Selecting relevant features from the electronic health record for clinical code prediction

Abstract

A multitude of information sources is present in the electronic health record (EHR), each of which can contain clues to automatically assign diagnosis and procedure codes. These sources however show information overlap and quality differences, which complicates the retrieval of these clues. Through feature selection, a denser representation with a consistent quality and less information overlap can be obtained. We introduce and compare coverage-based feature selection methods, based on confidence and information gain. These approaches were evaluated over a range of medical specialties, with seven different medical specialties for ICD-9-CM code prediction (six at the Antwerp University Hospital and one in the MIMIC-III dataset) and two different medical specialties for ICD-10-CM code prediction. Using confidence coverage to integrate all sources in an EHR shows a consistent improvement in F-measure (49.83% for diagnosis codes on average), both compared with the baseline (44.25% for diagnosis codes on average) and with using the best standalone source (44.41% for diagnosis codes on average). Confidence coverage creates a concise patient stay representation independent of a rigid framework such as UMLS, and contains easily interpretable features. Confidence coverage has several advantages to a baseline setup. In our baseline setup, feature selection was limited to a filter removing features with less than five total occurrences in the trainingset. Prediction results improved consistently when using multiple heterogeneous sources to predict clinical codes, while reducing the number of features and the processing time.

Explainable Prediction of Medical Codes from Clinical Text

“…Automatic ICD coding ICD coding is a longstanding task in the medical informatics community, which has been approached with machine learning and handcrafted methods (Scheurwegs et al, 2015). Many recent approaches, like ours, use unstructured text data as the only source of information (e.g., Kavuluru et al, 2015;Subotin and Davis, 2014), though some incorporates struc- tured data as well (e.g., Scheurwegs et al, 2017;Wang et al, 2016). Most previous methods have either evaluated only on a strict subset of the full ICD label space (Wang et al, 2016), relied on datasets that focus on a subset of medical scenarios , or evaluated on data that are not publicly available, making direct comparison difficult (Subotin and Davis, 2016).…”

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…Variable selection and penalization methods along with sparse estimation strategies allow many predictors to be incorporated into statistical models, and there is an excellent opportunity for the use of such methods in the setting of EHR. Automated feature selection algorithms are often used within machine learning algorithms to determine which predictors to include, and this can also be combined with expert preprocessing of the candidate predictors . Regularization techniques, including LASSO, ridge regression, and elastic net, have been applied in the EHR setting …”

“…Automated feature selection algorithms are often used within machine learning algorithms to determine which predictors to include, and this can also be combined with expert preprocessing of the candidate predictors. 153,154 Regularization techniques, including LASSO, ridge regression, and elastic net, have been applied in the EHR setting. 155,156 Machine learning algorithms have also gained popularity in EHR data analysis, particularly in the development of risk prediction models.…”

A Comparison of Deep Learning Methods for ICD Coding of Clinical Records

“…The authors have evaluated ICD coding performance on a dataset consisting of more than 70,000 textual Electronic Medical Records (EMRs) from the University of Kentucky (UKY) Medical Center tagged with ICD-9 codes. Integrating feature selection on both structured and unstructured data is researched by the authors of [9] and has proven to aid the classification process. Two approaches are evaluated in this setting: early and late integration of structured and unstructured data, the latter yielding the better results.…”