846 research outputs found
Ontology-Based Clinical Information Extraction Using SNOMED CT
Extracting and encoding clinical information captured in unstructured clinical documents with standard medical terminologies is vital to enable secondary use of clinical data from practice. SNOMED CT is the most comprehensive medical ontology with broad types of concepts and detailed relationships and it has been widely used for many clinical applications. However, few studies have investigated the use of SNOMED CT in clinical information extraction.
In this dissertation research, we developed a fine-grained information model based on the SNOMED CT and built novel information extraction systems to recognize clinical entities and identify their relations, as well as to encode them to SNOMED CT concepts. Our evaluation shows that such ontology-based information extraction systems using SNOMED CT could achieve state-of-the-art performance, indicating its potential in clinical natural language processing
Assessing mortality prediction through different representation models based on concepts extracted from clinical notes
Recent years have seen particular interest in using electronic medical
records (EMRs) for secondary purposes to enhance the quality and safety of
healthcare delivery. EMRs tend to contain large amounts of valuable clinical
notes. Learning of embedding is a method for converting notes into a format
that makes them comparable. Transformer-based representation models have
recently made a great leap forward. These models are pre-trained on large
online datasets to understand natural language texts effectively. The quality
of a learning embedding is influenced by how clinical notes are used as input
to representation models. A clinical note has several sections with different
levels of information value. It is also common for healthcare providers to use
different expressions for the same concept. Existing methods use clinical notes
directly or with an initial preprocessing as input to representation models.
However, to learn a good embedding, we identified the most essential clinical
notes section. We then mapped the extracted concepts from selected sections to
the standard names in the Unified Medical Language System (UMLS). We used the
standard phrases corresponding to the unique concepts as input for clinical
models. We performed experiments to measure the usefulness of the learned
embedding vectors in the task of hospital mortality prediction on a subset of
the publicly available Medical Information Mart for Intensive Care (MIMIC-III)
dataset. According to the experiments, clinical transformer-based
representation models produced better results with getting input generated by
standard names of extracted unique concepts compared to other input formats.
The best-performing models were BioBERT, PubMedBERT, and UmlsBERT,
respectively
Ontology-driven and weakly supervised rare disease identification from clinical notes
BACKGROUND: Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. METHODS: We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-driven framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets, MIMIC-III discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging reports from two institutions in the US and the UK, with annotations. RESULTS: The improvements in the precision were pronounced (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. The overall pipeline processing clinical notes can extract rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). CONCLUSION: The study provides empirical evidence for the task by applying a weakly supervised NLP pipeline on clinical notes. The proposed weak supervised deep learning approach requires no human annotation except for validation and testing, by leveraging ontologies, NER+L tools, and contextual representations. The study also demonstrates that Natural Language Processing (NLP) can complement traditional ICD-based approaches to better estimate rare diseases in clinical notes. We discuss the usefulness and limitations of the weak supervision approach and propose directions for future studies
Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining
Biomedical knowledge is growing in an astounding pace with a majority of this
knowledge is represented as scientific publications. Text mining tools and
methods represents automatic approaches for extracting hidden patterns and
trends from this semi structured and unstructured data. In Biomedical Text
mining, Literature Based Discovery (LBD) is the process of automatically
discovering novel associations between medical terms otherwise mentioned in
disjoint literature sets. LBD approaches proven to be successfully reducing the
discovery time of potential associations that are hidden in the vast amount of
scientific literature. The process focuses on creating concept profiles for
medical terms such as a disease or symptom and connecting it with a drug and
treatment based on the statistical significance of the shared profiles. This
knowledge discovery approach introduced in 1989 still remains as a core task in
text mining. Currently the ABC principle based two approaches namely open
discovery and closed discovery are mostly explored in LBD process. This review
starts with general introduction about text mining followed by biomedical text
mining and introduces various literature resources such as MEDLINE, UMLS, MESH,
and SemMedDB. This is followed by brief introduction of the core ABC principle
and its associated two approaches open discovery and closed discovery in LBD
process. This review also discusses the deep learning applications in LBD by
reviewing the role of transformer models and neural networks based LBD models
and its future aspects. Finally, reviews the key biomedical discoveries
generated through LBD approaches in biomedicine and conclude with the current
limitations and future directions of LBD.Comment: 43 Pages, 5 Figures, 4 Table
Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques.
Biomedical natural language processing (NLP) has an important role in extracting consequential information in medical discharge notes. Detecting meaningful features from unstructured notes is a challenging task in medical document classification. The domain specific phrases and different synonyms within the medical documents make it hard to analyze them. Analyzing clinical notes becomes more challenging for short documents like abstract texts. All of these can result in poor classification performance, especially when there is a shortage of the clinical data in real life. Two new approaches (an ontology-guided approach and a combined ontology-based with dictionary-based approach) are suggested for augmenting medical data to enrich training data. Three different deep learning approaches are used to evaluate the classification performance of the proposed methods. The obtained results show that the proposed methods improved the classification accuracy in clinical notes classification
Accelerating COVID-19 research with graph mining and transformer-based learning
In 2020, the White House released the, "Call to Action to the Tech Community
on New Machine Readable COVID-19 Dataset," wherein artificial intelligence
experts are asked to collect data and develop text mining techniques that can
help the science community answer high-priority scientific questions related to
COVID-19. The Allen Institute for AI and collaborators announced the
availability of a rapidly growing open dataset of publications, the COVID-19
Open Research Dataset (CORD-19). As the pace of research accelerates,
biomedical scientists struggle to stay current. To expedite their
investigations, scientists leverage hypothesis generation systems, which can
automatically inspect published papers to discover novel implicit connections.
We present an automated general purpose hypothesis generation systems AGATHA-C
and AGATHA-GP for COVID-19 research. The systems are based on graph-mining and
the transformer model. The systems are massively validated using retrospective
information rediscovery and proactive analysis involving human-in-the-loop
expert analysis. Both systems achieve high-quality predictions across domains
(in some domains up to 0.97% ROC AUC) in fast computational time and are
released to the broad scientific community to accelerate biomedical research.
In addition, by performing the domain expert curated study, we show that the
systems are able to discover on-going research findings such as the
relationship between COVID-19 and oxytocin hormone
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