Search Results for author: Kirk Roberts

Found 46 papers, 4 papers with code

A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports

no code implementations LREC 2022 Surabhi Datta, Hio Cheng Lam, Atieh Pajouhi, Sunitha Mogalla, Kirk Roberts

This is one of the first attempts focusing on CDCR in the clinical domain and holds potential in benefiting physicians and clinical research through long-term tracking of radiology findings.

coreference-resolution Cross Document Coreference Resolution

RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports

no code implementations LREC 2022 Sarvesh Soni, Meghana Gudala, Atieh Pajouhi, Kirk Roberts

We present a radiology question answering dataset, RadQA, with 3074 questions posed against radiology reports and annotated with their corresponding answer spans (resulting in a total of 6148 question-answer evidence pairs) by physicians.

Question Answering Reading Comprehension

A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports

no code implementations EMNLP (SpLU) 2020 Surabhi Datta, Kirk Roberts

Radiology reports contain important clinical information about patients which are often tied through spatial expressions.

Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases

no code implementations25 Mar 2024 Yumeng Yang, Ashley Gilliam, Ethan B Ludmir, Kirk Roberts

Clinical trials are pivotal in medical research, and NLP can enhance their success, with application in recruitment.

Few-Shot Learning

Question Answering for Electronic Health Records: A Scoping Review of datasets and models

no code implementations12 Oct 2023 Jayetri Bardhan, Kirk Roberts, Daisy Zhe Wang

Because of the differences in data format and modality, this differs greatly from other medical QA tasks that employ medical websites or scientific papers to retrieve answers, making it critical to research EHR question answering.

Decision Making Question Answering

Text Classification of Cancer Clinical Trial Eligibility Criteria

no code implementations14 Sep 2023 Yumeng Yang, Soumya Jayaraj, Ethan B Ludmir, Kirk Roberts

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility is stated in natural language.

Language Modelling text-classification +1

Evaluation of AI Chatbots for Patient-Specific EHR Questions

no code implementations5 Jun 2023 Alaleh Hamidi, Kirk Roberts

This paper investigates the use of artificial intelligence chatbots for patient-specific question answering (QA) from clinical notes using several large language model (LLM) based systems: ChatGPT (versions 3. 5 and 4), Google Bard, and Claude.

Language Modelling Large Language Model +1

Application of an ontology for model cards to generate computable artifacts for linking machine learning information from biomedical research

no code implementations21 Mar 2023 Muhammad Amith, Licong Cui, Kirk Roberts, Cui Tao

Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc.

Toward a Neural Semantic Parsing System for EHR Question Answering

no code implementations8 Nov 2022 Sarvesh Soni, Kirk Roberts

Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR question answering (QA).

Question Answering Semantic Parsing

DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

1 code implementation LREC 2022 Jayetri Bardhan, Anthony Colas, Kirk Roberts, Daisy Zhe Wang

Our goal is to provide a benchmark dataset for multi-modal QA systems, and to open up new avenues of research in improving question answering over EHR structured data by using context from unstructured clinical data.

Question Answering Text-To-SQL

Deep learning-based NLP Data Pipeline for EHR Scanned Document Information Extraction

no code implementations14 Sep 2021 Enshuo Hsu, Ioannis Malagaris, Yong-Fang Kuo, Rizwana Sultana, Kirk Roberts

We also evaluated the combinations of image preprocessing methods (gray-scaling, dilate & erode, increased contrast by 20%, increased contrast by 60%), and two deep learning architectures (with and without structured input that provides document layout information).

Optical Character Recognition Optical Character Recognition (OCR) +1

Searching for Scientific Evidence in a Pandemic: An Overview of TREC-COVID

no code implementations19 Apr 2021 Kirk Roberts, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, Kyle Lo, Ian Soboroff, Ellen Voorhees, Lucy Lu Wang, William R Hersh

We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19.

Information Retrieval Retrieval

Three-level Hierarchical Transformer Networks for Long-sequence and Multiple Clinical Documents Classification

1 code implementation17 Apr 2021 Yuqi Si, Kirk Roberts

We present a Three-level Hierarchical Transformer Network (3-level-HTN) for modeling long-term dependencies across clinical notes for the purpose of patient-level prediction.

Document Classification General Classification +1

Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data

no code implementations24 Feb 2021 Yuqi Si, Elmer V Bernstam, Kirk Roberts

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing.

