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Exploring complaints by female and male patients at Swedish hospitals using a probabilistic graphical model

SCANDINAVIAN JOURNAL OF CARING SCIENCES 2018年第3期32卷 1148-1156页

作者： Eriksson, Erik Masao Raharjo, Hendry Gustavsson, Susanne Chalmers Univ Technol Ctr Healthcare Improvement Dept Technol Management & Econ SE-41296 Gothenburg Sweden Skaraborg Hosp Skovde Sweden

BackgroundPatients' complaints have been highlighted as important for constructively improving healthcare services. In so doing, it may be important to identify disparities in experiences based on patients' demographics, such as sex. AimTo explore hospital recorded complaints addressing potential sex differences and whether complaints were reported by the patient or a relative. MethodsQuantitative study of all 835 closed patient complaints during 2013 at three mid-sized hospitals in Sweden. The complaints were categorisation based on perceived quality theory and analysed using a probabilistic graphical model. The findings were validated through qualitative interviews. FindingsFemale patients were more likely than male patients to report dissatisfaction with interpersonal issues, whereas male patients were more likely to report dissatisfaction with administration. If a complaint from a male patient had been reported by a relative, the matter was more likely to be interpersonal. Improvement suggestions were predominantly reported by staff. However, patients and relatives proved more likely than staff to report improvement suggestions when dissatisfied with interpersonal matters. ConclusionUsing a Bayesian network, this article suggests that complaints in health care should be more holistically understood and the factors should be viewed as interconnected. This article addresses complaints as an important source of identifying not only perceived healthcare deficiencies and sex disparities, but also improvement suggestions.

关键词： probabilistic graphical model perceived quality patient satisfaction complaints sex gender quality improvement

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API Misuse Detection via probabilistic graphical model 2024

API Misuse Detection via Probabilistic Graphical Model

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33rd ACM SIGSOFT International Conference on Software Testing and Analysis (ISSTA)

作者： Ma, Yunlong Tian, Wentong Gao, Xiang Sun, Hailong Li, Li Beihang Univ State Key Lab Complex & Crit Software Environm CC Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9798400706127

API misuses can cause a range of issues in software development, including program crashes, bugs, and vulnerabilities. Different approaches have been developed to automatically detect API misuses by checking the program against usage rules extracted from extensive codebase or API documents. However, these mined rules may not be precise or complete, leading to high false positive/negative rates. In this paper, we propose a novel solution to this problem by representing the mined API usage rules as a probabilistic graphical model, where each rule's probability value represents its trustworthiness of being correct. Our approach automatically constructs probabilistic usage rules by mining codebase and documents, and aggregating knowledge from different sources. Here, the usage rules obtained from the codebase initialize the probabilistic model, while the knowledge from the documents serves as a supplement for adjusting and complementing the probabilities accordingly. We evaluate our approach on the MuBench benchmark. Experimental results show that our approach achieves 42.0% precision and 54.5% recall, significantly outperforming state-of-the-art approaches.

关键词： API misuse detection Mining Software Repository Document Mining probabilistic graphical model

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Graph Embedding Through probabilistic graphical model Applied to Symbolic Graphs 8th

Graph Embedding Through Probabilistic Graphical Model Applie...

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8th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)

作者： Jarraya, Hana Ramos Terrades, Oriol Llados, Josep Univ Autonoma Barcelona Comp Vis Ctr CVC Campus UABEdifici O S-N E-08193 Barcelona Spain

ISBN: (纸本)9783319588384;9783319588377

We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a probabilistic graphical model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.

关键词： Attributed Graph probabilistic graphical model Graph Embedding Structured Support Vector Machines

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Lessons learned on the implementation of probabilistic graphical model-based digital twins: A space habitat study *

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JOURNAL OF SPACE SAFETY ENGINEERING 2023年第2期10卷 172-181页

作者： Gratius, Nicolas Hou, Yu Berges, Mario Akinci, Burcu Carnegie Mellon Univ Dept Civil & Environm Engn 5000 Forbes Ave Pittsburgh PA 15213 USA

