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Inference in the Promedas Medical Expert System
[chapter]
Lecture Notes in Computer Science
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We show that Belief Propagation (BP) can be successfully applied as approximate inference algorithm in the Promedas network. ...
In the current paper, the Promedas model for internal medicine, developed by our team, is introduced. ...
Conclusions In this paper we have shown that BP is an attractive alternative for exact inference for complex medical diagnosis inference tasks. ...
doi:10.1007/978-3-540-73599-1_61
fatcat:nxcezipv2naojhvtt3jqnjsezy
Approximate inference for medical diagnosis
1999
Pattern Recognition Letters
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This has obstructed the development of a useful system for in ternal medicine. ...
in medical practice. ...
Acknowled g ements This research is supported by the Technology Foundation STW, applied science division of N\VO and the technology programme of the Ministry of Economic Affairs ...
doi:10.1016/s0167-8655(99)00090-2
fatcat:nagznp23bzfbthzwnx4m4ucme4
Bayesian Networks for Expert Systems: Theory and Practical Applications
[chapter]
2010
Studies in Computational Intelligence
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The first network is applied in a system for medical diagnostic decision support. A distinguishing feature of this network is the large amount of variables in the model. ...
The distinguishing feature in this application is that Bayesian networks are generated and computed on-the-fly based on case information. ...
The research for the Promedas project has been supported by the Dutch Technology Foundation STW, applied science division of NWO and the Technology Program of the Ministry of Economic Affairs. ...
doi:10.1007/978-3-642-11688-9_20
fatcat:yeymzcog4rdwhjpk3mpxdls5d4
Data mining for improved cardiac care
2006
SIGKDD Explorations
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Further, due to the extremely poor quality of data in medical patient records, most of today's healthcare IT systems cannot provide significant support to improve the quality of CVD care (particularly ...
There are two principal factors that enable REMIND to overcome the barriers associated with inference from medical records. ...
., Arun Goel, Geoff Towell, PhD, Prasad Aloni, Michael Greenberg, Romer Rosales, PhD, Balaji Krishnapuram, PhD, Harald Steck, PhD, Abhinay Pandya, the entire SISL team and Narasimha Murthy. ...
doi:10.1145/1147234.1147236
fatcat:e6wxljtdfnhpdkzksq4bvl4en4
Approximation by Quantization
[article]
2012
arXiv
pre-print
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Inference in graphical models consists of repeatedly multiplying and summing out potentials. ...
It is generally intractable because the derived potentials obtained in this way can be exponentially large. ...
The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ARO, DARPA ...
arXiv:1202.3723v1
fatcat:hu63fqm7yfcojdd2xf66nx3d6u
Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Computing the partition function of a graphical model is a fundamental task in probabilistic inference. ...
Our adaptive interleaving policy can automatically balance the computational effort between these two schemes in an instance-dependent way, which provides our framework with the strengths of both schemes ...
Acknowledgements We thank all the reviewers for their helpful feedback. This work is sponsored in part by NSF grants IIS-1526842 and IIS-1254071, the U.S. ...
doi:10.1609/aaai.v33i01.33017900
fatcat:s3araatx4bh4bnrl3iazccxg4q
A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment
2014
Computers in Biology and Medicine
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The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. ...
The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer's Disease and Related Disorders (at the Institute of Psychiatry ...
Acknowledgments We thank the Center of the Consortium to Establish a Registry for Alzheimer's Disease for kindly providing the patients' cases set used in this study. ...
doi:10.1016/j.compbiomed.2014.04.010
pmid:24946259
fatcat:ozm3w3kvc5a5bo4vkzmf3dzi6q
Anytime Anyspace AND/OR Best-First Search for Bounding Marginal MAP
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Marginal MAP is a key task in Bayesian inference and decision-making. ...
It is known to be very difficult in general, particularly because the evaluation of each MAP assignment requires solving an internal summation problem. ...
This work is sponsored in part by NSF grants IIS-1526842, IIS-1254071, and by the United States Air Force under Contract No. FA8750-14-C-0011 and FA9453-16-C-0508. ...
doi:10.1609/aaai.v32i1.12123
fatcat:muimldw64zbfxh4lxotz2juz6q
A Hybrid Health Journey Recommender System Using Electronic Medical Records
2018
ACM Conference on Recommender Systems
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) data for learning the structure of the interwoven health graph (conditions, medications, procedures, and more). ...
