Search Results for author:
Found 52 papers, 7 papers with code
Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition
Abstract This article describes a simple PCFG induction model with a fixed category domain that predicts a large majority of attested constituent boundaries, and predicts labels consistent with nearly half of attested constituent labels on a standard evaluation data set of child-directed speech.
EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain
We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representation of biomedical terminology (PubMedBERT) on a clinical corpus along with a novel entity-centric masking strategy to infuse domain knowledge in the learning process.
Ensemble-based Fine-Tuning Strategy for Temporal Relation Extraction from the Clinical Narrative
In this paper, we investigate ensemble methods for fine-tuning transformer-based pretrained models for clinical natural language processing tasks, specifically temporal relation extraction from the clinical narrative.
Defining and Learning Refined Temporal Relations in the Clinical Narrative
We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative.
Incorporating Risk Factor Embeddings in Pre-trained Transformers Improves Sentiment Prediction in Psychiatric Discharge Summaries
Reducing rates of early hospital readmission has been recognized and identified as a key to improve quality of care and reduce costs.
Extracting Relations between Radiotherapy Treatment Details
We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records.
Domain adaptation in practice: Lessons from a real-world information extraction pipeline
Advances in transfer learning and domain adaptation have raised hopes that once-challenging NLP tasks are ready to be put to use for sophisticated information extraction needs.
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.
Identifying Task Groupings for Multi-Task Learning Using Pointwise V-Usable Information
We propose a metric of task relatedness based on task difficulty measured by pointwise V-usable information (PVI).
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
Generative models have been showing potential for producing data in mass.
The impact of responding to patient messages with large language model assistance
Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously.
Leveraging A Medical Knowledge Graph into Large Language Models for Diagnosis Prediction
In this paper, we outline an innovative approach for augmenting the proficiency of LLMs in the realm of automated diagnosis generation, achieved through the incorporation of a medical knowledge graph (KG) and a novel graph model: Dr. Knows, inspired by the clinical diagnostic reasoning process.
Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023.
Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning
Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors.
Progress Note Understanding -- Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 Shared Task
The goal of the task was to identify and prioritize diagnoses as the first steps in diagnostic decision support to find the most relevant information in long documents like daily progress notes.
DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing
The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce the cognitive burden and medical error has not been investigated.
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.
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding
This work introduces a hierarchical annotation schema with three stages to address clinical text understanding, clinical reasoning, and summarization.
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.
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic Processing
Participants are then tested on data representing a new (target) domain.
Classifying Long Clinical Documents with Pre-trained Transformers
Automatic phenotyping is a task of identifying cohorts of patients that match a predefined set of criteria.
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
We show the importance and feasibility of learning comprehensive representations of patient EHR data through a systematic review.
A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction
Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text.
Methods for Extracting Information from Messages from Primary Care Providers to Specialists
Electronic consult (eConsult) systems allow specialists more flexibility to respond to referrals more efficiently, thereby increasing access in under-resourced healthcare settings like safety net systems.
Cross-document coreference: An approach to capturing coreference without context
This paper discusses a cross-document coreference annotation schema that was developed to further automatic extraction of timelines in the clinical domain.
Two-stage Federated Phenotyping and Patient Representation Learning
A large percentage of medical information is in unstructured text format in electronic medical record systems.
Unsupervised Learning of PCFGs with Normalizing Flow
This paper describes a neural PCFG inducer which employs context embeddings (Peters et al., 2018) in a normalizing flow model (Dinh et al., 2015) to extend PCFG induction to use semantic and morphological information.
Simplified Neural Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) is the task of training a statistical model on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain.
Extracting Adverse Drug Event Information with Minimal Engineering
In this paper we describe an evaluation of the potential of classical information extraction methods to extract drug-related attributes, including adverse drug events, and compare to more recently developed neural methods.
FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos.
Self-training improves Recurrent Neural Networks performance for Temporal Relation Extraction
Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance.
Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction
There have been several recent attempts to improve the accuracy of grammar induction systems by bounding the recursive complexity of the induction model (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016; Jin et al., 2018).
Spotting Spurious Data with Neural Networks
Automatic identification of spurious instances (those with potentially wrong labels in datasets) can improve the quality of existing language resources, especially when annotations are obtained through crowdsourcing or automatically generated based on coded rankings.
Learning Patient Representations from Text
Mining electronic health records for patients who satisfy a set of predefined criteria is known in medical informatics as phenotyping.
Unsupervised Grammar Induction with Depth-bounded PCFG
There has been recent interest in applying cognitively or empirically motivated bounds on recursion depth to limit the search space of grammar induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al., 2016).
Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks
We present a novel approach for training artificial neural networks.
Unsupervised Domain Adaptation for Clinical Negation Detection
Detecting negated concepts in clinical texts is an important part of NLP information extraction systems.
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing.
Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input
This paper presents a new memory-bounded left-corner parsing model for unsupervised raw-text syntax induction, using unsupervised hierarchical hidden Markov models (UHHMM).
Temporal Annotation in the Clinical Domain
The corpus is available to the community and has been proposed for use in a SemEval 2015 task.
