AIMC Topic: Humans

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CQL4NLP: Development and Integration of FHIR NLP Extensions in Clinical Quality Language for EHR-driven Phenotyping.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Lack of standardized representation of natural language processing (NLP) components in phenotyping algorithms hinders portability of the phenotyping algorithms and their execution in a high-throughput and reproducible manner. The objective of the stu...

Pseudo-data generation for the extraction of Problems, Treatments and Tests.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
One of the primary challenges for clinical Named Entity Recognition (NER) is the availability of annotated training data. Technical and legal hurdles prevent the creation and release of corpora related to electronic health records (EHRs). In this wor...

An Examination of the Statistical Laws of Semantic Change in Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Natural language is continually changing. Given the prevalence of unstructured, free-text clinical notes in the healthcare domain, understanding the aspects of this change is of critical importance to clinical Natural Language Processing (NLP) system...

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-making, which ...

Deep EHR Spotlight: a Framework and Mechanism to Highlight Events in Electronic Health Records for Explainable Predictions.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
The wide adoption of Electronic Health Records (EHR) has resulted in large amounts of clinical data becoming available, which promises to support service delivery and advance clinical and informatics research. Deep learning techniques have demonstrat...

Extracting Adverse Drug Events from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength, ...

Integration of NLP2FHIR Representation with Deep Learning Models for EHR Phenotyping: A Pilot Study on Obesity Datasets.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
HL7 Fast Healthcare Interoperability Resources (FHIR) is one of the current data standards for enabling electronic healthcare information exchange. Previous studies have shown that FHIR is capable of modeling both structured and unstructured data fro...

A Comparison between Human and NLP-based Annotation of Clinical Trial Eligibility Criteria Text Using The OMOP Common Data Model.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Human annotations are the established gold standard for evaluating natural language processing (NLP) methods. The goals of this study are to quantify and qualify the disagreement between human and NLP. We developed an NLP system for annotating clinic...

Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes ge...

Generative Adversarial Networks for Creating Synthetic Free-Text Medical Data: A Proposal for Collaborative Research and Re-use of Machine Learning Models.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
Restrictions in sharing Patient Health Identifiers (PHI) limit cross-organizational re-use of free-text medical data. We leverage Generative Adversarial Networks (GAN) to produce synthetic unstructured free-text medical data with low re-identificatio...