Habitats for future human spaceflights will require more resilient environmental control and life support systems (ECLSS). To that end, it is important to facilitate decision making in case of unexpected failure by quantifying the uncertain and dynamic nature of the physical phenomena involved. Combining probabilistic and deterministic models is a particularly promising approach to address this issue. In particular, probabilistic graphical model (PGM) based digital twins are relevant as they embed random variables evolving overtime. Previous research used this modeling method for several applications such as monitoring structural health or manufacturing processes. We envision that the space exploration sector can also benefit from this approach by using the insight gained on specific sub-systems. In this study, we propose lessons learned on the implementation process of PGM-based Digital Twin to quantify uncertainties for temperature prognosis in ECLSS. These findings are introduced as a step-by-step guideline which result in developing a probabilistic model applicable to space habitats. We focused on directed acyclic graphs as this type of PGM can integrate expert's knowledge with data which has been proven to enhance accuracy. A literature review was conducted to identify the state-of-the-art practices and the proposed lessons learned were derived from the study of a physical infrastructure meant to predict the behavior of a space habitat. A temperature control failure scenario was considered, and the Digital Twin estimated the time available before the temperature would become critical. Experiments were conducted on three office rooms to simulate the behavior of an ECLSS. The model was trained offline using historical sensor data and performed inference online by computing the conditional probability of a multivariate normal density. We found that a successful implementation process requires to iteratively go through four stages: outline, design, calibrate and ev

关键词： probabilistic graphical model ECLSS Digital twin Space habitat

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A Hybrid Recurrent Neural Network/Dynamic probabilistic graphical model Predictor of the Disulfide Bonding State of Cysteines from the Primary Structure of Proteins 7th

A Hybrid Recurrent Neural Network/Dynamic Probabilistic Grap...

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7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR)

作者： Bongini, Marco Laveglia, Vincenzo Trentin, Edmondo Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy

ISBN: (纸本)9783319461823;9783319461816

Cysteines in a protein have a tendency to form mutual disulfide bonds. This affects the secondary and tertiary structure of the protein. Therefore, automatic prediction of the bonding state of cysteines from the primary structure of proteins has long been a relevant task in bioinformatics. The paper investigates the feasibility of a predictor based on a hybrid approach that combines the dynamic encoding capabilities of a recurrent autoencoder with the short-term/long-term dependencies modeling capabilities of a dynamic probabilistic graphical model (a dynamic extension of the hybrid random field). Results obtained using 1797 proteins from the May 2010 version of the Protein Data Bank show an average accuracy of 85% by relying only on the sub-sequences of the residue chains with no additional attributes (like global descriptors, or evolutionary information provided by multiple alignment).

关键词： Hybrid random field probabilistic graphical model Recurrent neural network Recurrent autoencoder Disulfide bond Cysteine bonding state

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A Unified probabilistic graphical model based Approach for the Robust Decoding of Color Structured Light Pattern 4

A Unified Probabilistic Graphical Model based Approach for t...

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4th IEEE International Conference on Information Science and Technology (ICIST)

作者： Yang, Chao Liu, Fang Song, Zhan Chinese Univ Hong Kong Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China China Air Force Logist Coll Dept Aviat Ammunit Xuzhou Jiangsu Peoples R China

ISBN: (纸本)9781479948086

Color coding is an important research topic in spatial encoded structured light sensing (SLS). In this study, we propose a novel graphical model based approach for the color pattern decoding task. For efficient color labeling, the color pattern is firstly decomposed into separate binary pattern images. With the labeled pattern elements, a unified probabilistic graphical framework is constructed to represent the pseudorandom pattern as a clique tree structure. The model contains two parts: the Conditional Random Field (CRF) is used to represent the dependences between these local decisions, and the Bayesian network (BN) is applied for the representation of background colors effect. A colorful target is experimented to demonstrate its feasibility. And the 3D reconstructed models based on the decoding results are also provided to show its robustness.

关键词： probabilistic graphical model structured light sensing pseudorandom array pattern decoding

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A probabilistic graphical model for Learning as Search 7

A Probabilistic Graphical Model for Learning as Search

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7th IEEE Annual Computing and Communication Workshop and Conference (IEEE CCWC)

作者： Sekharan, Chandra N. Loyola Univ Dept Comp Sci Chicago IL 60611 USA

ISBN: (纸本)9781509042289

This work describes ongoing research on web information retrieval using queries which require prolonged browser interactions in exploring, revising, resubmitting, and studying results produced by the search engine. Over the last two decades or so, web search has improved significantly in a lot of areas, but has not made much progress at the difficult end of the online search spectrum - one that is precisely characterized by queries with complex information needs. Such queries, we call learning queries, require extended browser interaction by the user for satisfactory results. The main goal of the research is to address the difficulty of learning queries by lessening browser interactions through the implementation of a probabilistic graphical model. The expected outcome is an improvement in relevance, inspired by automatic discovery of collaborative search structures in conjunction with leveraging current search engine capabilities. To attain the project goal, we design and implement a browser extension that collects post-query browsing details based on browser interactions by users. The post-query browsing details are used to create a database of query-indexed trees for validating the model proposed. The work presented here describes the design and implementation of browser extensions and the project methodology.

