检索结果-内蒙古大学图书馆

Identifying prescription patterns with a topic model of diseases and medications

JOURNAL OF BIOMEDICAL INFORMATICS 2017年 75卷 35-47页

作者： Park, Sungrae Choi, Doosup Kim, Minki Cha, Wonchul Kim, Chuhyun Moon, Il-Chul Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon South Korea Korea Adv Inst Sci & Technol Coll Business Seoul South Korea Samsung Med Ctr Dept Emergency Med Seoul South Korea Inje Univ Dept Emergency Med Coll Med Seoul South Korea SeoulPaik Hosp Seoul South Korea

Wide variance exists among individuals and institutions for treating patients with medicine. This paper analyzes prescription patterns using a topic model with more than four million prescriptions. Specifically, we propose the disease-medicine pattern model (DMPM) to extract patterns from a large collection of insurance data by considering disease codes joined with prescribed medicines. We analyzed insurance prescription data from 2011 with DMPM and found prescription patterns that could not be identified by traditional simple disease classification, such as the International Classification of Diseases (ICD). We analyzed the identified prescription patterns from multiple aspects. First, we found that our model better explain unseen prescriptions than other probabilistic models. Second, we analyzed the similarities of the extracted patterns to identify their characteristics. Third, we compared the identified patterns from DMPM to the known disease categorization, ICD. This comparison showed what additional information can be provided by the data-oriented bottom-up patterns in contrast to the knowledge-based top down categorization. The comparison results showed that the bottom-up categorization allowed for the identification of (1) diverse treatment options for the same disease symptoms, and (2) diverse disease cases sharing the same prescription options. Additionally, the extracted bottom-up patterns revealed treatment differences based on basic patient information better than the top-down categorization. We conclude that this data-oriented analysis will be an effective alternative method for analyzing the complex interwoven disease-prescription relationship. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Topic modeling probabilistic graphical models Medical information

来源：评论

学校读者我要写书评

暂无评论

A Bayesian network model for surface roughness prediction in the machining process

引用

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2008年第12期39卷 1181-1192页

作者： Correa, M. Bielza, C. Ramirez, M. de J. Alique, J. R. CSIC Dept Informat Ind Inst Automat Ind Madrid Spain Univ Politecn Madrid Dept Inteligencia Artificial Madrid Spain ITESM Dept Mecatron & Automatiz Monterrey Mexico

The literature reports many scientific works on the use of artificial intelligence techniques such as neural networks or fuzzy logic to predict surface roughness. This article aims at introducing Bayesian network-based classifiers to predict surface roughness (Ra) in high-speed machining. These models are appropriate as prediction techniques because the non-linearity of the machining process demands robust and reliable algorithms to deal with all the invisible trends present when a work piece is machining. The experimental test obtained from a high-speed milling contouring process analysed the indicator of goodness using the Naive Bayes and the Tree-Augmented Network algorithms. Up to 81.2% accuracy was achieved in the Ra classification results. Therefore, we envisage that Bayesian network-based classifiers may become a powerful and flexible tool in high-speed machining.

关键词： Bayesian networks supervised classification probabilistic graphical models surface roughness high-speed milling

来源：评论

学校读者我要写书评

暂无评论

引用

IEEE TRANSACTIONS ON NANOTECHNOLOGY 2015年第6期14卷 980-991页

作者： Khasanvis, Santosh Li, Mingyu Rahman, Mostafizur Salehi-Fashami, Mohammad Biswas, Ayan K. Atulasimha, Jayasimha Bandyopadhyay, Supriyo Moritz, Csaba Andras Univ Massachusetts Amherst MA 01002 USA Virginia Commonwealth Univ Richmond VA 23284 USA

probabilistic graphical models are powerful mathematical formalisms for machine learning and reasoning under uncertainty that are widely used for cognitive computing. However, they cannot be employed efficiently for large problems (with variables in the order of 100K or larger) on conventional systems, due to inefficiencies resulting from layers of abstraction and separation of logic and memory in CMOS implementations. In this paper, we present a magnetoelectric probabilistic technology framework for implementing probabilistic reasoning functions. The technology leverages straintronic magneto-tunneling junction (S-MTJ) devices in a novel mixed-signal circuit framework for direct computations on probabilities while enabling in-memory computations with persistence. Initial evaluations of the Bayesian likelihood estimation operation occurring during Bayesian Network inference indicate up to 127 x lower area, 214 x lower active power, and 70 x lower latency compared to an equivalent 45-nm CMOS Boolean implementation.

