Predicting Latent Variables with Knowledge and Data A Case Study in Trauma Care

Date

2013-08-25

Author

Yet, Barbaros
Perkins, Zane
Tai, Nigel
Fenton, Norman

Metadata

Show full item record

Item Usage Stats

49
views

0
downloads

Making a prediction that is useful for decision makers is not the same as building a model that fits data well. One reason for this is that we often need to pre-dict the true state of a variable that is only indirectly observed, using measurements. Such ‘latent’ variables are not present in the data and often get confused with meas-urements. We present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the pre-diction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that can cause fatality following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed us-ing knowledge as well as data.

URI

http://ceur-ws.org/Vol-1024/paper-06.pdf
https://hdl.handle.net/11511/86468

Conference Name

29th Conference on Uncertainty in Artificial Intelligence (UAI 2013 - Bayesian Applications Workshop), 2013

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Comparison of Forbidden Zone Data Hiding and Quantization Index Modulation Esen, Ersin; Alatan, Abdullah Aydın (2012-01-01) Developing practical data hiding schemes that approach theoretical steganographic capacity limits is still a challenging research area. In this aspect, this problem is approached from a different perspective by proposing a novel method that relies on the newly introduced forbidden zone concept, which is defined as the host signal range, where no alterations are allowed. The proposed method, Forbidden Zone Data Hiding (FZDH), combines the forbidden zone with binning schemes and it is formulated both in gener...
Reducing inconsistencies in intuitionistic 2-way adaptive fuzzy control systems Gurkan, E; Erkmen, Aydan Müşerref; Erkmen, İsmet (2000-08-30) Our objective in this paper is to model and reduce inconsistency in expert knowledge for our proposed 2-way adaptive fuzzy system that makes use of intuitionistic fuzzy sets. Intuitionistic fuzzy sets model an interval valued distribution of information in the adaptive control architecture with the necessity at the lower bound as the degree of membership functions and the possibility at the upper bound as the complement of the degree of nonmembership functions. Uncertainty is modelled as the width of this i...
Implementing real-time data analytics methods for predictive manufacturing in oil and gas industry : from the perspective of industry 4.0 Yeldan, Yiğit; Pamukçu, M. Teoman.; Department of Science and Technology Policy Studies (2019) With the recent developments in statistics and computer science, digitalization has become more important for manufacturing companies. Thanks to the progress made in the area of information technologies, it has become possible for all production systems to communicate with each other by transmitting and receiving data digitally in order to manage the decision-making process in the best manner. Several studies suggest that production processes that are based on full automation will be compulsory for companie...
Modeling, inference and optimization of regulatory networks based on time series data Weber, Gerhard Wilhelm; DEFTERLİ, ÖZLEM; ALPARSLAN GÖK, Sırma Zeynep; Kropat, Erik (2011-05-16) In this survey paper, we present advances achieved during the last years in the development and use of OR, in particular, optimization methods in the new gene-environment and eco-finance networks, based on usually finite data series, with an emphasis on uncertainty in them and in the interactions of the model items. Indeed, our networks represent models in the form of time-continuous and time-discrete dynamics, whose unknown parameters we estimate under constraints on complexity and regularization by variou...
Predicting the Trending Research Topics by Deep Neural Network Based Content Analysis Yukselen, Murat; Mutlu, Alev; Karagöz, Pınar (2022-01-01) Tracking the trends and taking early steps accordingly is important in academia, as well as in other domains such as technology and finance. In this work, we focus on the problem of predicting the trending research topics from a collection of academic papers. Previous efforts model the problem in different ways and mostly apply classical approaches such as correlation analysis and clustering. There are also several recent neural model based solutions, however they rely on feature vectors and additional info...

Citation Formats

B. Yet, Z. Perkins, N. Tai, and N. Fenton, “Predicting Latent Variables with Knowledge and Data A Case Study in Trauma Care,” WASHINGTON, Amerika Birleşik Devletleri, 2013, vol. 1024, Accessed: 00, 2021. [Online]. Available: http://ceur-ws.org/Vol-1024/paper-06.pdf.