Representation Learning Transfer Learning

Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping

no code implementations10 Oct 2020 Surabhi Datta, Shekhar Khanpara, Roy F. Riascos, Kirk Roberts

Classifying fine-grained ischemic stroke phenotypes relies on identifying important clinical information.

Extracting Concepts for Precision Oncology from the Biomedical Literature

no code implementations30 Sep 2020 Nicholas Greenspan, Yuqi Si, Kirk Roberts

Finally, we propose additional directions for research for improving extraction performance and utilizing the NLP system in downstream precision oncology applications.

RadLex Normalization in Radiology Reports

no code implementations10 Sep 2020 Surabhi Datta, Jordan Godfrey-Stovall, Kirk Roberts

Further, no study to date has attempted to leverage RadLex for standardization.

Language Modelling

Patient Cohort Retrieval using Transformer Language Models

no code implementations10 Sep 2020 Sarvesh Soni, Kirk Roberts

Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks.

Feature Engineering Retrieval

An Evaluation of Two Commercial Deep Learning-Based Information Retrieval Systems for COVID-19 Literature

no code implementations6 Jul 2020 Sarvesh Soni, Kirk Roberts

This has led to both corpora for biomedical articles related to COVID-19 (such as the CORD-19 corpus (Wang et al., 2020)) as well as search engines to query such data.

Information Retrieval Retrieval

Towards an Ontology-based Medication Conversational Agent for PrEP and PEP

no code implementations WS 2020 Muhammad Amith, Licong Cui, Kirk Roberts, Cui Tao

ABSTRACT: HIV (human immunodeficiency virus) can damage a human{'}s immune system and cause Acquired Immunodeficiency Syndrome (AIDS) which could lead to severe outcomes, including death.

TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection

no code implementations9 May 2020 Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, Lucy Lu Wang

TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic.

Information Retrieval Retrieval

Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering

no code implementations LREC 2020 Sarvesh Soni, Kirk Roberts

We evaluate the performance of various Transformer language models, when pre-trained and fine-tuned on different combinations of open-domain, biomedical, and clinical corpora on two clinical question answering (QA) datasets (CliCR and emrQA).

Machine Reading Comprehension Question Answering

Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports

no code implementations LREC 2020 Surabhi Datta, Morgan Ulinski, Jordan Godfrey-Stovall, Shekhar Khanpara, Roy F. Riascos-Castaneda, Kirk Roberts

The framework is adopted from the existing SpatialNet representation in the general domain with the aim to generate more accurate representations of spatial language used by radiologists.

Extraction of Lactation Frames from Drug Labels and LactMed

no code implementations WS 2019 Heath Goodrum, Meghana Gudala, Ankita Misra, Kirk Roberts

This paper describes a natural language processing (NLP) approach to extracting lactation-specific drug information from two sources: FDA-mandated drug labels and the NLM Drugs and Lactation Database (LactMed).

A Paraphrase Generation System for EHR Question Answering

no code implementations WS 2019 Sarvesh Soni, Kirk Roberts

This paper proposes a dataset and method for automatically generating paraphrases for clinical questions relating to patient-specific information in electronic health records (EHRs).

Decoder Paraphrase Generation +1

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

no code implementations2 Apr 2019 Surabhi Datta, Elmer V Bernstam, Kirk Roberts

Conclusion: The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes.

Enhancing Clinical Concept Extraction with Contextual Embeddings

no code implementations22 Feb 2019 Yuqi Si, Jingqi Wang, Hua Xu, Kirk Roberts

We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings, and compare these on four concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015.

Clinical Concept Extraction Language Modelling +2

Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP

no code implementations WS 2016 Kirk Roberts

The proliferation of deep learning methods in natural language processing (NLP) and the large amounts of data they often require stands in stark contrast to the relatively data-poor clinical NLP domain.

Word Embeddings

Annotating Logical Forms for EHR Questions

no code implementations LREC 2016 Kirk Roberts, Dina Demner-Fushman

From these, 468 specific questions are found containing 259 unique medical concepts and requiring 53 unique predicates to represent the logical forms.

Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

1 code implementation CVPR 2016 Hoo-chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers

Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features.

Annotating Spatial Containment Relations Between Events

no code implementations LREC 2012 Kirk Roberts, Travis Goodwin, S Harabagiu, a M.

Events form complex predicate-argument structures that model the participants in the event, their roles, as well as the temporal and spatial grounding.

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