Retrospective Higher-Order Markov Processes for User Trails

Wu, Tao; Gleich, David F.

doi:10.1145/3097983.3098127

Cited by 9 publications

(7 citation statements)

References 38 publications

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“…While Markov models are valuable when history is not important or available, they potentially lack accuracy when history matters. 29 Work has been done 29 to develop more complex Markov chain modeling methods that incorporate history.…”

Section: Discussionmentioning

confidence: 99%

Generating Evidence for Chronic Obstructive Pulmonary Disease (COPD) Clinical Guidelines Using EHR Data

Johnson

Bikak

Griffin

et al. 2019

Preprint

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Objectives: The aim of this research was to develop data-driven models using electronic health records (EHRs) to conduct clinical studies for predicting clinical outcomes through probabilistic analysis that considers temporal aspects of clinical data. We assess the efficacy of antibiotics treatment and the optimal time of initiation for in-hospitalized diagnosed with acute exacerbation of COPD (AECOPD) as an application to probabilistic modeling. Materials and Methods: We developed a semi-automatic Markov Chain Monte Carlo (MCMC) modeling and simulation approach that encodes clinical conditions as computable definitions of health states and exact time duration as input for parameter estimations using raw EHR data. We applied the MCMC approach to the MIMIC-III clinical database, where ICD-9 diagnosis codes (491.21, 491.22, and 494.1) were used to identify data for 697 AECOPD patients of which 25.9% were administered antibiotics. Results: The average time to antibiotic administration was 27 hours, and 32% of patients were administered vancomycin as the initial antibiotic. The model simulations showed a 50% decrease in mortality rate as the number of patients administered antibiotics increased. There was an estimated 5.5% mortality rate when antibiotics were initially administrated after 48 hours vs 1.8% when antibiotics were initially administrated between 24 and 48 hours. Discussion: Our findings suggest that there may be a mortality benefit in initiation of antibiotics early in patient with severe respiratory failure in settings of COPD exacerbations warranting an ICU admission. Conclusion: Probabilistic modeling and simulation methods that considers temporal aspects of raw clinical patient data can be used to adequately generate evidence for clinical guidelines.

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Section: Discussionmentioning

confidence: 99%

Generating Evidence for Chronic Obstructive Pulmonary Disease (COPD) Clinical Guidelines Using EHR Data

Johnson

Bikak

Griffin

et al. 2019

Preprint

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“…Other loosely related work that deserves a mention includes vertex-reinforced random walks [395] and the spacey random walk [396], which are at the base of multilinear PageRank, as well as tensor spectral clustering [397], for partitioning higher-order network structures such as triangles, and tensor factorization, for predicting user trails [398], both modeled as higher-order Markov chains. For more details on useful mathematical tools, we also recommend Golub and Van Loan [399] for general matrix computations, Manning et al [77,Ch.8] for understanding lowrank matrix approximations, Dayar [400] for learning about the relation between Markov chains and the Kronecker product, and Wu and Chu [401] for more information on higher-order Markov chains, tensors and the power iteration.…”

