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Found 10 papers, 4 papers with code
Clinical Prompt Learning with Frozen Language Models
Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown impressive performance on a number of natural language tasks with common benchmarking text datasets in full, few-shot, and zero-shot train-evaluation setups.
Rationale production to support clinical decision-making
In this work, we apply InfoCal, the current state-of-the-art model that produces extractive rationales for its predictions, to the task of predicting hospital readmission using hospital discharge notes.
Population Gradients improve performance across data-sets and architectures in object classification
The most successful methods such as ReLU transfer functions, batch normalization, Xavier initialization, dropout, learning rate decay, or dynamic optimizers, have become standards in the field due, particularly, to their ability to increase the performance of Neural Networks (NNs) significantly and in almost all situations.
An efficient representation of chronological events in medical texts
In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs).
Med7: a transferable clinical natural language processing model for electronic health records
In this work we introduced a named-entity recognition model for clinical natural language processing.
Medical Named Entity Recognition
named-entity-recognition
+4
Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture
Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery.
Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks
In our study, we develop an approach that solves these problems for named entity recognition, obtaining 94. 6 F1 score in I2B2 2009 Medical Extraction Challenge [6], 4. 3 above the architecture that won the competition.
Few-shot Learning for Named Entity Recognition in Medical Text
Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 2017).
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model
Recurrent major mood episodes and subsyndromal mood instability cause substantial disability in patients with bipolar disorder.
A Primer on the Signature Method in Machine Learning
We have chosen to focus in detail on the principle properties of the signature which we believe are fundamental to understanding its role in applications.
