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Christopher C. Yang

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17 papers
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17

JBHI Journal 2021 Journal Article

Proximity of Cellular and Physiological Response Failures in Sepsis

  • Ali Jazayeri
  • Muge Capan
  • Julie Ivy
  • Ryan Arnold
  • Christopher C. Yang

Sepsis is a devastating multi-stage health condition with a high mortality rate. Its complexity, prevalence, and dependency of its outcomes on early detection have attracted substantial attention from data science and machine learning communities. Previous studies rely on individual cellular and physiological responses representing organ system failures to predict health outcomes or the onset of different sepsis stages. However, it is known that organ systems’ failures and dynamics are not independent events. In this study, we identify the dependency patterns of significant proximate sepsis-related failures of cellular and physiological responses using data from 12, 223 adult patients hospitalized between July 2013 and December 2015. The results show that proximate failures of cellular and physiological responses create better feature sets for outcome prediction than individual responses. Our findings reveal the few significant proximate failures that play the major roles in predicting patients’ outcomes. This study's results can be simply translated into clinical practices and inform the prediction and improvement of patients’ conditions and outcomes.

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TIST Journal 2015 Journal Article

An Association-Based Unified Framework for Mining Features and Opinion Words

  • Zhen Hai
  • Kuiyu Chang
  • Gao Cong
  • Christopher C. Yang

Mining features and opinion words is essential for fine-grained opinion analysis of customer reviews. It is observed that semantic dependencies naturally exist between features and opinion words, even among features or opinion words themselves. In this article, we employ a corpus statistics association measure to quantify the pairwise word dependencies and propose a generalized association-based unified framework to identify features, including explicit and implicit features, and opinion words from reviews. We first extract explicit features and opinion words via an association-based bootstrapping method (ABOOT). ABOOT starts with a small list of annotated feature seeds and then iteratively recognizes a large number of domain-specific features and opinion words by discovering the corpus statistics association between each pair of words on a given review domain. Two instances of this ABOOT method are evaluated based on two particular association models, likelihood ratio tests (LRTs) and latent semantic analysis (LSA). Next, we introduce a natural extension to identify implicit features by employing the recognized known semantic correlations between features and opinion words. Experimental results illustrate the benefits of the proposed association-based methods for identifying features and opinion words versus benchmark methods.

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TIST Journal 2015 Journal Article

Using Health-Consumer-Contributed Data to Detect Adverse Drug Reactions by Association Mining with Temporal Analysis

  • Haodong Yang
  • Christopher C. Yang

Since adverse drug reactions (ADRs) represent a significant health problem all over the world, ADR detection has become an important research topic in drug safety surveillance. As many potential ADRs cannot be detected though premarketing review, drug safety currently depends heavily on postmarketing surveillance. Particularly, current postmarketing surveillance in the United States primarily relies on the FDA Adverse Event Reporting System (FAERS). However, the effectiveness of such spontaneous reporting systems for ADR detection is not as good as expected because of the extremely high underreporting ratio of ADRs. Moreover, it often takes the FDA years to complete the whole process of collecting reports, investigating cases, and releasing alerts. Given the prosperity of social media, many online health communities are publicly available for health consumers to share and discuss any healthcare experience such as ADRs they are suffering. Such health-consumer-contributed content is timely and informative, but this data source still remains untapped for postmarketing drug safety surveillance. In this study, we propose to use (1) association mining to identify the relations between a drug and an ADR and (2) temporal analysis to detect drug safety signals at the early stage. We collect data from MedHelp and use the FDA's alerts and information of drug labeling revision as the gold standard to evaluate the effectiveness of our approach. The experiment results show that health-related social media is a promising source for ADR detection, and our proposed techniques are effective to identify early ADR signals.

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TIST Journal 2014 Journal Article

Detecting Social Media Hidden Communities Using Dynamic Stochastic Blockmodel with Temporal Dirichlet Process

  • Xuning Tang
  • Christopher C. Yang

Detecting evolving hidden communities within dynamic social networks has attracted significant attention recently due to its broad applications in e-commerce, online social media, security intelligence, public health, and other areas. Many community network detection techniques employ a two-stage approach to identify and detect evolutionary relationships between communities of two adjacent time epochs. These techniques often identify communities with high temporal variation, since the two-stage approach detects communities of each epoch independently without considering the continuity of communities across two time epochs. Other techniques require identification of a predefined number of hidden communities which is not realistic in many applications. To overcome these limitations, we propose the Dynamic Stochastic Blockmodel with Temporal Dirichlet Process, which enables the detection of hidden communities and tracks their evolution simultaneously from a network stream. The number of hidden communities is automatically determined by a temporal Dirichlet process without human intervention. We tested our proposed technique on three different testbeds with results identifying a high performance level when compared to the baseline algorithm.

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IS Journal 2014 Journal Article

Social Intelligence and Technology

  • Christopher C. Yang
  • John Yen
  • Jiming Liu

In this special issue on social intelligence and technology, the guest editors discuss social media's evolution, including societal problems that have arisen or been solved as a result of this technology. They also introduce articles that offer novel solutions in the field.

