Predicting Sequences of Clinical Events by Using a Personalized Temporal Latent Embedding Model

Esteban, Cristóbal; Schmidt, Danilo; Krompaß, Denis; Tresp, Volker

doi:10.1109/ichi.2015.23

Cited by 35 publications

(31 citation statements)

References 11 publications

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“…After the step of full-text analysis of the 48 articles eleven of these articles finally remained (2.9%) [19,20,[26][27][28][29][30][31][32][33][34] for analysis in MAXQDA by using a coding scheme.…”

Section: Overall Resultsmentioning

confidence: 99%

“…Table 3 shows all types of graphs used in the articles to represent electronic health records of a patient. Most of the remaining articles use a representation of electronic health records in a graph representing an individual patient in a temporal manner (temporal event data mining) [20,27,28,30,32,33]. In contrast to that, causal networking represents the causal context of patient data and was used in two papers for representing patient data [29,30].…”

Section: Graph Propertiesmentioning

confidence: 99%

“…Five papers investigate the storage of patient graphs in some kind, e.g. in [20,27,28,30,32,33] graph databases. In only two papers, the authors are interested in similarity comparisons of the created patient graphs, whereas in nine papers the plain presentation of patient data in a graph plays a central role.…”

Section: Data Sourcesmentioning

confidence: 99%

“…By modelling the clinical evolution of an individual patient with kidney failure Esteban et al wanted to develop the basis for a future clinical decision support system. This graph-based model contains thousands of events like laboratory results, ordered tests and diagnoses [28] and represents a patient in a graph. Hanzlicek et al described MUDR EHR, a multimedia distributed health record for decision support.…”

Section: Goals and Content Of The Articlesmentioning

confidence: 99%

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Graph-Representation of Patient Data: a Systematic Literature Review

et al. 2020

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Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straightforward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis.

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Section: Overall Resultsmentioning

confidence: 99%

Section: Graph Propertiesmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Section: Goals and Content Of The Articlesmentioning

confidence: 99%

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Graph-Representation of Patient Data: a Systematic Literature Review

et al. 2020

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“…3) Embedding Layers: Embedding layers can also be integrated as part of a larger model to transform highdimensional features into a lower-dimensional space. The embedding can consist of a simple linear transformation [81], [82] or as a fully-connected (deep) network [11], [81], [78]. One study projected the input into a higherdimensional space using a convolutional layer [46].…”

Section: Representation Learningmentioning

confidence: 99%

Machine Learning for Clinical Outcome Prediction

Shamout

Zhu

Clifton

2021

IEEE Rev. Biomed. Eng.

119

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Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality and cardiac arrest to acute kidney injury and arrhythmia. In this review article, we summarize the stateof-the-art in related works covering data processing, inference, and model evaluation, in the context of outcome prediction models developed using data extracted from electronic health records. We also discuss limitations of prominent modeling assumptions and highlight opportunities for future research.

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Relational Models

Tresp

Nickel

2014

Encyclopedia of Social Network Analysis and Mining

Self Cite

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We provide a survey on relational models. Relational models describe complete networked domains by taking into account global dependencies in the data. Relational models can lead to more accurate predictions if compared to non-relational machine learning approaches. Relational models typically are based on probabilistic graphical models, e.g., Bayesian networks, Markov networks, or latent variable models. Relational models have applications in social networks analysis, the modeling of knowledge graphs, bioinformatics, recommendation systems, natural language processing, medical decision support, and linked data. arXiv:1609.03145v1 [cs.AI] 11 Sep 2016 7.4 Non-relational Learning in a Relational Domain An common approximation to a relational model is to model unary relations of key entities in a similar way as in a non-relational model as i P ({X R,ei } R | f i )

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Predicting Sequences of Clinical Events by Using a Personalized Temporal Latent Embedding Model

Cited by 35 publications

References 11 publications

Graph-Representation of Patient Data: a Systematic Literature Review

Graph-Representation of Patient Data: a Systematic Literature Review

Machine Learning for Clinical Outcome Prediction

Relational Models

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