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Health Information Technology and Healthcare Information System

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:

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 and...

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Related Experiment Video

Updated: May 29, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Optimal asymmetrical SVM using pattern search. A health care application.

Gilles Cohen¹, Rodolphe Meyer

¹Direction of Medico Economic Analysis, University Hospital of Geneva, 1211 Geneva, Switzerland. gilles.cohen@hcuge.ch

Studies in Health Technology and Informatics

|September 7, 2011

Summary

This study introduces a Pattern Search method for optimizing Support Vector Machine hyperparameters. The Hooke and Jeeves Pattern Search (HJPS) method improves model selection for healthcare applications, offering better efficiency and accuracy than Grid Search.

Related Experiment Videos

Last Updated: May 29, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Machine Learning
Computational Biology
Health Informatics

Background:

Model selection is crucial for Support Vector Machines (SVMs).
Hyperparameter tuning significantly impacts SVM performance.
Existing methods like Grid Search (GS) can be computationally expensive.

Purpose of the Study:

To propose and evaluate a derivative-free Pattern Search method for SVM hyperparameter tuning.
To compare the proposed method against Grid Search (GS) for a healthcare classification task.

Main Methods:

Utilized the Hooke and Jeeves Pattern Search (HJPS) algorithm.
Employed an empirical error estimate as the steering criterion for optimization.
Experimentally evaluated the method on a healthcare dataset for nosocomial infection discrimination.

Main Results:

The HJPS method demonstrated superior solution quality compared to GS.
HJPS achieved better computational efficiency than GS.
The approach requires no derivative computation, enhancing practical applicability.

Conclusions:

The proposed HJPS method is effective for SVM model selection.
This technique offers a practical and efficient alternative for hyperparameter tuning in healthcare applications.
The method exhibits good performance and convergence properties.