AIMC Topic: Natural Language Processing

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Leveraging large language models for knowledge-free weak supervision in clinical natural language processing.

Scientific reports
The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily available or affordable. Weak supervision and in-context learning offer partia...

Position-context additive transformer-based model for classifying text data on social media.

Scientific reports
In recent years, the continuous increase in the growth of text data on social media has been a major reason to rely on the pre-training method to develop new text classification models specially transformer-based models that have proven worthwhile in...

Integration of large-scale community-developed causal loop diagrams: a Natural Language Processing approach to merging factors based on semantic similarity.

BMC public health
BACKGROUND: Complex public health problems have been addressed in communities through systems thinking and participatory methods like Group Model Building (GMB) and Causal Loop Diagrams (CLDs) albeit with some challenges. This study aimed to explore ...

A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations.

Nature human behaviour
This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals acr...

NLP modeling recommendations for restricted data availability in clinical settings.

BMC medical informatics and decision making
BACKGROUND: Clinical decision-making in healthcare often relies on unstructured text data, which can be challenging to analyze using traditional methods. Natural Language Processing (NLP) has emerged as a promising solution, but its application in cl...

Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.

BMC medical informatics and decision making
BACKGROUND: Clinical machine learning research and artificial intelligence driven clinical decision support models rely on clinically accurate labels. Manually extracting these labels with the help of clinical specialists is often time-consuming and ...

NLP-driven integration of electrophysiology and traditional Chinese medicine for enhanced diagnostics and management of postpartum pain.

SLAS technology
Postpartum pain encompasses a range of physical and emotional discomforts, often influenced by hormonal changes, physical recovery, and individual psychological states. The complex interactions between the variables can make it difficult for traditio...

Advanced NLP-driven predictive modeling for tailored treatment strategies in gastrointestinal cancer.

SLAS technology
Gastrointestinal cancer represents a significant health burden, necessitating innovative approaches for personalized treatment. This study aims to develop an advanced natural language processing (NLP)-driven predictive modeling framework for tailored...

Development of a natural language processing algorithm to extract social determinants of health from clinician notes.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Disparities in access to the organ transplant waitlist are well-documented, but research into modifiable factors has been limited due to a lack of access to organized prewaitlisting data. This study aimed to develop a natural language processing (NLP...