Section: Mlprmentioning

confidence: 99%

Graph-based entity-oriented search

Devezas

2021

SIGIR Forum

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Entity-oriented search has revolutionized search engines. In the era of Google Knowledge Graph and Microsoft Satori, users demand an effortless process of search. Whether they express an information need through a keyword query, expecting documents and entities, or through a clicked entity, expecting related entities, there is an inherent need for the combination of corpora and knowledge bases to obtain an answer. Such integration frequently relies on independent signals extracted from inverted indexes, and from quad indexes indirectly accessed through queries to a triplestore. However, relying on two separate representation models inhibits the effective cross-referencing of information, discarding otherwise available relations that could lead to a better ranking. Moreover, different retrieval tasks often demand separate implementations, although the problem is, at its core, the same. With the goal of harnessing all available information to optimize retrieval, we explore joint representation models of documents and entities, while taking a step towards the definition of a more general retrieval approach. Specifically, we propose that graphs should be used to incorporate explicit and implicit information derived from the relations between text found in corpora and entities found in knowledge bases. We also take advantage of this framework to elaborate a general model for entity-oriented search, proposing a universal ranking function for the tasks of ad hoc document retrieval (leveraging entities), ad hoc entity retrieval, and entity list completion. At a conceptual stage, we begin by proposing the graph-of-entity, based on the relations between combinations of term and entity nodes. We introduce the entity weight as the corresponding ranking function, relying on the idea of seed nodes for representing the query, either directly through term nodes, or based on the expansion to adjacent entity nodes. The score is computed based on a series of geodesic distances to the remaining nodes, providing a ranking for the documents (or entities) in the graph. In order to improve on the low scalability of the graph-of-entity, we then redesigned this model in a way that reduced the number of edges in relation to the number of nodes, by relying on the hypergraph data structure. The resulting model, which we called hypergraph-of-entity, is the main contribution of this thesis. The obtained reduction was achieved by replacing binary edges with n -ary relations based on sets of nodes and entities (undirected document hyperedges), sets of entities (undirected hyperedges, either based on cooccurrence or a grouping by semantic subject), and pairs of a set of terms and a set of one entity (directed hyperedges, mapping text to an object). We introduce the random walk score as the corresponding ranking function, relying on the same idea of seed nodes, similar to the entity weight in the graph-of-entity. Scoring based on this function is highly reliant on the structure of the hypergraph, which we call representation-driven retrieval. As such, we explore several extensions of the hypergraph-of-entity, including relations of synonymy, or contextual similarity, as well as different weighting functions per node and hyperedge type. We also propose TF-bins as a discretization for representing term frequency in the hypergraph-of-entity. For the random walk score, we propose and explore several parameters, including length and repeats, with or without seed node expansion, direction, or weights, and with or without a certain degree of node and/or hyperedge fatigue, a concept that we also propose. For evaluation, we took advantage of TREC 2017 OpenSearch track, which relied on an online evaluation process based on the Living Labs API, and we also participated in TREC 2018 Common Core track, which was based on the newly introduced TREC Washington Post Corpus. Our main experiments were supported on the INEX 2009 Wikipedia collection, which proved to be a fundamental test collection for assessing retrieval effectiveness across multiple tasks. At first, our experiments solely focused on ad hoc document retrieval, ensuring that the model performed adequately for a classical task. We then expanded the work to cover all three entity-oriented search tasks. Results supported the viability of a general retrieval model, opening novel challenges in information retrieval, and proposing a new path towards generality in this area.

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“…Thus, approaches employing higher-order Markov processes have a cubic space complexity, and are for this reason inefficient. To cope with this issue, more efficient parameterization approaches, such as the Linear Additive Markov Process (LAMP [28]) and the Retrospective Higher-Order Markov Process (RHOMP [44]), have been proposed. In contrast to the higher-order Markov process, the number of maintained parameters in the LAMP model and the RHOMP model grow linearly, which makes them more suitable for streaming data.…”

Section: The Candidate Change Point (Detection) Approachmentioning

confidence: 99%

“…In the proposed method, we introduce an adaptive estimation for detecting changes, we use CCP heritage of the CCP model to estimate the CCP mean distribution of streaming data. This method can be compared to the linear high order of states based on Markov chain [28,44].…”

Section: Adaptive Estimation Of Data In Streamingmentioning

confidence: 99%

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Applying temporal dependence to detect changes in streaming data

2018

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Detection of changes in streaming data is an important mining task, with a wide range of real-life applications. Numerous algorithms have been proposed to efficiently detect changes in streaming data. However, the limitation of existing algorithms is that they assume that data are generated independently. In particular, temporal dependencies of data in a stream are still not thoroughly studied. Motivated by this, in this work we propose a new efficient method to detect changes in streaming data by exploring the temporal dependencies of data in the stream. As part of this, we introduce a new statistical model called the candidate change point (CCP) model, with which the main idea is to compute the probabilities of finding change points in the stream. The computed probabilities are used to generate a distribution, which is, in turn, used in statistical hypothesis tests to determine the candidate changes. We use the CCP model to develop a new algorithm called Candidate Change Point Detector (CCPD), which detects change points in linear time, and is thus applicable for real-time applications. Our extensive experimental evaluation demonstrates the efficiency and the feasibility of our approach.

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Retrospective Higher-Order Markov Processes for User Trails

Cited by 9 publications

References 38 publications

Generating Evidence for Chronic Obstructive Pulmonary Disease (COPD) Clinical Guidelines Using EHR Data

Generating Evidence for Chronic Obstructive Pulmonary Disease (COPD) Clinical Guidelines Using EHR Data

Graph-based entity-oriented search

Applying temporal dependence to detect changes in streaming data

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