Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach

Abstract

BackgroundMedical terms are a major obstacle for patients to comprehend their electronic health record (EHR) notes. Clinical natural language processing (NLP) systems that link EHR terms to lay terms or definitions allow patients to easily access helpful information when reading through their EHR notes, and have shown to improve patient EHR comprehension. However, high-quality lay language resources for EHR terms are very limited in the public domain. Because expanding and curating such a resource is a costly process, it is beneficial and even necessary to identify terms important for patient EHR comprehension first.ObjectiveWe aimed to develop an NLP system, called adapted distant supervision (ADS), to rank candidate terms mined from EHR corpora. We will give EHR terms ranked as high by ADS a higher priority for lay language annotation—that is, creating lay definitions for these terms.MethodsAdapted distant supervision uses distant supervision from consumer health vocabulary and transfer learning to adapt itself to solve the problem of ranking EHR terms in the target domain. We investigated 2 state-of-the-art transfer learning algorithms (ie, feature space augmentation and supervised distant supervision) and designed 5 types of learning features, including distributed word representations learned from large EHR data for ADS. For evaluating ADS, we asked domain experts to annotate 6038 candidate terms as important or nonimportant for EHR comprehension. We then randomly divided these data into the target-domain training data (1000 examples) and the evaluation data (5038 examples). We compared ADS with 2 strong baselines, including standard supervised learning, on the evaluation data.ResultsThe ADS system using feature space augmentation achieved the best average precision, 0.850, on the evaluation set when using 1000 target-domain training examples. The ADS system using supervised distant supervision achieved the best average precision, 0.819, on the evaluation set when using only 100 target-domain training examples. The 2 ADS systems both performed significantly better than the baseline systems (P<.001 for all measures and all conditions). Using a rich set of learning features contributed to ADS’s performance substantially.ConclusionsADS can effectively rank terms mined from EHRs. Transfer learning improved ADS’s performance even with a small number of target-domain training examples. EHR terms prioritized by ADS were used to expand a lay language resource that supports patient EHR comprehension. The top 10,000 EHR terms ranked by ADS are available upon request.

A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

“…For quality assurance, all definitions in CoDeMed were collected from authorized online health education resources (eg, glossaries of National Institute of Health and National Cancer Institute) and simplified and reviewed by domain experts, which included MDs. Because this process is time-consuming, we developed an adapted distant supervision (ADS) system to automatically identify important medical terms from EHR corpora to prioritize the annotation efforts on these terms [ 33 ].…”

“…To alleviate this problem, our system used transfer learning and a small amount of manually annotated training examples to adapt the classification model to the target domain to identify medical terms that are important for patient EHR comprehension. We empirically show the effectiveness of ADS by using a gold standard dataset of 6038 EHR terms annotated by domain experts [ 33 ]. For each candidate term, the ADS system output its probability of being an important term.…”

A clinical text classification paradigm using weak supervision and deep representation

“…Furthermore, weak supervision has been widely applied in other common NLP tasks including knowledge-base completion [28], sentiment analysis [29], and information retrieval [30]. In the biomedical domain, weak supervision has been used to augment machine learning based classifiers to identify drug-drug interactions or medical terms from biomedical literature [31–34]. In the clinical domain, Wallace et al [35] proposed a weak supervision approach to better exploit a weakly labeled corpus to extract sentences of population/problem, intervention, comparator, and outcome from clinical trial reports.…”

Unsupervised Clinical Language Translation

“…Studies show that expert-curated dictionaries don't include all professional words that are commonly seen in the clinical narratives (e.g. "lumbar" is not seen in CHV) [9,23]. In contrast, the layman terms are not covered well in the UMLS [15].…”