Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

The nursing process provides a clinical decision-making framework for patients and families to establish and implement a personalized care plan. Since part of the nurse's duties is to teach patients, the steps of the nursing process are the most effective way to approach instruction. The nursing process and the teaching-learning process are inextricably linked.
It is critical to determine the patient's learning needs during the assessment. Determination of learning needs compounds data...

Clinical Trials: Overview

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...

Nursing Clinical Information System

Nursing Clinical Information System

Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:

Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Systematic common and rare variant association testing in 392,030 whole genomes in <i>All of Us</i>.

medRxiv : the preprint server for health sciences·2026

Same author

Multi-scale data improves performance of machine learning model for long COVID identification.

Communications medicine·2026

Same author

Downward bias in the association between APOE and Alzheimer's disease using prevalent and by-proxy disease sampling in the All of Us research program.

BMC medical genomics·2026

Same author

Genetic Prediction of Circulating Lipoprotein(a) Levels in Diverse Populations.

medRxiv : the preprint server for health sciences·2026

Same author

11 million days of longitudinal wearable data reveal novel future health insights.

medRxiv : the preprint server for health sciences·2026

Same author

A one-shot, lossless algorithm for cross-cohort learning in mixed-outcomes analysis.

Patterns (New York, N.Y.)·2025

Same journal

Causal intervention validation of gene regulatory signals in scGPT.

Journal of biomedical informatics·2026

Same journal

CoAff-DTI: Fine-grained drug-target interaction prediction using pre-trained language models and affinity-guided mechanisms.

Journal of biomedical informatics·2026

Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026

Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026

Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026

Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 10, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Using natural language processing to provide personalized learning opportunities from trainee clinical notes.

Joshua C Denny¹, Anderson Spickard¹, Peter J Speltz²

¹Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, United States; Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, United States.

Journal of Biomedical Informatics

|June 14, 2015

Summary

This summary is machine-generated.

A novel electronic advisor system uses natural language processing (NLP) to identify geriatric medicine competencies like advance directives (AD) and altered mental status (AMS) from student clinical notes. This system shows promise for scalable competency assessment and targeted education in medical training.

Keywords:

Advanced directives Altered mental status Decision support Geriatric education Medical education Natural language processing

More Related Videos

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Apr 10, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Medical Education Technology
Natural Language Processing in Healthcare
Geriatric Medicine Training

Background:

Competency-based assessment is standard in medical education.
Identifying specific learning opportunities from clinical notes is challenging.

Purpose of the Study:

To develop and evaluate a novel electronic advisor system using NLP.
To identify two geriatric medicine competencies: advance directives (AD) and altered mental status (AMS) from medical student clinical notes.
To deliver customized learning materials based on identified competencies.

Main Methods:

Third-year medical student clinical notes were processed using NLP to identify AD and AMS.
An electronic advisor system generated email alerts with supplemental learning materials.
Physician review evaluated the precision and recall of the NLP system.
Student knowledge was assessed via pre- and post-tests.

Main Results:

The system successfully identified AD and AMS competencies with high precision (100% for AD, 93% for AMS) and recall (69% for AD, 100% for AMS).
54 out of 66 students received 393 email alerts, prompting a small change in student behavior regarding advance directives.
Students accessed educational links 34 times, indicating engagement with the provided material.
No significant difference was observed in pre-test (62%) and post-test (60%) scores.

Conclusions:

The NLP-based electronic advisor system effectively identified educational opportunities in clinical notes.
The system demonstrated a capacity to influence student behavior and offers a scalable model for competency assessment.
This technology can deliver targeted educational interventions to medical trainees.