Preprocessing structured clinical data for predictive modeling and decision support
| Autor: | Mónica Duarte Oliveira, Filipe Janela, Jose Ferrao, Henrique Martins |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Decision support system
020205 medical informatics Computer science Health Informatics 02 engineering and technology State of the Art/Best Practice Paper Health informatics Clinical decision support system 03 medical and health sciences 0302 clinical medicine Health Information Management 0202 electrical engineering electronic engineering information engineering Data Mining Electronic Health Records Humans 030212 general & internal medicine Models Statistical business.industry Decision Support Systems Clinical Missing data Data science Computer Science Applications Identifier Data access Feature (computer vision) Data pre-processing Erratum business |
| Zdroj: | Applied Clinical Informatics. :1135-1153 |
| ISSN: | 1869-0327 |
| DOI: | 10.4338/aci-2016-03-soa-0035 |
| Popis: | SummaryBackground EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls.Objectives This article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them.Methods Along standard data processing stages – extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework –, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies.Results Following the EDPAI framework, researchers face five key challenges: (1) gathering and integrating data, (2) identifying and handling different feature types, (3) combining features to handle redundancy and granularity, (4) addressing data missingness, and (5) handling multiple feature values. Strategies to address these challenges include: crosschecking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately.Conclusions This article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data.Citation: Ferrão JC, Oliveira MD, Janela F, Martins HMG. Preprocessing structured clinical data for predictive modeling and decision support – a roadmap to tackle the challenges. |
| Databáze: | OpenAIRE |
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