NDLI: Do Anesthesiologists Know What They Are Doing? Mining a Surgical Time-Series Database to Correlate Expert Assessment with Outcomes

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Do Anesthesiologists Know What They Are Doing? Mining a Surgical Time-Series Database to Correlate Expert Assessment with Outcomes

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Do Anesthesiologists Know What They Are Doing? Mining a Surgical Time-Series Database to Correlate Expert Assessment with Outcomes

Content Provider	ACM Digital Library
Author	Frenzel md, John C. Jermaine, Christopher M. Ruiz md, Joseph R. Myers, Risa B.
Copyright Year	2016
Abstract	Anesthesiologists are taught to carefully manage patient vital signs during surgery. Unfortunately, there is little empirical evidence that vital sign management, as currently practiced, is correlated with patient outcomes. We seek to validate or repudiate current clinical practice and determine whether or not clinician evaluation of surgical vital signs correlate with outcomes. Using a database of over 90,000 cases, we attempt to determine whether those cases that anesthesiologists would subjectively decide are “low quality” are more likely to result in negative outcomes. The problem reduces to one of multi-dimensional time-series classification. Our approach is to have a set of expert anesthesiologists independently label a small number of training cases, from which we build classifiers and label all 90,000 cases. We then use the labeling to search for correlation with outcomes and compare the prevalence of important 30-day outcomes between providers. To mimic the providers’ quality labels, we consider several standard classification methods, such as dynamic time warping in conjunction with a kNN classifier, as well as complexity invariant distance, and a regression based upon the feature extraction methods outlined by Mao et al. 2012 (using features such as time-series mean, standard deviation, skew, etc.). We also propose a new feature selection mechanism that learns a hidden Markov model to segment the time series; the fraction of time that each series spends in each state is used to label the series using a regression-based classifier. In the end, we obtain strong, empirical evidence that current best practice is correlated with reduced negative patient outcomes. We also learn that all of the experts were able to significantly separate cases by outcome, with higher prevalence of negative 30-day outcomes in the cases labeled as “low quality” for almost all of the outcomes investigated.
Starting Page	1
Ending Page	27
Page Count	27
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2822897
Volume Number	10
Issue Number	3
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-02-09
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Vital signs Hidden Markov model (HMM) Ordinal regression
Content Type	Text
Resource Type	Article
Subject	Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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