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TIST Journal 2012 Journal Article

Ranking User Influence in Healthcare Social Media

  • Xuning Tang
  • Christopher C. Yang

Due to the revolutionary development of Web 2.0 technology, individual users have become major contributors of Web content in online social media. In light of the growing activities, how to measure a user’s influence to other users in online social media becomes increasingly important. This research need is urgent especially in the online healthcare community since positive influence can be beneficial while negative influence may cause-negative impact on other users of the same community. In this article, a research framework was proposed to study user influence within the online healthcare community. We proposed a new approach to incorporate users’ reply relationship, conversation content and response immediacy which capture both explicit and implicit interaction between users to identify influential users of online healthcare community. A weighted social network is developed to represent the influence between users. We tested our proposed techniques thoroughly on two medical support forums. Two algorithms UserRank and Weighted in-degree are benchmarked with PageRank and in-degree. Experiment results demonstrated the validity and effectiveness of our proposed approaches.

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IS Journal 2009 Journal Article

AI and Global Science and Technology Assessment

  • Hsinchun Chen
  • Ronald N. Kostoff
  • Chaomei Chen
  • Jian Zhang
  • Michael S. Vogeley
  • Katy Borner
  • Nianli Ma
  • Russell J. Duhon

Addressing the research opportunities we've identified could substantially broaden the spectrum of multilingual text-mining and its practicality for supporting global S&T knowledge management. These opportunities also share a common set of challenges that deserve further attention. For example, competitive intelligence surveillance, which allows organizations to understand their current and potential competitors better, often requires the extraction of names of different organizations, technologies, or products from various S&T documents. When dealing with multilingual documents, adequate cross-lingual entity-resolution mechanisms are essential for effective global S&T analysis. Furthermore, some S&T documents are scientific or technologically oriented, whereas others have a predominantly business orientation. This increases the chance of different documents using different terms inreferring to identical or similar concepts. Establishing cross-domain interoperability is essential, especially in multilingual environments.

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ICRA Conference 1997 Conference Paper

Camera settings for dimensional inspection using displacement and quantization errors

  • Kevin L. Crosby
  • Christopher C. Yang
  • Frank W. Ciarallo
  • Michael M. Marefat

An important aspect of inspection planning involves determining camera poses based on some criterion. We seek to find camera poses where the effects of displacement and quantization errors are minimal. The mean squared error is formulated, including all dependencies, and minimized to determine an optimal camera pose that satisfies the sensor constraints of resolution, focus, field-of-view, and visibility. Dimensional tolerances for line entities are also formulated and exploited to determine the acceptability of a given camera pose for all entities observed.

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ICRA Conference 1997 Conference Paper

Tolerance analysis and synthesis by interval constraint networks

  • Christopher C. Yang
  • Michael M. Marefat
  • Frank W. Ciarallo

This paper proposes interval constraint network and interval propagation techniques for automatic tolerance design. A hierarchical representation is utilized in the interval constraint network. The consistency of a constraint is defined for the purpose of tolerance design. Forward and backward propagation techniques are introduced in the interval constraint network for tolerance analysis and synthesis, respectively. Both a propagation technique for a single constraint and a parallel propagation technique for multiple constraints between two adjacent levels in the network are introduced. Experiments conducted to illustrate the procedures of tolerance analysis and synthesis for the tank problem are described.

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ICRA Conference 1996 Conference Paper

Displacement errors in active visual inspection

  • Frank W. Ciarallo
  • Christopher C. Yang
  • Michael M. Marefat

Displacement error is inherent in automated visual inspection systems. This paper discusses the effect of displacement error of the end-effector on the precision measurement of the dimension of an edge line segment. The position and orientation errors of the end-effector are assumed to be normal distributed. A probabilistic analysis in terms of the these errors is developed for the displacement errors. Given that the nominal orientation and position of the sensor are known, the effect of this error on the dimension measurement is also analyzed. Based on this analysis, we investigate whether a given set of sensor setting parameters in an active system is suitable to obtain a desired accuracy for specific line segment dimensional measurements. In addition, based on this approach, one can determine sensor positions and view directions which meet specific targets for tolerance and accuracy of inspection. Developing these mechanisms is central to achieving effective, economic, and accurate inspection systems.

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ICRA Conference 1994 Conference Paper

Active Visual Inspection Based on CAD Models

  • Christopher C. Yang
  • Michael M. Marefat
  • Rangasami L. Kashyap

This paper is concerned with problems in automated visual inspection of manufactured (particularly machined) components based on their (CAD) design models. In order to achieve the integrated intelligent inspection goals, the authors address several interrelated problems. These problems include: (i) developing hierarchical representation mechanisms to effectively capture the knowledge about geometric entities, their relationships, sensors, and plans, (ii) reasoning mechanisms to determine the different attributes of the different features of an object to be inspected, and the alternative strategies which can be used for inspection of each attribute, (iii) strategies for automated generation of position and viewing angles of the cameras in an active vision system, and for determining the visible entities in each configuration, and (iv) optimization of the constructed plan including minimizing the number of sensor settings and the total distance traveled by an active visual sensor. >

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