AIMC Topic: Natural Language Processing

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Parsable Clinical Trial Eligibility Criteria Representation Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Successful clinical trials offer better treatments to current or future patients and advance scientific research. Clinical trials define the target population using specific eligibility criteria to ensure an optimal enrollment sample. Clinical trial ...

Identifying Barriers to Post-Acute Care Referral and Characterizing Negative Patient Preferences Among Hospitalized Older Adults Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Our objective was to detect common barriers to post-acute care (B2PAC) among hospitalized older adults using natural language processing (NLP) of clinical notes from patients discharged home when a clinical decision support system recommended post-ac...

MERIT: inimal Suprvision Through Label Augmentation for Biomedical Exraction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Relation Extraction (RE) is an important task in extracting structured data from free biomedical text. Obtaining labeled data needed to train RE models in specialized domains such as biomedicine can be very expensive because it requires expert knowle...

Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a part of entity representations. In this paper, we exploit additional evidence by also making use of ....

PathologyBERT - Pre-trained Vs. A New Transformer Language Model for Pathology Domain.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions. However, successful text mining of a large pathology database can play a critical role to advance 'big data' cancer ...

HealthPrompt: A Zero-shot Learning Paradigm for Clinical Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Developing clinical natural language systems based on machine learning and deep learning is dependent on the availability of large-scale annotated clinical text datasets, most of which are time-consuming to create and not publicly available. The lack...

Towards User-centered Corpus Development: Lessons Learnt from Designing and Developing MedTator.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A gold standard annotated corpus is usually indispensable when developing natural language processing (NLP) systems. Building a high-quality annotated corpus for clinical NLP requires considerable time and domain expertise during the annotation proce...

[Feasibility Study of the Prediction of Radiologist's Instructions with the Bi-LSTM Model Trained with Descriptions of MR Imaging Order-statement].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: Magnetic resonance (MR) images provide essential diagnostic information; however, it is also a very burdensome examination for patients. At our hospital, radiologists make imaging instructions for all MR examination orders, but this is a tim...

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health.

Frontiers in public health
Large Language Models (LLMs) have recently gathered attention with the release of ChatGPT, a user-centered chatbot released by OpenAI. In this perspective article, we retrace the evolution of LLMs to understand the revolution brought by ChatGPT in th...