| 1. |
- Asker, Lars, et al.
(författare)
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Mining Candidates for Adverse Drug Interactions in Electronic Patient Records
- 2014
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Ingår i: PETRA '14 Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments, PETRA’14. - New York : ACM Press. - 9781450327466
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Konferensbidrag (refereegranskat)abstract
- Electronic patient records provide a valuable source of information for detecting adverse drug events. In this paper, we explore two different but complementary approaches to extracting useful information from electronic patient records with the goal of identifying candidate drugs, or combinations of drugs, to be further investigated for suspected adverse drug events. We propose a novel filter-and-refine approach that combines sequential pattern mining and disproportionality analysis. The proposed method is expected to identify groups of possibly interacting drugs suspected for causing certain adverse drug events. We perform an empirical investigation of the proposed method using a subset of the Stockholm electronic patient record corpus. The data used in this study consists of all diagnoses and medications for a group of patients diagnoses with at least one heart related diagnosis during the period 2008--2010. The study shows that the method indeed is able to detect combinations of drugs that occur more frequently for patients with cardiovascular diseases than for patients in a control group, providing opportunities for finding candidate drugs that cause adverse drug effects through interaction.
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| 2. |
- Tselas, Nikolaos, et al.
(författare)
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Benchmarking Dynamic Time Warping on Nearest Neighbor Classification of Electrocardiograms
- 2014
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Ingår i: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments. - New York, NY, USA : ACM Press. - 9781450327466
-
Konferensbidrag (refereegranskat)abstract
- The human cardiovascular system is a complicated structure that has been the focus of research in many different domains, such as medicine, biology, as well as computer science. Due to the complexity of the heart, even nowadays some of the most common disorders are still hard to identify. In this paper, we map each ECG to a time series or set of time series and explore the applicability of two common time series similarity matching methods, namely, DTW and cDTW, to the problem of ECG classification. We benchmark the two methods on four different datasets in terms of accuracy. In addition, we explore their predictive performance when various ECG channels are taken into account. The latter is performed using a dataset taken from Physiobank. Our findings suggest that different ECG channels are more appropriate for different cardiovascular malfunctions.
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