AIMC Topic: Humans

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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 ....

Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and scalable deep learning-based solution.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Federated learning (FL) is a privacy preserving approach to learning that overcome issues related to data access, privacy, and security, which represent key challenges in the healthcare sector. FL enables hospitals to collaboratively learn a shared p...

Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning ...

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...

Can ChatGPT Accurately Answer a PICOT Question? Assessing AI Response to a Clinical Question.

Nurse educator
BACKGROUND: ChatGPT, an artificial intelligence (AI) text generator trained to predict correct words, can provide answers to questions but has shown mixed results in answering medical questions.

It's Not Only What You Say, But Also How You Say It: Machine Learning Approach to Estimate Trust from Conversation.

Human factors
OBJECTIVE: The objective of this study was to estimate trust from conversations using both lexical and acoustic data.

Efficacy of Robot-Assisted and Virtual Reality Interventions on Balance, Gait, and Daily Function in Patients With Stroke: A Systematic Review and Network Meta-analysis.

Archives of physical medicine and rehabilitation
OBJECTIVE: This study aimed to evaluate the comparative effectiveness and ranking of robot-assisted training, virtual reality, and robot-assisted rehabilitation combined with virtual reality in improving balance, gait, and daily function in patients ...