A temporal pattern mining approach for classifying electronic health record data

Batal, Iyad; Valizadegan, Hamed; Cooper, Gregory F.; Hauskrecht, Miloš

doi:10.1145/2508037.2508044

Cited by 104 publications

(65 citation statements)

References 35 publications

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“…For example, negative interaction among the drugs pravastatin and paroxetine was found in [13]. We attempted to analyse the data using temporal pattern mining based on Batal et al [2]. Our results are consistent with that study and that of others (e.g.…”

Section: Discussionsupporting

confidence: 87%

“…The other attributes ignore a potentially important aspect of the data: its temporality. Thus, a temporal pattern mining approach based on is used Batal et al [2]. The general idea of this approach is described below.…”

Section: Dataset Description and Preparationmentioning

confidence: 99%

“…An entire explanation of the algorithm is beyond the scope of this paper. An in-depth description of the algorithm including many optimisations is available in [2]. To maximise the likelihood of finding predictive patterns, the algorithm is run on CRC and no CRC cases separately.…”

Section: Discovering Temporal Patterns the Final Row Ofmentioning

confidence: 99%

“…Using this dataset we generate predictive models using random forests [3] (RF), SVM [15] and the simpler CART algorithm [4]. In addition, we use a sophisticated approach (based on [2]) to generate and exploit temporal patterns to see whether this benefits the accuracy of the predictions. Given the variety in the quality of the datasets across studies (which makes it difficult to directly compare results), we compare the outcome with an application of the previously generated models on our dataset.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer

Kop

Hoogendoorn

Moons

et al. 2015

Artificial Intelligence in Medicine

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Abstract. Electronic Medical Records (EMRs) provide a wealth of data that can be used to generate predictive models for diseases. Quite some studies have been performed that use EMRs to generate such models for specific diseases, but most of them are based on more traditional techniques used in medical domain, such as logistic regression. This paper studies the benefit of using advanced data mining techniques for Colorectal Cancer (CRC). CRC is the second most common cancer in the EU and is known to be a disease with very a-specific predictors, making it difficult to generate good predictive models. In addition, the EMR data itself has its own challenges, including the sparsity, the differences in which physicians code the data, the temporal nature of the data, and the imbalance in the data. Results show that state-of-the-art data mining techniques, including temporal data mining, are able to generate better predictive models than currently available in the literature.

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Section: Discussionsupporting

confidence: 87%

Section: Dataset Description and Preparationmentioning

confidence: 99%

Section: Discovering Temporal Patterns the Final Row Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer

Kop

Hoogendoorn

Moons

et al. 2015

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

show abstract

“…There are also some applications of sequence analysis in healthcare for predicting patients who are at risk of developing a disease. For example, Batal, Valizadegan, Cooper, and Hauskrecht (2013) applied this approach to predict patients who are at risk of developing heparin induced thrombocytopenia. Similarly, sequence analysis can be a useful method to find patterns in EMR.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a Sequence of Events in Healthcare

Kalgotra¹,

Sharda²,

Paiva³

2017

JMWAIS

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Healthcare industry generates streams of data in different problem domains. Analysis of such data requires stream analytics tools and techniques to generate useful insights. Stream analytics involve analysis of time variant events. The specific patterns in the events can indicate some imminent outcomes such as state of a heart, etc. Therefore, novel ways to find specific patterns in the events generated by multiple sources are required. A key requirement for applying any such method is data preparation and organization to enable such analysis. In this paper, we extend the CRISP-DM process to include data preparation approaches for sequence mining. We present progression analysis, an approach for converting multidimensional time variant streams of health records in a form to be able to detect useful sequential signals. To illustrate the process, we use patient health history stored in an Electronic Medical Record system (EMR) and present a healthcare application to compare progression of diseases over time between patients diagnosed with Tobacco Use Disorder (TUD) and non-tobacco users. Interestingly, many diseases follow the same path for TUD and non-TUD patients. Finally, the generalizability of the progression analysis is discussed.

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Multivariate temporal data analysis ‐ a review

Moskovitch

2021

WIREs Data Min & Knowl

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The information technology revolution, especially with the adoption of the Internet of Things, longitudinal data in many domains become more available and accessible for secondary analysis. Such data provide meaningful opportunities to understand process in many domains along time, but also challenges. A main challenge is the heterogeneity of the temporal variables due to the different types of data, whether a measurement or an event, and type of samplings: fixed or irregular. Other variables can be also events that may or not have duration. In this review, we discuss the various types of temporal data, and the various relevant analysis methods. Starting with fixed frequency variables, with forecasting and time series methods, and proceeding with sequential data, and sequential patterns mining, and time intervals mining for events having various time duration. Also the use of various deep learning based architectures for temporal data is discussed. The challenge of heterogeneous multivariate temporal data analysis and discuss various options to deal with it, focusing on an increasingly used option of transforming the data into symbolic time intervals through temporal abstraction and the use of time intervals related patterns discovery for temporal knowledge discovery, clustering, classification prediction, and more. Finally, we discuss the overview of the field, and areas in which more studies and contributions are needed. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mininganalytics, temporal data mining, time series | INTRODUCTIONWith the introduction of the internet, an exponential growth in text based data, which required new methods to analyze text, retrieve, understand, classify, and more. However, with the recent growing adoption of the Internet of Things, in which devices in various domains become connected to the internet network and produce multitude of logged longitudinal data, we are facing a similar increase in multivariate temporal data that is generated by various devices and sensors. Such data can be logged from smart watches, cars, smart refrigerators, smart phones, televisions, sensors in the space of rooms for various purposes, and many more, which can be useful in various domains, and for different tasks. This trend is expected to increase significantly and requires new methods and more scalability.An extension of temporal data analytics, which are out of the scope of this article, is spatio-temporal data analysis (Gong

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A temporal pattern mining approach for classifying electronic health record data

Cited by 104 publications

References 35 publications

On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer

On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer

Analysis of a Sequence of Events in Healthcare

Multivariate temporal data analysis ‐ a review

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