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Erschienen in:
Machine Learning for Health Informatics Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Knowledge Discovery in Clinical Data

verfasst von : Aryya Gangopadhyay, Rose Yesha, Eliot Siegel

Erschienen in: Machine Learning for Health Informatics

Verlag: Springer International Publishing

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Abstract

There has been a recent surge in the implementation of electronic health care records. These patient records contain valuable medical information including patient demographic data, diagnosis, therapeutic approach, and patient outcomes. It is important to analyze patterns within these records in order to more effectively treat individuals. In this paper, a method is presented for identifying these themes and patterns within patient data. This methodology includes extraction of the main themes or patterns in the data and linking those themes back to the corpus from which they were generated. In our research, we partitioned graphs from terms gathered from electronic medical records. We used two sets of data including eight charts and ten case studies for this study from primary disease categories. The Electronic Medical Records (EMRs) and case studies were modeled as networks of interacting terms where the interactions were captured by their co-occurrences in the documents. A greedy algorithm was used to find communities with high modularity. Finally, we compared our method with probabilistic topic modeling algorithms and evaluated the efficacy of our method by using recall and precision measures.

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Vorheriges Kapitel Network-Guided Biomarker Discovery
Nächstes Kapitel Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning
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Metadaten
Titel
Knowledge Discovery in Clinical Data
verfasst von
Aryya Gangopadhyay
Rose Yesha
Eliot Siegel
Copyright-Jahr
2016
Buch
Machine Learning for Health Informatics

Print ISBN: 978-3-319-50477-3

Electronic ISBN: 978-3-319-50478-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-50478-0_17