisation. Design Retrospective cohort study to develop a risk index using multivariable logistic r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::24ad1814212d65cc3a428386d5068845
http://europepmc.org/articles/PMC5780692

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A stochastic model of acute-care decisions based on patient and provider heterogeneity

Autor: Julie S. Ivy, Jeanne M. Huddleston, James R. Wilson, Muge Capan

Publikováno v: Health Care Management Science

The primary cause of preventable death in many hospitals is the failure to recognize and/or rescue patients from acute physiologic deterioration (APD). APD affects all hospitalized patients, potentially causing cardiac arrest and death. Identifying A

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57c75eb065be0dfbe7f0fac5706b5eeb
https://doi.org/10.1007/s10729-015-9347-x

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LSTM for septic shock: Adding unreliable labels to reliable predictions

Autor: Yuan Zhang, Julie S. Ivy, Min Chi, Muge Capan, Jeanne M. Huddleston, Chen Lin

Publikováno v: IEEE BigData
Web of Science

Sepsis is a leading cause of death over the world and septic shock, the most severe complication of sepsis, reaches a mortality rate as high as 50%. Early diagnosis and treatment can prevent most morbidity and mortality. Nowadays, the increasing avai

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::832fd32f52a584c4d7e4dd6a5a4cb8d1
https://doi.org/10.1109/bigdata.2017.8258049

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