关键词： probabilistic graphical models Bayesian networks non-Boolean computing mixed-signal nanoscale memory-in-computing

来源：评论

学校读者我要写书评

暂无评论

Efficient Attack Graph Analysis through Approximate Inference

引用

ACM TRANSACTIONS ON PRIVACY AND SECURITY 2017年第3期20卷 10.1-10.1页

作者： Munoz-Gonzalez, Luis Sgandurra, Daniele Paudice, Andrea Lupu, Emil C. Imperial Coll London Dept Comp 180 Queens Gate London SW7 2AZ England Royal Holloway Univ London Informat Secur Grp Egham TW20 0EX Surrey England

Attack graphs provide compact representations of the attack paths an attacker can follow to compromise network resources from the analysis of network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk of compromise to the system's components given their vulnerabilities and interconnections and accounts for multi-step attacks spreading through the system. While static analysis considers the risk posture at rest, dynamic analysis also accounts for evidence of compromise, for example, from Security Information and Event Management software or forensic investigation. However, in this context, exact Bayesian inference techniques do not scale well. In this article, we show how Loopy Belief Propagation-an approximate inference technique-can be applied to attack graphs and that it scales linearly in the number of nodes for both static and dynamic analysis, making such analyses viable for larger networks. We experiment with different topologies and network clustering on synthetic Bayesian attack graphs with thousands of nodes to show that the algorithm's accuracy is acceptable and that it converges to a stable solution. We compare sequential and parallel versions of Loopy Belief Propagation with exact inference techniques for both static and dynamic analysis, showing the advantages and gains of approximate inference techniques when scaling to larger attack graphs.

关键词： Bayesian networks probabilistic graphical models approximate inference

来源：评论

学校读者我要写书评

暂无评论

A Bayesian network for combining descriptors: application to symbol recognition

引用

INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2010年第1期13卷 65-75页

作者： Barrat, Sabine Tabbone, Salvatore Univ Nancy 2 LORIA UMR 7503 F-54506 Vandoeuvre Les Nancy France

In this paper, we propose a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor. This approach is based on a probabilistic graphical model. This model also enables to handle both discrete and continuous-valued variables. In fact, in order to improve the recognition rate, we have combined two kinds of features: discrete features (corresponding to shape measures) and continuous features (corresponding to shape descriptors). In order to solve the dimensionality problem due to the large dimension of visual features, we have adapted a variable selection method. Experimental results, obtained in a supervised learning context, on noisy and occluded symbols, show the feasibility of the approach.

关键词： Symbol recognition Descriptor combination Variable selection probabilistic graphical models Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Improving Bayesian Network Structure Learning in the Presence of Measurement Error

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-28页

作者： Liu, Yang Constantinou, Anthony C. Guo, Zhigao Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Alan Turing Inst London NW1 2DB England

Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables. However, this assumption does not hold in the presence of measurement error, which can lead to spurious edges. This is one of the reasons why the synthetic performance of these algorithms often overestimates real-world performance. This paper describes a heuristic algorithm that can be added as an additional learning phase at the end of any structure learning algorithm, and serves as a correction learning phase that removes potential false positive edges. The results show that the proposed correction algorithm successfully im-proves the graphical score of five well-established structure learning algorithms spanning different classes of learning in the presence of measurement error.

关键词： causal discovery data noise directed acyclic graph measurement error probabilistic graphical models

来源：评论

学校读者我要写书评

暂无评论

Understanding disease processes by partitioned dynamic Bayesian networks

引用

JOURNAL OF BIOMEDICAL INFORMATICS 2016年 61卷 283-297页

作者： Bueno, Marcos L. P. Hommersom, Arjen Lucas, Peter J. F. Lappenschaar, Martijn Janzing, Joost G. E. Radboud Univ Nijmegen Inst Comp & Informat Sci NL-6525 ED Nijmegen Netherlands Leiden Univ Leiden Inst Adv Comp Sci NL-2300 RA Leiden Netherlands Open Univ Fac Management Sci & Technol Heerlen Netherlands Radboud Univ Nijmegen Dept Psychiat Med Ctr NL-6525 ED Nijmegen Netherlands

