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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Reason and Intuition01:37

Reason and Intuition

The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the brain can only use...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

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Updated: Jun 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

Fast-and-frugal decision trees for clinicians.

Konstantinos V Katsikopoulos1, Susana Pereira2, Niklas Keller3

  • 1Department of Decision Analytics and Risk, Southampton Business School, University of Southampton, Southampton, UK.

Presse Medicale (Paris, France : 1983)
|June 24, 2026
PubMed
Summary
This summary is machine-generated.

Fast-and-frugal trees offer intuitive, simplified decision-making models for clinicians, overcoming the complexity of standard models. These heuristics use minimal information for effective clinical support, enhancing accuracy and transparency in artificial intelligence applications.

Keywords:
clinical decision makingfast-and-frugal treesheuristics

Related Experiment Videos

Last Updated: Jun 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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

Published on: January 11, 2020

Area of Science:

  • Decision Science
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Standard decision models face clinical resistance due to complexity and inability to handle medical data's ill-defined nature.
  • Fast-and-frugal heuristics provide intuitive decision-making tools using limited information and simple logic.

Purpose of the Study:

  • To define, discuss, and illustrate the construction of fast-and-frugal trees for clinical decision support.
  • To explore applications of fast-and-frugal trees in fetal monitoring and intensive care unit assignment.
  • To discuss future directions for theory and applications, particularly concerning AI in clinical decision-making.

Main Methods:

  • Review of literature on fast-and-frugal heuristics and trees.
  • Illustrative examples of building and applying fast-and-frugal trees.
  • Discussion of case studies in fetal monitoring and ICU assignment.

Main Results:

  • Fast-and-frugal trees are a successful family of heuristics applicable to clinical practice.
  • Demonstrated utility in supporting critical decisions in fetal monitoring and ICU allocation.
  • Identified challenges and opportunities for integrating these heuristics with AI.

Conclusions:

  • Fast-and-frugal trees offer a practical alternative to complex decision models in medicine.
  • These heuristics can enhance the accuracy and transparency of AI-driven clinical decision support systems.
  • Further research is needed to bridge theory and application for robust AI in healthcare.