AIMC Topic: Natural Language Processing

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Prognosis of p16 and Human Papillomavirus Discordant Oropharyngeal Cancers and the Exploration of Using Natural Language Processing to Analyze Free-Text Pathology Reports.

JCO clinical cancer informatics
PURPOSE: Treatment deintensification for human papillomavirus-positive (HPV+)-associated oropharyngeal cancer (OPC) has been the catalyst of experts worldwide. In situ hybridization is optimal to identify HPV+ OPC, but immunohistochemistry for its su...

Injury degree appraisal of large language model based on retrieval-augmented generation and deep learning.

International journal of law and psychiatry
Large Language Models (LLMs) have shown impressive performance in various natural language processing tasks. However, their application in specialized domains like forensic injury appraisal remains challenging due to the lack of domain-specific knowl...

Leveraging natural language processing for efficient information extraction from breast cancer pathology reports: Single-institution study.

PloS one
BACKGROUND: Pathology reports provide important information for accurate diagnosis of cancer and optimal treatment decision making. In particular, breast cancer has known to be the most common cancer in women worldwide.

Comparative ranking of marginal confounding impact of natural language processing-derived versus structured features in pharmacoepidemiology.

Computers in biology and medicine
OBJECTIVE: To explore the ability of natural language processing (NLP) methods to identify confounder information beyond what can be identified using claims codes alone for pharmacoepidemiology.

A series of natural language processing for predicting tumor response evaluation and survival curve from electronic health records.

BMC medical informatics and decision making
BACKGROUND: The clinical information housed within unstructured electronic health records (EHRs) has the potential to promote cancer research. The National Cancer Center Hospital (NCCH) is widely recognized as a leading institution for the treatment ...

De-identification of clinical notes with pseudo-labeling using regular expression rules and pre-trained BERT.

BMC medical informatics and decision making
BACKGROUND: De-identification of clinical notes is essential to utilize the rich information in unstructured text data in medical research. However, only limited work has been done in removing personal information from clinical notes in Korea.

Artificial intelligence and natural language processing for improved telemedicine: Before, during and after remote consultation.

Atencion primaria
The rapid evolution of telemedicine has revealed significant documentation and workflow challenges. Clinicians often struggle with the administrative burdens of telehealth visits, sacrificing valuable time better spent in direct patient interaction. ...

Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports.

Scientific reports
Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultrasound reports include structured and free text and vary across institutions, hampering attempts to min...

Machine learning tools match physician accuracy in multilingual text annotation.

Scientific reports
In the medical field, text annotation involves categorizing clinical and biomedical texts with specific medical categories, enhancing the organization and interpretation of large volumes of unstructured data. This process is crucial for developing to...

Transformer-based heart language model with electrocardiogram annotations.

Scientific reports
This paper explores the potential of transformer-based foundation models to detect Atrial Fibrillation (AFIB) in electrocardiogram (ECG) processing, an arrhythmia specified as an irregular heart rhythm without patterns. We construct a language with t...