4 research outputs found
PhenoDEF: a corpus for annotating sentences with information of phenotype definitions in biomedical literature
Background
Adverse events induced by drug-drug interactions are a major concern in the United States. Current research is moving toward using electronic health record (EHR) data, including for adverse drug events discovery. One of the first steps in EHR-based studies is to define a phenotype for establishing a cohort of patients. However, phenotype definitions are not readily available for all phenotypes. One of the first steps of developing automated text mining tools is building a corpus. Therefore, this study aimed to develop annotation guidelines and a gold standard corpus to facilitate building future automated approaches for mining phenotype definitions contained in the literature. Furthermore, our aim is to improve the understanding of how these published phenotype definitions are presented in the literature and how we annotate them for future text mining tasks.
Results
Two annotators manually annotated the corpus on a sentence-level for the presence of evidence for phenotype definitions. Three major categories (inclusion, intermediate, and exclusion) with a total of ten dimensions were proposed characterizing major contextual patterns and cues for presenting phenotype definitions in published literature. The developed annotation guidelines were used to annotate the corpus that contained 3971 sentences: 1923 out of 3971 (48.4%) for the inclusion category, 1851 out of 3971 (46.6%) for the intermediate category, and 2273 out of 3971 (57.2%) for exclusion category. The highest number of annotated sentences was 1449 out of 3971 (36.5%) for the “Biomedical & Procedure” dimension. The lowest number of annotated sentences was 49 out of 3971 (1.2%) for “The use of NLP”. The overall percent inter-annotator agreement was 97.8%. Percent and Kappa statistics also showed high inter-annotator agreement across all dimensions.
Conclusions
The corpus and annotation guidelines can serve as a foundational informatics approach for annotating and mining phenotype definitions in literature, and can be used later for text mining applications
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
Deriving disease subtypes from electronic health records (EHRs) can guide
next-generation personalized medicine. However, challenges in summarizing and
representing patient data prevent widespread practice of scalable EHR-based
stratification analysis. Here we present an unsupervised framework based on
deep learning to process heterogeneous EHRs and derive patient representations
that can efficiently and effectively enable patient stratification at scale. We
considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising
of a total of 57,464 clinical concepts. We introduce a representation learning
model based on word embeddings, convolutional neural networks, and autoencoders
(i.e., ConvAE) to transform patient trajectories into low-dimensional latent
vectors. We evaluated these representations as broadly enabling patient
stratification by applying hierarchical clustering to different multi-disease
and disease-specific patient cohorts. ConvAE significantly outperformed several
baselines in a clustering task to identify patients with different complex
conditions, with 2.61 entropy and 0.31 purity average scores. When applied to
stratify patients within a certain condition, ConvAE led to various clinically
relevant subtypes for different disorders, including type 2 diabetes,
Parkinson's disease and Alzheimer's disease, largely related to comorbidities,
disease progression, and symptom severity. With these results, we demonstrate
that ConvAE can generate patient representations that lead to clinically
meaningful insights. This scalable framework can help better understand varying
etiologies in heterogeneous sub-populations and unlock patterns for EHR-based
research in the realm of personalized medicine.Comment: C.F. and R.M. share senior authorshi
Biomedical Literature Mining and Knowledge Discovery of Phenotyping Definitions
Indiana University-Purdue University Indianapolis (IUPUI)Phenotyping definitions are essential in cohort identification when conducting
clinical research, but they become an obstacle when they are not readily available.
Developing new definitions manually requires expert involvement that is labor-intensive,
time-consuming, and unscalable. Moreover, automated approaches rely mostly on
electronic health records’ data that suffer from bias, confounding, and incompleteness.
Limited efforts established in utilizing text-mining and data-driven approaches to automate
extraction and literature-based knowledge discovery of phenotyping definitions and to
support their scalability. In this dissertation, we proposed a text-mining pipeline combining
rule-based and machine-learning methods to automate retrieval, classification, and
extraction of phenotyping definitions’ information from literature. To achieve this, we first
developed an annotation guideline with ten dimensions to annotate sentences with evidence
of phenotyping definitions' modalities, such as phenotypes and laboratories. Two
annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text
observational studies’ methods sections (n=86). Percent and Kappa statistics showed high
inter-annotator agreement on sentence-level annotations. Second, we constructed two
validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level.
We applied the abstract-level classifier on a large-scale biomedical literature of over
20 million abstracts published between 1975 and 2018 to classify positive abstracts
(n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from
their methods sections and used the full-text sentence-level classifier to extract positive
sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the
positively classified sentences. Lexica-based methods were used to recognize medical
concepts in these sentences (n=19,423). Co-occurrence and association methods were used
to identify and rank phenotype candidates that are associated with a phenotype of interest.
We derived 12,616,465 associations from our large-scale corpus. Our literature-based
associations and large-scale corpus contribute in building new data-driven phenotyping
definitions and expanding existing definitions with minimal expert involvement