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Found 13 papers, 4 papers with code
Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models
In this work, we propose a new NLP task that aims to generate a list of problems in a patient’s daily care plan using input from the provider’s progress notes during hospitalization.
Triplet-Trained Vector Space and Sieve-Based Search Improve Biomedical Concept Normalization
We propose a vector-space model for concept normalization, where mentions and concepts are encoded via transformer networks that are trained via a triplet objective with online hard triplet mining.
Sensing-assisted Robust SWIPT for Mobile Energy Harvesting Receivers
Simultaneous wireless information and power transfer (SWIPT) has been proposed to offer communication services and transfer power to the energy harvesting receiver (EHR) concurrently.
Joint BS Selection, User Association, and Beamforming Design for Network Integrated Sensing and Communication
Different from conventional radar, the cellular network in the integrated sensing and communication (ISAC) system enables collaborative sensing by multiple sensing nodes, e. g., base stations (BSs).
Energy-Aware Resource Allocation and Trajectory Design for UAV-Enabled ISAC
In this paper, we investigate joint resource allocation and trajectory design for multi-user multi-target unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC).
Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models
In this work, we propose a new NLP task that aims to generate a list of problems in a patient's daily care plan using input from the provider's progress notes during hospitalization.
A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing
Conclusions: The existing clinical NLP tasks cover a wide range of topics and the field will continue to grow and attract more attention from both general domain NLP and clinical informatics community.
BCH-NLP at BioCreative VII Track 3: medications detection in tweets using transformer networks and multi-task learning
In this paper, we present our work participating in the BioCreative VII Track 3 - automatic extraction of medication names in tweets, where we implemented a multi-task learning model that is jointly trained on text classification and sequence labelling.
A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization
Concept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is challenging because ontologies are large.
Multi-class Hierarchical Question Classification for Multiple Choice Science Exams
Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately.
Pre-trained Contextualized Character Embeddings Lead to Major Improvements in Time Normalization: a Detailed Analysis
Recent studies have shown that pre-trained contextual word embeddings, which assign the same word different vectors in different contexts, improve performance in many tasks.
SemEval 2018 Task 6: Parsing Time Normalizations
This paper presents the outcomes of the Parsing Time Normalization shared task held within SemEval-2018.
From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations
This paper presents the first model for time normalization trained on the SCATE corpus.
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