The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria

Identifying cohorts of patients based on eligibility criteria such as medical conditions, procedures, and medication use is critical to recruitment for clinical trials. Such criteria are often most naturally described in free-text, using language familiar to clinicians and researchers. In order to identify potential participants at scale, these criteria must first be translated into queries on clinical databases, which can be labor-intensive and error-prone. Natural language processing (NLP) methods offer a potential means of such conversion into database queries automatically. However they must first be trained and evaluated using corpora which capture clinical trials criteria in sufficient detail. In this paper, we introduce the Leaf Clinical Trials (LCT) corpus, a human-annotated corpus of over 1,000 clinical trial eligibility criteria descriptions using highly granular structured labels capturing a range of biomedical phenomena. We provide details of our schema, annotation process, corpus quality, and statistics. Additionally, we present baseline information extraction results on this corpus as benchmarks for future work.

Event-based clinical findings extraction from radiology reports with pre-trained language model

Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images. Comprehensive semantic representations of radiological findings would enable a wide range of secondary use applications to support diagnosis, triage, outcomes prediction, and clinical research. In this paper, we present a new corpus of radiology reports annotated with clinical findings. Our annotation schema captures detailed representations of pathologic findings that are observable on imaging ("lesions") and other types of clinical problems ("medical problems"). The schema used an event-based representation to capture fine-grained details, including assertion, anatomy, characteristics, size, count, etc. Our gold standard corpus contained a total of 500 annotated computed tomography (CT) reports. We extracted triggers and argument entities using two state-of-the-art deep learning architectures, including BERT. We then predicted the linkages between trigger and argument entities (referred to as argument roles) using a BERT-based relation extraction model. We achieved the best extraction performance using a BERT model pre-trained on 3 million radiology reports from our institution: 90.9%-93.4% F1 for finding triggers 72.0%-85.6% F1 for arguments roles. To assess model generalizability, we used an external validation set randomly sampled from the MIMIC Chest X-ray (MIMIC-CXR) database. The extraction performance on this validation set was 95.6% for finding triggers and 79.1%-89.7% for argument roles, demonstrating that the model generalized well to the cross-institutional data with a different imaging modality. We extracted the finding events from all the radiology reports in the MIMIC-CXR database and provided the extractions to the research community.

Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model

Medical imaging is critical to the diagnosis and treatment of numerous medical problems, including many forms of cancer. Medical imaging reports distill the findings and observations of radiologists, creating an unstructured textual representation of unstructured medical images. Large-scale use of this text-encoded information requires converting the unstructured text to a structured, semantic representation. We explore the extraction and normalization of anatomical information in radiology reports that is associated with radiological findings. We investigate this extraction and normalization task using a span-based relation extraction model that jointly extracts entities and relations using BERT. This work examines the factors that influence extraction and normalization performance, including the body part/organ system, frequency of occurrence, span length, and span diversity. It discusses approaches for improving performance and creating high-quality semantic representations of radiological phenomena.

Identifying ARDS using the Hierarchical Attention Network with Sentence Objectives Framework

Acute respiratory distress syndrome (ARDS) is a life-threatening condition that is often undiagnosed or diagnosed late. ARDS is especially prominent in those infected with COVID-19. We explore the automatic identification of ARDS indicators and confounding factors in free-text chest radiograph reports. We present a new annotated corpus of chest radiograph reports and introduce the Hierarchical Attention Network with Sentence Objectives (HANSO) text classification framework. HANSO utilizes fine-grained annotations to improve document classification performance. HANSO can extract ARDS-related information with high performance by leveraging relation annotations, even if the annotated spans are noisy. Using annotated chest radiograph images as a gold standard, HANSO identifies bilateral infiltrates, an indicator of ARDS, in chest radiograph reports with performance (0.87 F1) comparable to human annotations (0.84 F1). This algorithm could facilitate more efficient and expeditious identification of ARDS by clinicians and researchers and contribute to the development of new therapies to improve patient care.

Performance of Automatic De-identification Across Different Note Types

Free-text clinical notes detail all aspects of patient care and have great potential to facilitate quality improvement and assurance initiatives as well as advance clinical research. However, concerns about patient privacy and confidentiality limit the use of clinical notes for research. As a result, the information documented in these notes remains unavailable for most researchers. De-identification (de-id), i.e., locating and removing personally identifying protected health information (PHI), is one way of improving access to clinical narratives. However, there are limited off-the-shelf de-identification systems able to consistently detect PHI across different data sources and medical specialties. In this abstract, we present the performance of a state-of-the art de-id system called NeuroNER1 on a diverse set of notes from University of Washington (UW) when the models are trained on data from an external institution (Partners Healthcare) vs. from the same institution (UW). We present results at the level of PHI and note types.

