About: Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data Goto Sponge NotDistinct Permalink

An Entity of Type : wikibase:Item, within Data Space : wikidata.demo.openlinksw.com associated with source document(s)

scientific article published on 01 October 2017

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) наукова стаття, опублікована в жовтні 2017 (uk) artículu científicu espublizáu n'ochobre de 2017 (ast) scientific article published on 01 October 2017 (en)
publication date	wds:Q58586002-81B47D2C-5703-4563-B017-513D25A84B79
publication date	2017-10-01 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q58586002-F4AB0F32-244D-4903-9874-B1E067D44493
language of work or name	English
cites work	wds:Q58586002-00136647-50C7-4A97-AE06-F2E3890A87B5 wds:Q58586002-06A16107-824C-4DA7-95F9-0DB014A39BF5 wds:Q58586002-17EF7F09-BCB6-4994-87E0-F04225757741 wds:Q58586002-18A1C9B8-7306-4CAE-AE82-9224F9637FB2 wds:Q58586002-2387FA93-E6DB-4ACF-9500-19DD4BDDA603 wds:Q58586002-2A3599DF-5D09-4683-A471-B341EC94DB4B wds:Q58586002-306CFE7E-9E94-4AF9-A487-411DF9B1D2C3 wds:Q58586002-39057E64-87F8-4FC7-9A20-7DD924607212 wds:Q58586002-4728DA37-121C-49A3-BB52-A99CD91C0192 wds:Q58586002-50E87032-E4A1-4AE5-8708-9F6949A13D65 wds:Q58586002-55C7037A-6105-44CD-A4E0-4F4E3D782647 wds:Q58586002-57A3862A-1458-4643-89CB-6B90C9B46699 wds:Q58586002-585D7F7A-1064-4FCF-8D30-B5EA762369BB wds:Q58586002-63A992BB-5C9B-44D5-8707-B5923C00DFE3 wds:Q58586002-65D43E69-4A4E-41BE-B8C0-5D3336C4FD07 wds:Q58586002-6979D75B-ED2B-43B0-AAA4-D3E1D514F695 wds:Q58586002-729F214D-BB85-4526-B650-799A630D8406 wds:Q58586002-7577E383-205E-469F-9728-1085B6C6A1E8 wds:Q58586002-75EBDC3A-B496-4B5D-A1FE-A03A1E11563A wds:Q58586002-788DE399-34D8-471B-9B0E-6428C39AE998 wds:Q58586002-7B6AA2A1-97B3-4FE5-98C6-B2C9FC3FACA3 wds:Q58586002-869EB06A-8905-4CAE-9A79-199E0333B1A5 wds:Q58586002-925392DF-8A5E-4874-96CA-748AC3E1255B wds:Q58586002-9D88F647-B1E1-46FC-9C88-186FBC423259 wds:Q58586002-B3E64E1A-B59E-422B-A729-E04F055B04CE wds:Q58586002-BAAD6CC2-9A77-4F4E-B24E-A7494F082A49 wds:Q58586002-BE273AE9-ADE8-41B9-91A1-3403AD440DD7 wds:Q58586002-C0C3998B-EB85-4634-BCCF-69A96AE5A903 wds:Q58586002-C8FB4ABD-ECE2-4105-8C12-0B09F9EBFF52 wds:Q58586002-CED818B0-121F-4EB2-9E0E-BB7213CA7E81 wds:Q58586002-CFB98B9B-9BBD-4AE9-92B5-787E418F37E8 wds:Q58586002-D099BB44-600B-4F91-9097-4A476DB4D7D7 wds:Q58586002-EBC8D9A7-9D80-4EE3-9F7F-6591BD63E445 wds:Q58586002-F004DA2D-675E-4492-A674-0A1D5779AEB8 wds:Q58586002-FC3A5846-62D8-4CA1-B6EF-B4BFE5F7EE70
cites work	The confusion assessment method for the intensive care unit (CAM-ICU) and intensive care delirium screening checklist (ICDSC) for the diagnosis of delirium: a systematic review and meta-analysis of clinical studies The epidemiology of delirium: challenges and opportunities for population studies. Delirium as a predictor of mortality in mechanically ventilated patients in the intensive care unit One-year health care costs associated with delirium in the elderly population Index for rating diagnostic tests Comorbidity measures for use with administrative data Prevalence and risk factors for development of delirium in surgical and trauma intensive care unit patients Incidence, risk factors and consequences of ICU delirium Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression POSSUM: a scoring system for surgical audit. Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches A new method of classifying prognostic comorbidity in longitudinal studies: development and validation Delirium in elderly adults: diagnosis, prevention and treatment Comparing logistic regression, support vector machines, and permanental classification methods in predicting hypertension Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study Acute renal failure after endovascular vs open repair of abdominal aortic aneurysm Artificial neural networks: opening the black box. Predisposing factors for delirium in the surgical intensive care unit Neighborhood socioeconomic conditions and depression: a systematic review and meta-analysis RETRACTED: The impact of anesthesiologists on coronary artery bypass graft surgery outcomes Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study Risk factors and prediction of postoperative delirium in elderly hip-surgery patients: implementation and validation of a medical risk factor model. Publication of surgeon specific outcome data: a review of implementation, controversies and the potential impact on surgical training. Comparison of the performance of log-logistic regression and artificial neural networks for predicting breast cancer relapse. Intersection of Race/Ethnicity and Socioeconomic Status in Mortality After Breast Cancer Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications Validation of the delirium observation screening scale in a hospitalized older population Delirium in the intensive care setting: A reevaluation of the validity of the CAM-ICU and ICDSC versus the DSM-IV-TR in determining a diagnosis of delirium as part of the daily clinical routine. Insurance status is associated with treatment allocation and outcomes after subarachnoid hemorrhage Support vector machines versus logistic regression: improving prospective performance in clinical decision-making Risk Factors for Postoperative Delirium in Abdominal Surgery: A Proposal of a Postoperative Delirium Risk Score in Abdominal Surgery. Association of pre-operative medication use with post-operative delirium in surgical oncology patients receiving comprehensive geriatric assessment The spatial clustering of obesity: does the built environment matter? Pre- and Intraoperative Predictors of Delirium after Open Abdominal Aortic Aneurysm Repair Early detection and prevention of delirium in older patients with cancer
author name string	wds:Q58586002-2D22B217-FBB6-4E98-BD4F-EEABBD7CC7CE wds:Q58586002-390F9B0C-FA5F-428B-A7BC-5137D5A7CA43 wds:Q58586002-7E0AEFCD-AD75-4763-89FF-308499EB4582
author name string	Anis Davoudi Alberto C Bursian Tazcan Ozrazgat-Baslanti
rdfs:label	Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data (en) Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data (nl)
skos:prefLabel	Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data (en) Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data (nl)
name	Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data (en) Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data (nl)
author	wds:Q58586002-74477F02-5911-4796-A78F-EC187DA69518 wds:Q58586002-7C1908A2-3EB2-4784-8E7C-331D77A24246 wds:Q58586002-A5995BE1-E13A-41CC-BF56-CE51FD712945
author	Azra Bihorac Parisa Rashidi Ashkan Ebadi
title	wds:Q58586002-03D9D7FB-12AE-4D52-9AC5-DC8C6C3C0A20
title	Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data (en)
page(s)	wds:Q58586002-2840AA31-9F66-4000-9DA6-FAC3BBBBFE84

Faceted Search & Find service v1.16.117 as of May 05 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3239 as of May 5 2024, on Linux (x86_64-centos_6-linux-gnu), Single-Server Edition (378 GB total memory, 180 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software