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Detecting Adverse Drug Events Using Concept Hierarchies of Clinical Codes
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2014 (English)In: 2014 IEEE International Conference on Healthcare Informatics: Proceedings, IEEE Computer Society, 2014, 285-293 p.Conference paper, Published paper (Refereed)
Abstract [en]

Electronic health records (EHRs) provide a potentially valuable source of information for pharmacovigilance. However, adverse drug events (ADEs), which can be encoded in EHRs with specific diagnosis codes, are heavily under-reported. To provide more accurate estimates for drug safety surveillance, machine learning systems that are able to detect ADEs could be used to identify and suggest missing ADE-specific diagnosis codes. A fundamental consideration when building such systems is how to represent the EHR data to allow for accurate predictive modeling. In this study, two types of clinical code are used to represent drugs and diagnoses: the Anatomical Therapeutic Chemical Classification System (ATC) and the International Statistical Classification of Diseases and Health Problems (ICD). More specifically, it is investigated whether their hierarchical structure can be exploited to improve predictive performance. The use of random forests with feature sets that include only the original, low-level, codes is compared to using random forests with feature sets that contain all levels in the hierarchies. An empirical investigation using thirty datasets with different ADE targets is presented, demonstrating that the predictive performance, in terms of accuracy and area under ROC curve, can be significantly improved by exploiting codes on all levels in the hierarchies, compared to using only the low-level encoding. A further analysis is presented in which two strategies are employed for adding features level-wise according to the concept hierarchies: top-down, starting with the highest abstraction levels, and bottom-up, starting with the most specific encoding. The main finding from this subsequent analysis is that predictive performance can be kept at a high level even without employing the more specific levels in the concept hierarchies.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014. 285-293 p.
Keyword [en]
Clinical codes, concept hierarchy, electronic health records, adverse drug events, data mining
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-110969DOI: 10.1109/ICHI.2014.46ISBN: 978-1-4799-5701-9 (electronic)OAI: oai:DiVA.org:su-110969DiVA: diva2:773743
Conference
IEEE International Conference on Healthcare Informatics, Verona, Italy, 15-17 September 2014
Available from: 2014-12-19 Created: 2014-12-19 Last updated: 2017-01-30Bibliographically approved
In thesis
1. Learning Predictive Models from Electronic Health Records
Open this publication in new window or tab >>Learning Predictive Models from Electronic Health Records
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The ongoing digitization of healthcare, which has been much accelerated by the widespread adoption of electronic health records, generates unprecedented amounts of clinical data in a readily computable form. This, in turn, affords great opportunities for making meaningful secondary use of clinical data in the endeavor to improve healthcare, as well as to support epidemiology and medical research. To that end, there is a need for techniques capable of effectively and efficiently analyzing large amounts of clinical data. While machine learning provides the necessary tools, learning effective predictive models from electronic health records comes with many challenges due to the complexity of the data. Electronic health records contain heterogeneous and longitudinal data that jointly provides a rich perspective of patient trajectories in the healthcare process. The diverse characteristics of the data need to be properly accounted for when learning predictive models from clinical data. However, how best to represent healthcare data for predictive modeling has been insufficiently studied. This thesis addresses several of the technical challenges involved in learning effective predictive models from electronic health records.

Methods are developed to address the challenges of (i) representing heterogeneous types of data, (ii) leveraging the concept hierarchy of clinical codes, and (iii) modeling the temporality of clinical events. The proposed methods are evaluated empirically in the context of detecting adverse drug events in electronic health records. Various representations of each type of data that account for its unique characteristics are investigated and it is shown that combining multiple representations yields improved predictive performance. It is also demonstrated how the information embedded in the concept hierarchy of clinical codes can be exploited, both for creating enriched feature spaces and for decomposing the predictive task. Moreover, incorporating temporal information leads to more effective predictive models by distinguishing between event occurrences in the patient history. Both single-point representations, using pre-assigned or learned temporal weights, and multivariate time series representations are shown to be more informative than representations in which temporality is ignored. Effective methods for representing heterogeneous and longitudinal data are key for enhancing and truly enabling meaningful secondary use of electronic health records through large-scale analysis of clinical data.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2017. 82 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 17-001
Keyword
Data Science, Machine Learning, Predictive Modeling, Data Representation, Health Informatics, Electronic Health Records
National Category
Computer Science
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-137936 (URN)978-91-7649-682-4 (ISBN)978-91-7649-683-1 (ISBN)
Public defence
2017-03-02, Lilla hörsalen, NOD-huset, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Available from: 2017-02-07 Created: 2017-01-13 Last updated: 2017-02-08Bibliographically approved

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