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Related Concept Videos

Decision Making01:20

Decision Making

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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.
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Decision Making: Traditional Method01:14

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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.
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Decision Making: P-value Method01:09

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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...
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Reason and Intuition01:37

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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...
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Statgraphics01:10

Statgraphics

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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
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Shared Decision Making: From Decision Science to Data Science.

Azza Shaoibi1, Brian Neelon2, Leslie A Lenert1,3

  • 1Epidemiology Analytics, Janssen Research and Development, Titusville, NJ, USA.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|February 7, 2020
PubMed
Summary
This summary is machine-generated.

A new Bayesian collaborative filtering (CF) algorithm accurately recommends treatments by linking patient preferences and outcomes. This predictive analytics approach improves shared decision-making, especially when patient groups are distinct.

Keywords:
collaborative filteringconjoint analysispreference phenotypesrecommender systemsshared decision makingtreatment recommendation

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Area of Science:

  • Computational statistics
  • Health informatics
  • Decision science

Background:

  • Shared decision-making relies on understanding patient preferences, but current clinical practice lacks methods to link these preferences with outcomes.
  • Integrating pretreatment preferences and patient-reported outcomes is crucial for personalized treatment recommendations.

Purpose of the Study:

  • To propose and evaluate a Bayesian collaborative filtering (CF) algorithm for personalized treatment recommendations.
  • To combine patient preferences and outcomes for improved clinical decision support.

Main Methods:

  • Developed a Bayesian CF algorithm involving conjoint analysis for preference elicitation.
  • Implemented patient clustering into preference phenotypes.
  • Generated treatment recommendations based on outcomes of similar patients.
  • Conducted simulation studies to compare the algorithm with a 2-stage approach.

Main Results:

  • Bayesian CF and 2-stage methods showed similar performance with high overlap in preference phenotypes.
  • Both methods accurately predicted recommendations when treatment moderately influenced satisfaction (kappa ≈ 0.70-0.73).
  • Bayesian CF outperformed the 2-stage approach in well-separated clusters, yielding higher accuracy (kappa ≈ 0.73-0.83).

Conclusions:

  • The Bayesian CF algorithm is a feasible and accurate method for treatment recommendation.
  • It effectively identifies patient preference phenotypes and guides shared decision-making.
  • This approach offers a roadmap for integrating predictive analytics into clinical practice.