CREATe: Clinical Report Extraction and Annotation Technology
- URL: http://arxiv.org/abs/2103.00562v1
- Date: Sun, 28 Feb 2021 16:50:14 GMT
- Title: CREATe: Clinical Report Extraction and Annotation Technology
- Authors: Yichao Zhou, Wei-Ting Chen, Bowen Zhang, David Lee, J. Harry Caufield,
Kai-Wei Chang, Yizhou Sun, Peipei Ping and Wei Wang
- Abstract summary: Clinical case reports are written descriptions of the unique aspects of a particular clinical case.
There has been no attempt to develop an end-to-end system to annotate, index, or otherwise curate these reports.
We propose a novel computational resource platform, CREATe, for extracting, indexing, and querying the contents of clinical case reports.
- Score: 53.731999072534876
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Clinical case reports are written descriptions of the unique aspects of a
particular clinical case, playing an essential role in sharing clinical
experiences about atypical disease phenotypes and new therapies. However, to
our knowledge, there has been no attempt to develop an end-to-end system to
annotate, index, or otherwise curate these reports. In this paper, we propose a
novel computational resource platform, CREATe, for extracting, indexing, and
querying the contents of clinical case reports. CREATe fosters an environment
of sustainable resource support and discovery, enabling researchers to overcome
the challenges of information science. An online video of the demonstration can
be viewed at https://youtu.be/Q8owBQYTjDc.
Related papers
- Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinement [9.347971487478038]
This paper develops a novel vision-language representation learning framework by proposing a key semantic knowledge-emphasized report refinement method.
Our framework surpasses seven state-of-the-art methods in both fine-tuning and zero-shot settings.
arXiv Detail & Related papers (2024-01-21T07:57:04Z) - Radiology Report Generation Using Transformers Conditioned with
Non-imaging Data [55.17268696112258]
This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information.
The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
arXiv Detail & Related papers (2023-11-18T14:52:26Z) - CARE: Extracting Experimental Findings From Clinical Literature [29.763929941107616]
This work presents CARE, a new IE dataset for the task of extracting clinical findings.
We develop a new annotation schema capturing fine-grained findings as n-ary relations between entities and attributes.
We collect extensive annotations for 700 abstracts from two sources: clinical trials and case reports.
arXiv Detail & Related papers (2023-11-16T10:06:19Z) - Investigating Alternative Feature Extraction Pipelines For Clinical Note
Phenotyping [0.0]
Using computational systems for the extraction of medical attributes offers many applications.
BERT-based models can be used to transform clinical notes into a series of representations.
We propose an alternative pipeline utilizing ScispaCyNeumann for extraction of common diseases.
arXiv Detail & Related papers (2023-10-05T02:51:51Z) - Enhancing Clinical Evidence Recommendation with Multi-Channel
Heterogeneous Learning on Evidence Graphs [4.672216806648563]
The goal of recommending clinical evidence is to provide medical practitioners with relevant information to support their decision-making processes.
The direct connections between certain clinical problems and related evidence are often sparse, creating a challenge of link sparsity.
To address these challenges, we define two knowledge graphs: an Evidence Co-reference Graph and an Evidence Text Graph.
arXiv Detail & Related papers (2023-04-03T12:15:53Z) - Informing clinical assessment by contextualizing post-hoc explanations
of risk prediction models in type-2 diabetes [50.8044927215346]
We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state.
We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability.
Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
arXiv Detail & Related papers (2023-02-11T18:07:11Z) - Cross-Lingual Knowledge Transfer for Clinical Phenotyping [55.92262310716537]
We investigate cross-lingual knowledge transfer strategies to execute this task for clinics that do not use the English language.
We evaluate these strategies for a Greek and a Spanish clinic leveraging clinical notes from different clinical domains.
Our results show that using multilingual data overall improves clinical phenotyping models and can compensate for data sparseness.
arXiv Detail & Related papers (2022-08-03T08:33:21Z) - VBridge: Connecting the Dots Between Features, Explanations, and Data
for Healthcare Models [85.4333256782337]
VBridge is a visual analytics tool that seamlessly incorporates machine learning explanations into clinicians' decision-making workflow.
We identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the need for cohort-level evidence.
We demonstrated the effectiveness of VBridge through two case studies and expert interviews with four clinicians.
arXiv Detail & Related papers (2021-08-04T17:34:13Z) - Variational Topic Inference for Chest X-Ray Report Generation [102.04931207504173]
Report generation for medical imaging promises to reduce workload and assist diagnosis in clinical practice.
Recent work has shown that deep learning models can successfully caption natural images.
We propose variational topic inference for automatic report generation.
arXiv Detail & Related papers (2021-07-15T13:34:38Z) - Toward Understanding Clinical Context of Medication Change Events in
Clinical Narratives [0.4270213395622267]
We present the Contextualized Medication Event dataset (CMED), a dataset for capturing relevant context of medication changes documented in clinical notes.
CMED consists of 9,013 medication mentions annotated over 500 clinical notes, and will be released to the community as a shared task in 2021.
arXiv Detail & Related papers (2020-11-17T18:55:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.