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Related Concept Videos

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Modeling in Therapy

Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
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There are thirteen domains for...
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Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

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Related Experiment Videos

An ontology-driven, diagnostic modeling system.

Peter J Haug1, Jeffrey P Ferraro, John Holmen

  • 1Medical Informatics Department, Intermountain Healthcare, Salt Lake City, Utah, USA. Peter.Haug@imail.org

Journal of the American Medical Informatics Association : JAMIA
|March 26, 2013
PubMed
Summary
This summary is machine-generated.

A new ontology-driven diagnostic modeling system (ODMS) automates the creation of diagnostic decision support tools. Initial tests show promising accuracy for automated pneumonia diagnosis, paving the way for improved clinical applications.

Keywords:
Data MiningDiagnostic SystemOntologyPneumonia

Related Experiment Videos

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support

Background:

  • Developing accurate diagnostic decision support systems (DDSS) is complex and time-consuming.
  • Leveraging medical knowledge within ontologies can streamline DDSS development.
  • Automating the creation of DDSS can improve efficiency and scalability.

Purpose of the Study:

  • To present an automated system for developing diagnostic decision support systems using medical ontologies.
  • To demonstrate the system's utility by developing a pneumonia diagnostic tool.
  • To evaluate the performance of the automatically generated tool.

Main Methods:

  • Developed the ontology-driven diagnostic modeling system (ODMS).
  • Utilized a medical ontology to guide data acquisition from a clinical data warehouse.
  • Employed automated machine learning algorithms for diagnostic screening application creation.
  • Tested the system with patient data from emergency room samples.

Main Results:

  • The ODMS was used to develop a preliminary tool for identifying pneumonia in emergency departments.
  • The automatically created tool achieved an area under the receiver operating characteristic curve of 0.920.
  • Performance was comparable to a manually created tool currently in clinical use (AUC 0.944).

Conclusions:

  • The ontology-driven diagnostic modeling system (ODMS) shows feasibility for automated diagnostic system development.
  • Initial results indicate promising accuracy for automated diagnostic tools.
  • The system provides a pathway for continuous model improvement in clinical decision support.