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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Risk prediction models: a framework for assessment.

T H S Dent1, C F Wright, B C M Stephan

  • 1PHG Foundation, University of Cambridge, Cambridge, UK. tom.dent@phgfoundation.org

Public Health Genomics
|December 20, 2011
PubMed
Summary
This summary is machine-generated.

Medical risk prediction models require careful assessment to ensure they are suitable for clinical use. A new appraisal approach ensures effective models are implemented promptly and resources are not wasted on ineffective ones.

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Genomics
  • Clinical Medicine
  • Public Health

Background:

  • Medical risk prediction models estimate future health events using genomic data.
  • These models aid decision-making but can exacerbate health inequalities if misapplied.
  • Existing models for conditions like cardiovascular disease, stroke, and cancer vary in suitability.

Purpose of the Study:

  • To propose an improved approach for appraising medical risk prediction models.
  • To ensure appropriate models are implemented in clinical practice.
  • To guide the evaluation and selection of risk-scoring models.

Main Methods:

  • Convened a conference of UK experts to discuss model appraisal.
  • Developed a framework for assessing the suitability of risk models for implementation.
  • Focused on specifying criteria for translation from research to practice.

Main Results:

  • Identified current approaches to model development and evaluation as inappropriate.
  • Highlighted the risk of implementing unsuitable models prematurely.
  • Addressed confusion regarding the selection of appropriate risk models.

Conclusions:

  • Risk prediction models necessitate rigorous assessment before clinical implementation.
  • A structured appraisal approach ensures timely adoption of effective models.
  • Prevents misallocation of resources and promotes equitable healthcare.