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 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:

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...

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

You might also read

Related Articles

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

Sort by

Same author

Sentiment Analysis of Naturalistic Speech Using Open-Weight Large Language Models.

Affective science·2026

Same author

Stabilizing fractional dynamical networks suppresses epileptic seizures.

Scientific reports·2026

Same author

The Genetic and Environmental Architecture of the Human Functional Connectome.

ArXiv·2026

Same author

Development of Alzheimer's disease risk score for future integrated primary care: a white-box approach.

Frontiers in aging neuroscience·2026

Same author

Testing affect regulation theories of dreaming.

Sleep·2026

Same author

Shifts in brain dynamics and drivers of consciousness state transitions.

Frontiers in computational neuroscience·2026

Same journal

A digital marker for stratifying cardiovascular metabolic comorbidities among the middle-aged and elderly adults.

PLOS digital health·2026

Same journal

User engagement in the tuberculosis treatment support tools intervention and its impact on treatment outcomes: A secondary analysis of a pragmatic trial.

PLOS digital health·2026

Same journal

Machine learning for risk stratification of hypertensive disorders of pregnancy: Enhancing clinical efficiency in low-resource antenatal care in Tanzania.

PLOS digital health·2026

Same journal

The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study.

PLOS digital health·2026

Same journal

Time-series prediction of adverse birth outcomes in the U.S. using multilayer perceptron neural networks.

PLOS digital health·2026

Same journal

Exploring attitudes and acceptance of artificial intelligence in multiple sclerosis from the patient perspective.

PLOS digital health·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Individualized machine-learning-based clinical assessment recommendation system.

Devin Setiawan¹, Yumiko Wiranto², Jeffrey M Girard²

¹Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, Kansas, United States of America.

PLOS Digital Health

|September 25, 2025

Summary

This summary is machine-generated.

This study introduces the Individualized Clinical Assessment Recommendation System (iCARE), a machine learning framework that enhances diagnostic accuracy by personalizing feature selection for patients. iCARE improves predictions when individual patient data offers unique insights.

More Related Videos

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

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: Jan 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

Machine Learning
Clinical Decision Support
Personalized Medicine

Background:

Traditional clinical assessments lack individualization, potentially missing crucial early diagnostic insights.
Standardized procedures may not cater to diverse patient needs, especially in early disease stages.
Personalized diagnosis can significantly benefit patient outcomes.

Purpose of the Study:

To develop a machine learning framework for individualized feature selection in clinical assessments.
To enhance diagnostic accuracy by tailoring feature selection to individual patient characteristics.
To address the individualized feature addition problem for improved clinical decision-making.

Main Methods:

The Individualized Clinical Assessment Recommendation System (iCARE) was developed.
iCARE utilizes locally weighted logistic regression and Shapley Additive Explanations (SHAP) for personalized feature selection.
Performance was evaluated on synthetic and real-world datasets (diabetes risk, heart failure) and compared against a Global approach using accuracy and AUC metrics.

Main Results:

iCARE significantly enhanced predictive accuracy and AUC in datasets where features had distinct predictive capabilities (e.g., synthetic datasets 1-3, early diabetes).
For synthetic dataset 1, iCARE achieved 0.999 accuracy and 1.000 AUC, vastly outperforming the Global approach (0.689 accuracy, 0.639 AUC).
Improvements of 6-12% in accuracy and AUC were observed for iCARE in the early diabetes and heart disease dataset compared to other methods.

Conclusions:

The iCARE framework effectively provides personalized feature recommendations, enhancing diagnostic accuracy in critical scenarios.
iCARE improves the precision and effectiveness of medical diagnoses by leveraging individualized patient data.
The system demonstrates value when patient characteristics offer unique predictive insights, supporting tailored clinical assessments.