For many clinical problems in patients the underlying pathophysiological process changes in the course of time as a result of medical interventions. In model building for such problems, the typical scarcity of data in a clinical setting has been often compensated by utilizing time homogeneous models, such as dynamic Bayesian networks. As a consequence, the specificities of the underlying process are lost in the obtained models. In the current work, we propose the new concept of partitioned dynamic Bayesian networks to capture distribution regime changes, i.e. time non-homogeneity, benefiting from an intuitive and compact representation with the solid theoretical foundation of Bayesian network models. In order to balance specificity and simplicity in real-world scenarios, we propose a heuristic algorithm to search and learn these non-homogeneous models taking into account a preference for less complex models. An extensive set of experiments were ran, in which simulating experiments show that the heuristic algorithm was capable of constructing well-suited solutions, in terms of goodness of fit and statistical distance to the original distributions, in consonance with the underlying processes that generated data, whether it was homogeneous or non-homogeneous. Finally, a study case on psychotic depression was conducted using non-homogeneous models learned by the heuristic, leading to insightful answers for clinically relevant questions concerning the dynamics of this mental disorder. (C) 2016 Elsevier Inc. All rights reserved.

关键词： probabilistic graphical models Dynamic Bayesian networks Non-homogeneous stochastic processes Heuristic algorithm Multivariate time series Psychotic depression

来源：评论

学校读者我要写书评

暂无评论

Bayesian networks for interpretable machine learning and optimization

引用

NEUROCOMPUTING 2021年 456卷 648-665页

作者： Mihaljevic, Bojan Bielza, Concha Larranaga, Pedro Univ Autonoma Madrid Dept Matemat Madrid Spain Univ Politecn Madrid Madrid Spain

As artificial intelligence is being increasingly used for high-stakes applications, it is becoming more and more important that the models used be interpretable. Bayesian networks offer a paradigm for inter-pretable artificial intelligence that is based on probability theory. They provide a semantics that enables a compact, declarative representation of a joint probability distribution over the variables of a domain by leveraging the conditional independencies among them. The representation consists of a directed acyclic graph that encodes the conditional independencies among the variables and a set of parameters that encodes conditional distributions. This representation has provided a basis for the development of algo-rithms for probabilistic reasoning (inference) and for learning probability distributions from data. Bayesian networks are used for a wide range of tasks in machine learning, including clustering, super -vised classification, multi-dimensional supervised classification, anomaly detection, and temporal mod-eling. They also provide a basis for estimation of distribution algorithms, a class of evolutionary algorithms for heuristic optimization. We illustrate the use of Bayesian networks for interpretable machine learning and optimization by presenting applications in neuroscience, the industry, and bioin-formatics, covering a wide range of machine learning and optimization tasks. (c) 2021 Published by Elsevier B.V.

关键词： Interpretability Explainable machine learning probabilistic graphical models

来源：评论

学校读者我要写书评

暂无评论

The Libra Toolkit for probabilistic models

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2015年第1期16卷 2459-2463页

作者： Lowd, Daniel Rooshenas, Amirmohammad Univ Oregon Dept Comp & Informat Sci Eugene OR 97403 USA

The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, and sum-product networks. Compared to other toolkits, Libra places a greater emphasis on learning the structure of tractable models in which exact inference is efficient. It also includes a variety of algorithms for learning graphical models in which inference is potentially intractable, and for performing exact and approximate inference. Libra is released under a 2-clause BSD license to encourage broad use in academia and industry.

关键词： probabilistic graphical models structure learning inference

来源：评论

学校读者我要写书评

暂无评论

libDAI: A Free and Open Source C plus plus Library for Discrete Approximate Inference in graphical models

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2010年第8期11卷 2169-2173页

作者： Mooij, Joris M. Max Planck Inst Biol Cybernet Dept Empir Inference D-72076 Tubingen Germany

This paper describes the software package libDAI, a free & open source C++ library that provides implementations of various exact and approximate inference methods for graphical models with discrete-valued variables. libDAI supports directed graphical models (Bayesian networks) as well as undirected ones (Markov random fields and factor graphs). It offers various approximations of the partition sum, marginal probability distributions and maximum probability states. Parameter learning is also supported. A feature comparison with other open source software packages for approximate inference is given. libDAI is licensed under the GPL v2+ license and is available at http://***.

关键词： probabilistic graphical models approximate inference open source software factor graphs Markov random fields Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：