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Documentation of Nursing Diagnosis

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Formulating and Validating Nursing Diagnosis II

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Related Experiment Video

Updated: May 17, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Knowledge acquisition for medical diagnosis using collective intelligence.

G Hernández-Chan¹, A Rodríguez-González, G Alor-Hernández

¹Computer Science Department, Universidad Carlos III de Madrid, Leganés, Madrid, Spain. gandhisamuel.hernandez@alumnos.uc3m.es

Journal of Medical Systems

|October 24, 2012

Summary

This summary is machine-generated.

This study introduces a novel schema for collecting diagnosis information in Diagnosis Decision Support Systems (DDSS). It leverages the wisdom of crowds (WOC) and consensus methods for enhanced biomedical knowledge acquisition.

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Last Updated: May 17, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Area of Science:

Biomedical informatics
Artificial intelligence in healthcare
Collective intelligence

Background:

The wisdom of crowds (WOC) aggregates group opinions for decision-making.
Applying WOC to biomedical knowledge acquisition is an emerging trend.
Current Diagnosis Decision Support Systems (DDSS) can benefit from improved data collection.

Purpose of the Study:

To propose a new schema for collecting diagnostic information within DDSS.
To utilize collective intelligence and consensus mechanisms for data acquisition.
To advance the field of biomedical knowledge acquisition through WOC principles.

Main Methods:

Development of a novel schema for data collection in DDSS.
Implementation of collective intelligence techniques.
Application of consensus methods to aggregate diagnostic information.

Main Results:

A functional schema for collecting diagnosis information using WOC was demonstrated.
The proposed system effectively utilizes collective intelligence for data gathering.
Consensus methods were successfully applied to refine diagnostic information.

Conclusions:

The developed schema offers a promising approach for enhancing DDSS.
Wisdom of crowds techniques can be effectively applied to biomedical knowledge acquisition.
This work contributes to the advancement of intelligent systems in healthcare diagnostics.