Transferability of Neural Network-based De-identification Systems

Methods and Materials: We investigated transferability of neural network-based de-identification sys-tems with and without domain generalization. We used two domain generalization approaches: a novel approach Joint-Domain Learning (JDL) as developed in this paper, and a state-of-the-art domain general-ization approach Common-Specific Decomposition (CSD) from the literature. First, we measured trans-ferability from a single external source. Second, we used two external sources and evaluated whether domain generalization can improve transferability of de-identification models across domains which rep-resent different note types from the same institution. Third, using two external sources with in-domain training data, we studied whether external source data are useful even in cases where sufficient in-domain training data are available. Finally, we investigated transferability of the de-identification mod-els across institutions. Results and Conclusions: We found transferability from a single external source gave inconsistent re-sults. Using additional external sources consistently yielded an F1-score of approximately 80%, but domain generalization was not always helpful to improve transferability. We also found that external sources were useful even in cases where in-domain training data were available by reducing the amount of needed in-domain training data or by improving performance. Transferability across institutions was differed by note type and annotation label. External sources from a different institution were also useful to further improve performance.

Jointly Learning Clinical Entities and Relations with Contextual Language Models and Explicit Context

We hypothesize that explicit integration of contextual information into an Multi-task Learning framework would emphasize the significance of context for boosting performance in jointly learning Named Entity Recognition (NER) and Relation Extraction (RE). Our work proves this hypothesis by segmenting entities from their surrounding context and by building contextual representations using each independent segment. This relation representation allows for a joint NER/RE system that achieves near state-of-the-art (SOTA) performance on both NER and RE tasks while beating the SOTA RE system at end-to-end NER & RE with a 49.07 F1.

Extracting COVID-19 Diagnoses and Symptoms From Clinical Text: A New Annotated Corpus and Neural Event Extraction Framework

Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). In a secondary use application, we explored the prediction of COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information. The automatically extracted symptoms improve prediction performance, beyond structured data alone.

Automatic Assignment of Radiology Examination Protocols Using Pre-trained Language Models with Knowledge Distillation

Selecting radiology examination protocol is a repetitive, error-prone, and time-consuming process. In this paper, we present a deep learning approach to automatically assign protocols to computer tomography examinations, by pre-training a domain-specific BERT model ($BERT_{rad}$). To handle the high data imbalance across exam protocols, we used a knowledge distillation approach that up-sampled the minority classes through data augmentation. We compared classification performance of the described approach with the statistical n-gram models using Support Vector Machine (SVM) and Random Forest (RF) classifiers, as well as the Google's $BERT_{base}$ model. SVM and RF achieved macro-averaged F1 scores of 0.45 and 0.6 while $BERT_{base}$ and $BERT_{rad}$ achieved 0.61 and 0.63. Knowledge distillation improved overall performance on the minority classes, achieving a F1 score of 0.66. Additionally, by choosing the optimal threshold, the BERT models could classify over 50% of test samples within 5% error rate and potentially alleviate half of radiologist protocoling workload.

Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction

Social determinants of health (SDOH) affect health outcomes, and knowledge of SDOH can inform clinical decision-making. Automatically extracting SDOH information from clinical text requires data-driven information extraction models trained on annotated corpora that are heterogeneous and frequently include critical SDOH. This work presents a new corpus with SDOH annotations, a novel active learning framework, and the first extraction results on the new corpus. The Social History Annotation Corpus (SHAC) includes 4,480 social history sections with detailed annotation for 12 SDOH characterizing the status, extent, and temporal information of 18K distinct events. We introduce a novel active learning framework that selects samples for annotation using a surrogate text classification task as a proxy for a more complex event extraction task. The active learning framework successfully increases the frequency of health risk factors and improves automatic detection of these events over undirected annotation. An event extraction model trained on SHAC achieves high extraction performance for substance use status (0.82-0.93 F1), employment status (0.81-0.86 F1), and living status type (0.81-0.93 F1) on data from three institutions.