关键词： search information retrieval rooted directed tree browser extension probabilistic graphical model

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Inferring hidden structure in mobile network performance data with noisy net promoter scores using a probabilistic graphical model

Inferring hidden structure in mobile network performance dat...

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Joint European Conference on Networks and Communications / 6G Summit (EuCNC/6G Summit)

作者： du Toit, J. Labuschagne, L. J. Stellenbosch Univ Dept Elect & Elect Engn Stellenbosch South Africa Bytefuse Stellenbosch South Africa

ISBN: (纸本)9798350311020

Understanding customer satisfaction in the context of mobile network performance is helpful when designing reliable cellular networks to retain customers and drive customer loyalty. Using ***, we propose a probabilistic graphical model that infers hidden structure in network key performance indicators using noisy customer survey responses. Our model uses real-world net promoter score survey data, network session data consisting of sites visited by respondents, and network performance data from active sessions. The model learns hidden structure in the network performance data that represent good and bad quality of experience. The discovered properties are consistent with industry-recommended signal strength and quality levels for UMTS and LTE standards. Furthermore, our methodology allows us to estimate a daily network performance for each site, which helps to identify problem areas in the network. Due to the subjective nature of survey data, our model also estimates the overall asymmetric noise associated with the surveys.

关键词： Mobile network performance Net promoter score quality of experience factor graph probabilistic graphical model expectation propagation

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Automatic Annotation Extension and Classification of Documents Using a probabilistic graphical model 13

Automatic Annotation Extension and Classification of Documen...

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13th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Bouzaieni, Abdessalem Barrat, Sabine Tabbone, Salvatore Univ Lorraine LORIA XILOPIX Vandoeuvre Les Nancy France Univ Tours Comp Sci Lab Tours France Univ Lorraine LORIA Vandoeuvre Les Nancy France

ISBN: (纸本)9781479918058

With the fast growth of document images, document annotation has become a research area of great interest. Annotation allows to describe the semantic content of documents and facilitates their use and research. However, for a huge number of documents, the manual annotation of each document becomes a tedious task. A solution is to annotate a small part of the documents and to extend it automatically to the whole dataset. In this paper, we propose a model for annotation extension and document classification using a probabilistic graphical model. In this latter, we combine visual and textual characteristics and we show that the integration of the user feedback improves the annotation step.

关键词： document image processing image classification probability automatic document annotation extension automatic document classification document annotation document images probabilistic graphical model textual characteristics user feedback visual characteristics Manganese Manuals Presses user feedback document image processing Image classification ANNOTATIONS probabilistic model manganese document image graphical models press-in auto-marking

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IProWA: A Novel probabilistic graphical model for Crowdsourcing Aggregation

IProWA: A Novel Probabilistic Graphical Model for Crowdsourc...

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IEEE International Conference on Big Data (Big Data)

作者： Wang, Tianqi Xiao, Houping Ma, Fenglong Gao, Jing Univ Buffalo Buffalo NY 14260 USA Georgia State Univ Atlanta GA 30303 USA Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9781728108582

Crowdsourcing has become a popular way to obtain a large volume of labeled data due to its low cost and high efficiency. Usually the crowdsourcing process enables redundancy in the collected labels in order to ensure the correctness of item labels. However, workers on the crowdsourcing platform may make mistakes on some items, leading to inconsistent labels. In this case, it is important to aggregate these noisy labels and obtain the true labels of the items. The correctness of the item label provided by a worker depends on both the worker's ability and the property of the item. However, most of the existing models consider the effect of workers' abilities but ignore that of the item properties. In this paper, we propose a novel crowdsourcing aggregation method (IProWA) which incorporates the modeling of not only worker expertise level but also item property. In particular, items are represented by a K dimensional vector (i.e., item parameter), where K is the number of possible categories and each dimension represents a category. The proposed model transforms the true label estimation into the estimation of item parameters as it connects the true label and the parameters of an item. In worker modeling, it models the different category propensities among different worker groups. Experimental results show that the performance of the proposed model is comparable to that of the state-of-the-art baselines and the learned item parameters can help interpret the property of that item.

关键词： crowdsourcing item parameter estimation probabilistic graphical model

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