The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track

Madan, Sumit; Szostak, Justyna; Elayavilli, Ravikumar Komandur; Tsai, Richard Tzong Han; Ali, Mehdi; Qian, Longhua; Rastegar-Mojarad, Majid; Hoeng, Julia; Fluck, Juliane

doi:10.1093/database/baz084

Cited by 18 publications

(14 citation statements)

References 33 publications

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“…While this NLP model performed well, model performance (precision and recall) could improve with the use of more complex strategies such as utilizing bigram or n-gram BoW, other sophisticated feature extractions (eg, graph mining [ 19 ]), and nonlinear models (eg, deep learning [ 20 ]). To reduce the computational cost of developing and implementing a model to test the use of PPC by trained facilitators, we propose the NLP model as a method that reduces the time and cost with other methods of assessment.…”

Section: Discussionmentioning

confidence: 99%

Measuring Adoption of Patient Priorities–Aligned Care Using Natural Language Processing of Electronic Health Records: Development and Validation of the Model

et al. 2021

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Background Patient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults who have multiple chronic conditions. Following identification of patient priorities, this information is documented in the patient’s electronic health record (EHR). Objective Our goal is to develop and validate a natural language processing (NLP) model that reliably documents when clinicians identify patient priorities (ie, values, outcome goals, and care preferences) within the EHR as a measure of PPC adoption. Methods This is a retrospective analysis of unstructured National Veteran Health Administration EHR free-text notes using an NLP model. The data were sourced from 778 patient notes of 658 patients from encounters with 144 social workers in the primary care setting. Each patient’s free-text clinical note was reviewed by 2 independent reviewers for the presence of PPC language such as priorities, values, and goals. We developed an NLP model that utilized statistical machine learning approaches. The performance of the NLP model in training and validation with 10-fold cross-validation is reported via accuracy, recall, and precision in comparison to the chart review. Results Of 778 notes, 589 (75.7%) were identified as containing PPC language (kappa=0.82, P<.001). The NLP model in the training stage had an accuracy of 0.98 (95% CI 0.98-0.99), a recall of 0.98 (95% CI 0.98-0.99), and precision of 0.98 (95% CI 0.97-1.00). The NLP model in the validation stage had an accuracy of 0.92 (95% CI 0.90-0.94), recall of 0.84 (95% CI 0.79-0.89), and precision of 0.84 (95% CI 0.77-0.91). In contrast, an approach using simple search terms for PPC only had a precision of 0.757. Conclusions An automated NLP model can reliably measure with high precision, recall, and accuracy when clinicians document patient priorities as a key step in the adoption of PPC.

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Section: Discussionmentioning

confidence: 99%

Measuring Adoption of Patient Priorities–Aligned Care Using Natural Language Processing of Electronic Health Records: Development and Validation of the Model

et al. 2021

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“…The corpus was provided by the organizer for the BioCreative V BEL task, which contains the training, sample and test sets. There is also a similar task ( 27 ) in the 2017 BioCreative VI, which uses the same training set as BC-V, but provides a new test set. However, the new test set is not publicly available, and therefore, we conduct experiments and compare results on the BC-V test set.…”

Section: Methodsmentioning

confidence: 99%

Extraction of causal relations based on SBEL and BERT model

Shao

et al. 2021

Database

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Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form—SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively. Database URL: https://github.com/grapeff/SBEL_datasets

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“…Molecular pathways can be “coded” as a network of relations by employing computational linguistics and semantic frameworks. One possible tool for this approach is the Biological Expression Language (BEL), which makes it possible to describe the interaction between proteins, genes, and other chemical compounds with means of first-order logic (Madan et al, 2019 ).…”

Section: Modeling Of Alzheimer's Diseasementioning

confidence: 99%

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Stefanovski

Meier

Pai

et al. 2021

Front. Neuroinform.

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Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

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The extraction of complex relationships and their conversion to biological expression language (BEL) overview of the BioCreative VI (2017) BEL track

Cited by 18 publications

References 33 publications

Measuring Adoption of Patient Priorities–Aligned Care Using Natural Language Processing of Electronic Health Records: Development and Validation of the Model

Measuring Adoption of Patient Priorities–Aligned Care Using Natural Language Processing of Electronic Health Records: Development and Validation of the Model

Extraction of causal relations based on SBEL and BERT model

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

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