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Found 15 papers, 6 papers with code
Are we there yet? Exploring clinical domain knowledge of BERT models
We explore whether state-of-the-art BERT models encode sufficient domain knowledge to correctly perform domain-specific inference.
Contextual explanation rules for neural clinical classifiers
For evaluation of explanations, we create a synthetic sepsis-identification dataset, as well as apply our technique on additional clinical and sentiment analysis datasets.
Updating the Minimum Information about CLinical Artificial Intelligence (MI-CLAIM) checklist for generative modeling research
In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the "Minimum information about clinical artificial intelligence modeling" (MI-CLAIM) checklist.
A comparative study of zero-shot inference with large language models and supervised modeling in breast cancer pathology classification
In this study, we explored whether recent LLMs can reduce the need for large-scale data annotations.
CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference
Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented in clinical notes.
Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes
Our findings indicate that significant disparities exist among breast cancer patients receiving different types of therapies based on social determinants of health.
Cross-institution text mining to uncover clinical associations: a case study relating social factors and code status in intensive care medicine
Confirmation is needed with better text mining models, ideally on a larger manually labeled dataset.
Topic Modeling on Clinical Social Work Notes for Exploring Social Determinants of Health Factors
We used word frequency analysis and Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion.
Developing a general-purpose clinical language inference model from a large corpus of clinical notes
We sought to evaluate the impact of using a domain-specific vocabulary and a large clinical training corpus on the performance of these language models in clinical language inference.
Distilling neural networks into skipgram-level decision lists
For evaluation of explanations, we create a synthetic sepsis-identification dataset, as well as apply our technique on additional clinical and sentiment analysis datasets.
Why can't memory networks read effectively?
Memory networks have been a popular choice among neural architectures for machine reading comprehension and question answering.
Revisiting neural relation classification in clinical notes with external information
Recently, segment convolutional neural networks have been proposed for end-to-end relation extraction in the clinical domain, achieving results comparable to or outperforming the approaches with heavy manual feature engineering.
Rule induction for global explanation of trained models
We find that the output rule-sets can explain the predictions of a neural network trained for 4-class text classification from the 20 newsgroups dataset to a macro-averaged F-score of 0. 80.
Patient representation learning and interpretable evaluation using clinical notes
We compare the model performance of the feature set constructed from a bag of words to that obtained from medical concepts.
Unsupervised patient representations from clinical notes with interpretable classification decisions
To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model.
