About: Predictive methods for surgery duration

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Predictive methods for surgery duration Goto Sponge NotDistinct Permalink

An Entity of Type : owl:Thing, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)
QRcode icon

http://dbpedia.demo.openlinksw.com/c/2AXG4wguSL

Predictions of surgery duration (SD) are used to schedule planned/elective surgeries so that utilization rate of operating theatres be optimized (maximized subject to policy constraints). An example for a constraint is that a pre-specified tolerance for the percentage of postponed surgeries (due to non-available operating room (OR) or recovery room space) not be exceeded. The tight linkage between SD prediction and surgery scheduling is the reason that most often scientific research related to scheduling methods addresses also SD predictive methods and vice versa. Durations of surgeries are known to have large variability. Therefore, SD predictive methods attempt, on the one hand, to reduce variability (via stratification and covariates, as detailed later), and on the other employ best ava

Attributes	Values
rdfs:label	Predictive methods for surgery duration (en)
rdfs:comment	Predictions of surgery duration (SD) are used to schedule planned/elective surgeries so that utilization rate of operating theatres be optimized (maximized subject to policy constraints). An example for a constraint is that a pre-specified tolerance for the percentage of postponed surgeries (due to non-available operating room (OR) or recovery room space) not be exceeded. The tight linkage between SD prediction and surgery scheduling is the reason that most often scientific research related to scheduling methods addresses also SD predictive methods and vice versa. Durations of surgeries are known to have large variability. Therefore, SD predictive methods attempt, on the one hand, to reduce variability (via stratification and covariates, as detailed later), and on the other employ best ava (en)
dcterms:subject	Prediction Health informatics Surgery Health care management Health Resources and Services Administration Hospitals
Wikipage page ID	67412446 (xsd:integer)
Wikipage revision ID	1105729625 (xsd:integer)
Link from a Wikipage to another Wikipage	Probability distribution fitting Procedure code Prediction Current Procedural Terminology Dependent and independent variables International Classification of Diseases Standard deviation Median Memorylessness Generalized gamma distribution Generalized linear model Quartile Empirical distribution function Gamma distribution Mixture distribution Monte Carlo method Log-normal distribution Machine learning Kernel density estimation Manufacturing engineering Health informatics Surgery Health care management Data mining Linear regression Log-logistic distribution Percentile Health Resources and Services Administration Exponential distribution Normal distribution Histogram Data mining Statistical dispersion Nonlinear regression Random forest Big data Survival analysis Coefficient of variation Jackknife resampling Weibull distribution Artificial intelligence in healthcare Hospitals Scale parameter Knowledge discovery in databases Scheduling (production processes) Multivariate adaptive regression spline Stratified sampling
sameAs	Predictive methods for surgery duration Predictive methods for surgery duration
has abstract	Predictions of surgery duration (SD) are used to schedule planned/elective surgeries so that utilization rate of operating theatres be optimized (maximized subject to policy constraints). An example for a constraint is that a pre-specified tolerance for the percentage of postponed surgeries (due to non-available operating room (OR) or recovery room space) not be exceeded. The tight linkage between SD prediction and surgery scheduling is the reason that most often scientific research related to scheduling methods addresses also SD predictive methods and vice versa. Durations of surgeries are known to have large variability. Therefore, SD predictive methods attempt, on the one hand, to reduce variability (via stratification and covariates, as detailed later), and on the other employ best available methods to produce SD predictions. The more accurate the predictions, the better the scheduling of surgeries (in terms of the required OR utilization optimization). An SD predictive method would ideally deliver a predicted SD statistical distribution (specifying the distribution and estimating its parameters). Once SD distribution is completely specified, various desired types of information could be extracted thereof, for example, the most probable duration (mode), or the probability that SD does not exceed a certain threshold value. In less ambitious circumstance, the predictive method would at least predict some of the basic properties of the distribution, like location and scale parameters (mean, median, mode, standard deviation or coefficient of variation, CV). Certain desired percentiles of the distribution may also be the objective of estimation and prediction. Experts estimates, empirical histograms of the distribution (based on historical computer records), data mining and knowledge discovery techniques often replace the ideal objective of fully specifying SD theoretical distribution. Reducing SD variability prior to prediction (as alluded to earlier) is commonly regarded as part and parcel of SD predictive method. Most probably, SD has, in addition to random variation, also a systematic component, namely, SD distribution may be affected by various related factors (like medical specialty, patient condition or age, professional experience and size of medical team, number of surgeries a surgeon has to perform in a shift, type of anesthetic administered). Accounting for these factors (via stratification or covariates) would diminish SD variability and enhance the accuracy of the predictive method. Incorporating expert estimates (like those of surgeons) in the predictive model may also contribute to diminish the uncertainty of data-based SD prediction. Often, statistically significant covariates (also related to as factors, predictors or explanatory variables) — are first identified (for example, via simple techniques like linear regression and knowledge discovery), and only later more advanced big-data techniques are employed, like Artificial Intelligence and Machine Learning, to produce the final prediction. Literature reviews of studies addressing surgeries scheduling most often also address related SD predictive methods. Here are some examples (latest first). The rest of this entry review various perspectives associated with the process of producing SD predictions — SD statistical distributions, Methods to reduce SD variability (stratification and covariates), Predictive models and methods, and Surgery as a work-process. The latter addresses surgery characterization as a work-process (repetitive, semi-repetitive or memoryless) and its effect on SD distributional shape. (en)
prov:wasDerivedFrom	wikipedia-en:Predictive_methods_for_surgery_duration?oldid=1105729625&ns=0
page length (characters) of wiki page	30459 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Predictive_methods_for_surgery_duration
is Link from a Wikipage to another Wikipage of	Predictive modelling Log-normal distribution Exponential distribution Alex Macario
is foaf:primaryTopic of	wikipedia-en:Predictive_methods_for_surgery_duration

Faceted Search & Find service v1.17_git147 as of Sep 06 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3332 as of Dec 5 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (378 GB total memory, 64 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software