NDLI: Cascading adverse drug event detection in electronic health records

Content Provider	IEEE Xplore Digital Library
Author	Jing Zhao Henriksson, A. Bostrom, H.
Copyright Year	2015
Description	Author affiliation: Comput. & Syst. Sci., Stockholm Univ., Stockholm, Sweden (Jing Zhao; Henriksson, A.; Bostrom, H.)
Abstract	The ability to detect adverse drug events (ADEs) in electronic health records (EHRs) is useful in many medical applications, such as alerting systems that indicate when an ADE-specific diagnosis code should be assigned. Automating the detection of ADEs can be attempted by applying machine learning to existing, labeled EHR data. How to do this in an effective manner is, however, an open question. The issues addressed in this study concern the granularity of the classification task: (1) If we wish to predict the occurrence of any ADE, is it advantageous to conflate the various ADE class labels prior to learning, or should they be merged post prediction? (2) If we wish to predict a family of ADEs or even a specific ADE, can the predictive performance be enhanced by dividing the classification task into a cascading scheme: predicting first, on a coarse level, whether there is an ADE or not, and, in the former case, followed by a more specific prediction on which family the ADE belongs to, and then finally a prediction on the specific ADE within that particular family? In this study, we conduct a series of experiments using a real, clinical dataset comprising healthcare episodes that have been assigned one of eight ADE-related diagnosis codes and a set of randomly extracted episodes that have not been assigned any ADE code. It is shown that, when distinguishing between ADEs and non-ADEs, merging the various ADE labels prior to learning leads to significantly higher predictive performance in terms of accuracy and area under ROC curve. A cascade of random forests is moreover constructed to determine either the family of ADEs or the specific class label; here, the performance is indeed enhanced compared to directly employing a one-step prediction. This study concludes that, if predictive performance is of primary importance, the cascading scheme should be the recommended approach over employing a one-step prediction for detecting ADEs in EHRs.
Sponsorship	IEEE Comput. Intell. Soc.
Starting Page	1
Ending Page	8
File Size	801141
Page Count	8
File Format	PDF
ISBN	9781467382724
DOI	10.1109/DSAA.2015.7344869
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-19
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Computers Safety History Electronic medical records Diseases
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Ontology-based cardiac adverse drug event problem prevention

Adverse Drug Event Notification System: Reusing Clinical Patient Data for Semi-automatic ADE Detection

Mining Adverse Drug Reactions from Electronic Health Records

Data Mining to Generate Adverse Drug Events Detection Rules

Dimensionality Reduction with Random Indexing: An Application on Adverse Drug Event Detection Using Electronic Health Records

The ADE scorecards: a tool for adverse drug event detection in electronic health records.

Linked2Safety: A secure linked data medical information space for semantically-interconnecting EHRs advancing patients' safety in medical research

par Emmanuel Chazard AUTOMATED DETECTION OF ADVERSE DRUG EVENTS BY DATA MINING OF ELECTRONIC HEALTH RECORDS (2011)

Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection

Cascading adverse drug event detection in electronic health records

Similar Documents

Ontology-based cardiac adverse drug event problem prevention

Adverse Drug Event Notification System: Reusing Clinical Patient Data for Semi-automatic ADE Detection

Mining Adverse Drug Reactions from Electronic Health Records

Data Mining to Generate Adverse Drug Events Detection Rules

Dimensionality Reduction with Random Indexing: An Application on Adverse Drug Event Detection Using Electronic Health Records

The ADE scorecards: a tool for adverse drug event detection in electronic health records.

Linked2Safety: A secure linked data medical information space for semantically-interconnecting EHRs advancing patients' safety in medical research

par Emmanuel Chazard AUTOMATED DETECTION OF ADVERSE DRUG EVENTS BY DATA MINING OF ELECTRONIC HEALTH RECORDS (2011)

Modeling electronic health records in ensembles of semantic spaces for adverse drug event detection

Cascading adverse drug event detection in electronic health records