AIMC Topic: Electronic Health Records

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Use of natural language processing method to identify regional anesthesia from clinical notes.

Regional anesthesia and pain medicine
INTRODUCTION: Accurate data capture is integral for research and quality improvement efforts. Unfortunately, limited guidance for defining and documenting regional anesthesia has resulted in wide variation in documentation practices, even within indi...

Enhancing diagnosis prediction with adaptive disease representation learning.

Artificial intelligence in medicine
Diagnosis prediction predicts which diseases a patient is most likely to suffer from in the future based on their historical electronic health records. The time series model can better capture the temporal progression relationship of patient diseases...

Performance Improvement of a Natural Language Processing Tool for Extracting Patient Narratives Related to Medical States From Japanese Pharmaceutical Care Records by Increasing the Amount of Training Data: Natural Language Processing Analysis and Validation Study.

JMIR medical informatics
BACKGROUND: Patients' oral expressions serve as valuable sources of clinical information to improve pharmacotherapy. Natural language processing (NLP) is a useful approach for analyzing unstructured text data, such as patient narratives. However, few...

Using a Longformer Large Language Model for Segmenting Unstructured Cancer Pathology Reports.

JCO clinical cancer informatics
PURPOSE: Many Natural Language Processing (NLP) methods achieve greater performance when the input text is preprocessed to remove extraneous or unnecessary text. A technique known as text segmentation can facilitate this step by isolating key section...

Physician Perspectives on Ambient AI Scribes.

JAMA network open
IMPORTANCE: Limited qualitative studies exist evaluating ambient artificial intelligence (AI) scribe tools. Such studies can provide deeper insights into ambient AI implementations by capturing lived experiences.

An interpretable machine learning approach for detecting psoriatic arthritis in a UK primary care psoriasis cohort using electronic health records from the Clinical Practice Research Datalink.

Annals of the rheumatic diseases
OBJECTIVES: Develop an interpretable machine learning model to detect patients with newly diagnosed psoriatic arthritis (PsA) in a cohort of psoriasis patients and identify important clinical indicators of PsA in primary care.

Feature-Based Audiogram Value Estimator (FAVE): Estimating Numerical Thresholds from Scanned Images of Handwritten Audiograms.

Journal of medical systems
Hearing loss is a public health concern that affects millions of people globally. Clinically, a person's hearing sensitivity is often measured using pure-tone audiometry and visualized as a pure-tone audiogram, a plot of hearing sensitivity as a func...

ChatGPT in Oncology Diagnosis and Treatment: Applications, Legal and Ethical Challenges.

Current oncology reports
PURPOSE OF REVIEW: This study aims to systematically review the trajectory of artificial intelligence (AI) development in the medical field, with a particular emphasis on ChatGPT, a cutting-edge tool that is transforming oncology's diagnosis and trea...

Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study.

JMIR aging
BACKGROUND: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Knowledge about the path from initial memory loss complaints to dementia diagnosis remains limited.

CECRel: A joint entity and relation extraction model for Chinese electronic medical records of coronary angiography via contrastive learning.

Journal of biomedical informatics
Entity and relation extraction from Chinese electronic medical records (EMRs) is a crucial foundation for constructing medical knowledge graphs and supporting downstream tasks. Chinese EMRs face challenges in accurately extracting medical entity rela...