We present a recommender system aimed at improving the healthcare experience of consumers. ...
One of the well known existing application is Promedas [13] , a medical patient-specific clinical diagnostic decision support system, that uses a probabilistic graphical model built with the help of medical ...
dblp:conf/recsys/JamshidiTMJPCK18
fatcat:cjbtz4imnfftpbb47bxpooktd4
Anytime Best+Depth-First Search for Bounding Marginal MAP
2017
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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We introduce new anytime search algorithms that combine best-first with depth-first search into hybrid schemes for Marginal MAP inference in graphical models. ...
The main goal is to facilitate the generation of upper bounds (via the best-first part) alongside the lower bounds of solutions (via the depth-first part) in an anytime fashion. ...
Acknowledgments This work was supported in part by NSF grants IIS-1526842 and IIS-1254071, and by the US Air Force under Contract No. FA8750-14-C-0011 under the DARPA PPAML program. ...
doi:10.1609/aaai.v31i1.11055
fatcat:qpkv3iyisjhsppgxoraczmnck4
A Novel Knowledge Base Decision Support System Model for Breast Cancer Treatment
2010
Sri Lanka Journal of Bio-Medical Informatics
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Unavailability or delay in receiving this information leads to medical error, unnecessary complications and suffering to the patient. ...
This paper focuses on the design, development and implementation of a Novel Knowledge Based Decision Support System model in breast cancer treatment for improving the accessibility of domain specific clinical ...
Inference engine of the system will act as an intermediary between user and the system. It will accept and forward the request of user in terms of keyword to the system for the required search. ...
doi:10.4038/sljbmi.v1i2.1609
fatcat:qcsmvbjownhtfcvnct5vfnp42m
Stochastic Anytime Search for Bounding Marginal MAP
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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The Marginal MAP inference task is known to be extremely hard particularly because the evaluation of each complete MAP assignment involves an exact likelihood computation (a combinatorial sum). ...
In this paper, we develop new search-based bounding schemes for Marginal MAP that produce anytime upper and lower bounds without performing exact likelihood computations. ...
Acknowledgments This work was supported in part by NSF grants IIS-1526842 and IIS-1254071, and by contracts FA8750-14-C-0011 and W911NF-18-C-0015 under the DARPA PPAML and World Modelers programs. ...
doi:10.24963/ijcai.2018/704
dblp:conf/ijcai/0002DI18
fatcat:xnx47pkymrg37fqbktpqx34yie
THE CLUSTER VARIATION METHOD FOR APPROXIMATE REASONING IN MEDICAL DIAGNOSIS
2002
Modelling Biomedical Signals
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In this paper, we discuss the rule based and probabilistic approaches to computer aided medical diagnosis. ...
When the method converges, it gives close to optimal results. 1 ...
Acknowledglllents This research was supported in part by the Dutch Technology Foundation (STW). ...
doi:10.1142/9789812778055_0001
fatcat:y7bmw2cy2zeh7pdcv4hlsxcdzm
Finite-sample Bounds for Marginal MAP
2018
Conference on Uncertainty in Artificial Intelligence
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Marginal MAP is a key task in Bayesian inference and decision-making, and known to be very challenging in general. ...
., 2017b], a recent advance in bounding the partition function, to provide finite-sample bounds for the surrogate task. ...
This work is sponsored in part by NSF grants IIS-1526842 and IIS-1254071, the U.S. Air Force (Contract FA9453-16-C-0508), and DARPA (Contract W911NF-18-C-0015). ...
dblp:conf/uai/LouDI18
fatcat:wkgsm76jendgnnj5iztohivuuq
Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes
2015
Artificial Intelligence in Medicine
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The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998. ...
Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific ...
Hunter, 1987 A representation of time for medical expert systems 34 1.3 [23] ...
doi:10.1016/j.artmed.2015.07.003
pmid:26265491
fatcat:bnzr2hwtifbd7dckguz5mgjhmq
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