About: Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study

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scientific article published on 27 November 2018

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description	wetenschappelijk artikel (nl) наукова стаття, опублікована в листопаді 2018 (uk) im November 2018 veröffentlichter wissenschaftlicher Artikel (de) artículu científicu espublizáu en payares de 2018 (ast) scientific article published on 27 November 2018 (en)
publication date	wds:Q59807851-EBDD7B7C-5219-4764-B12B-2828FAF0B6F7
publication date	2018-11-01 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q59807851-EAAF510B-6C08-47C1-90ED-309F6ABC5468
language of work or name	English
cites work	wds:Q59807851-0505A740-1FA7-4CEE-BB14-E8660F4567AC wds:Q59807851-194732A1-33C7-4344-91E8-7D5EDC2C4487 wds:Q59807851-2570E346-EE82-470A-9328-DF385CE7A27E wds:Q59807851-3FA00022-8EB2-428B-86E0-E83094099E5C wds:Q59807851-4FB2139A-8F5C-4EDD-9283-A13A00431136 wds:Q59807851-6127538F-8373-44CA-9566-3207E658A816 wds:Q59807851-63E06676-11E1-4B72-B299-1B3137A59E7A wds:Q59807851-68F6F7B7-6A93-47CF-8307-16AAF86C5541 wds:Q59807851-6DDEAAF9-EAFD-4B56-96AC-E6832ACCC09C wds:Q59807851-744F2A84-0939-4476-A8CB-BDA632EA5C42 wds:Q59807851-7738C6A3-175F-40A3-9A1A-1AA661E9EF4A wds:Q59807851-889FB516-8B09-4440-A492-81A9D7C74288 wds:Q59807851-971B85D5-ABFA-43F0-9CBA-CEE9D4B747A8 wds:Q59807851-9A3DBA5D-A604-471C-84C2-B4D917ACF5D4 wds:Q59807851-A1BEECEF-D03B-49E1-B311-A2CA70903DAD wds:Q59807851-B827C195-B315-4877-A776-DFC53E595DF3 wds:Q59807851-BB5C5560-4F9D-4700-ADB6-060B0EF262B1 wds:Q59807851-BF9E0817-2CE4-478A-876C-413362807912 wds:Q59807851-C0A90408-B07C-47AD-9670-9254EB409153 wds:Q59807851-D026B2FC-7AB8-412E-BC4B-F718D678BA65 wds:Q59807851-D1B581F7-7FF4-4341-93B9-4A3A7AC6D919 wds:Q59807851-D3025E78-25A7-4C56-8473-52DD2A8EEEDB wds:Q59807851-D5DB8EE0-CAC3-4471-8ADB-146AB7840BF1 wds:Q59807851-DC1DE62E-42AD-42E5-8158-24C1BC7ADC67 wds:Q59807851-E542B71C-D606-46B3-AE9A-73727DCC247A wds:Q59807851-EC65D4A1-9C47-4FBE-9DF7-013EDE596C15 wds:Q59807851-ECF1F2D0-7C2B-4171-95A6-792916CCA6D8 wds:Q59807851-F47DF3B1-F025-4387-ABCE-F5F1520C2F5B
cites work	Caveats for the use of operational electronic health record data in comparative effectiveness research Consequences for healthcare quality and research of the exclusion of records from the Death Master File Comorbidity measures for use with administrative data Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons Regularization Paths for Generalized Linear Models via Coordinate Descent Can routine preoperative data predict adverse outcomes in the elderly? Development and validation of a simple risk model incorporating a chart-derived frailty score Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods Escaping the EHR trap--the future of health IT. Big data in health care: using analytics to identify and manage high-risk and high-cost patients Hospital costs associated with surgical complications: a report from the private-sector National Surgical Quality Improvement Program Using electronic health records for surgical quality improvement in the era of big data Association of hospital participation in a quality reporting program with surgical outcomes and expenditures for Medicare beneficiaries Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement Development and validation of the Surgical Outcome Risk Tool (SORT). The perioperative surgical home: An innovative, patient-centred and cost-effective perioperative care model Evaluation of the performance of the ACS NSQIP surgical risk calculator in gynecologic oncology patients undergoing laparotomy Surgical Risk Preoperative Assessment System (SURPAS): III. Accurate Preoperative Prediction of 8 Adverse Outcomes Using 8 Predictor Variables. Complications in surgical patients Association of hospital participation in a surgical outcomes monitoring program with inpatient complications and mortality. Use of the American College of Surgeons NSQIP Surgical Risk Calculator for Laparoscopic Colectomy: how good is it and how can we improve it? Association of Integrated Care Coordination With Postsurgical Outcomes in High-Risk Older Adults: The Perioperative Optimization of Senior Health (POSH) Initiative. ACS NSQIP Risk Calculator: An Accurate Predictor of Complications in Major Head and Neck Surgery? Risk Prediction With Electronic Health Records: The Importance of Model Validation and Clinical Context The National Surgical Quality Improvement Program risk calculator does not adequately stratify risk for patients with clinical stage I non-small cell lung cancer Postoperative Complications of Laparoscopic Cholecystectomy for Acute Cholecystitis: A Comparison to the ACS-NSQIP Risk Calculator and the Tokyo Guidelines Barriers to Achieving Economies of Scale in Analysis of EHR Data. A Cautionary Tale Leveraging electronic health records for predictive modeling of post-surgical complications. Resolving the Productivity Paradox of Health Information Technology: A Time for Optimism
author name string	wds:Q59807851-1ED92A6C-810C-4D1D-B243-0E33CEC0FD9A wds:Q59807851-819AF32C-ED89-4499-ADA4-3F5E747D7D80 wds:Q59807851-B5DA306F-AECF-4B40-859B-BE27F44ED30D wds:Q59807851-E538656A-4063-4F34-BC1B-9AB01B35C2E7 wds:Q59807851-E554A5A2-DD02-4413-B1F4-0FCBE1458FA0
author name string	Mitchell T Heflin Sandhya A Lagoo-Deenadayalan Sehj Kashyap Mark Sendak Krista Whalen
rdfs:label	Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study (en) Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study (nl)
skos:prefLabel	Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study (en) Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study (nl)
name	Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study (en) Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study (nl)
author	wds:Q59807851-0FD160B9-F9DE-4ACD-86A5-A28C93A5C18C wds:Q59807851-122550C7-D710-4C5B-8CC3-E73315C9C90A wds:Q59807851-531A1F56-E382-47B7-A970-C89A8032BEF4 wds:Q59807851-AA25B883-372E-456B-ADC9-368C340CFCEE wds:Q59807851-CBE84A18-E093-4D1E-A442-AFC15CE31965 wds:Q59807851-F9811400-C206-470F-B753-48126D27C1D4
author	Suresh Balu Kristin M Corey Katherine Heller Shelley R McDonald Madhav Swaminathan Elizabeth Lorenzi
title	wds:Q59807851-76BD6294-0873-49C5-A6D8-3427B00F3A8E
title	Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study (en)
page(s)	wds:Q59807851-A424C796-9A10-4009-8023-F00011CCAF03
page(s)	e1002701
instance of	wds:Q59807851-D28D2E73-514E-4BAB-980F-47DF8BFF05C6
instance of	scholarly article

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