Shifting machine learning for healthcare from development to deployment and from models to data
In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …
the automation of physician tasks as well as enhancements in clinical capabilities and …
Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …
Domain-specific language model pretraining for biomedical natural language processing
Pretraining large neural language models, such as BERT, has led to impressive gains on
many natural language processing (NLP) tasks. However, most pretraining efforts focus on …
many natural language processing (NLP) tasks. However, most pretraining efforts focus on …
Publicly available clinical BERT embeddings
Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et
al., 2018) have dramatically improved performance for many natural language processing …
al., 2018) have dramatically improved performance for many natural language processing …
Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets
Inspired by the success of the General Language Understanding Evaluation benchmark, we
introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to …
introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to …
Clinicalbert: Modeling clinical notes and predicting hospital readmission
Clinical notes contain information about patients that goes beyond structured data like lab
values and medications. However, clinical notes have been underused relative to structured …
values and medications. However, clinical notes have been underused relative to structured …
Pretrained language models for biomedical and clinical tasks: understanding and extending the state-of-the-art
A large array of pretrained models are available to the biomedical NLP (BioNLP) community.
Finding the best model for a particular task can be difficult and time-consuming. For many …
Finding the best model for a particular task can be difficult and time-consuming. For many …
CharacterBERT: Reconciling ELMo and BERT for word-level open-vocabulary representations from characters
Due to the compelling improvements brought by BERT, many recent representation models
adopted the Transformer architecture as their main building block, consequently inheriting …
adopted the Transformer architecture as their main building block, consequently inheriting …
Pre-trained language models in biomedical domain: A systematic survey
Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …
language processing tasks. This also benefits the biomedical domain: researchers from …
Challenges and opportunities beyond structured data in analysis of electronic health records
M Tayefi, P Ngo, T Chomutare… - Wiley …, 2021 - Wiley Online Library
Electronic health records (EHR) contain a lot of valuable information about individual
patients and the whole population. Besides structured data, unstructured data in EHRs can …
patients and the whole population. Besides structured data, unstructured data in EHRs can …