Related Experiment Video
Updated: Aug 26, 2025
13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
12.2K
An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision
Kavya Ramisetty1, Jabez Christopher1, Subhrakanta Panda1
1Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani-Hyderabad Campus, Telangana, India.
Methods of Information in Medicine
|October 11, 2022
Summary
This study developed an explainable decision-making model to aid physicians in diagnosing complex allergy comorbidities. The model improves diagnostic accuracy and efficiency, especially for less-experienced clinicians.
Area of Science:
- Medical Informatics
- Immunology
Background:
- Allergy prevalence and severity are increasing globally, particularly in South Asia.
- Diagnosing allergy comorbidities is challenging due to complex symptom combinations.
Purpose of the Study:
- To develop an explainable decision-making model for diagnosing allergy comorbidities.
- To provide physicians with rational and understandable diagnostic support.
Main Methods:
- Utilized Bayes theorem-based probabilistic approaches on balanced allergy data.
- Incorporated expert-derived preference weights with objective knowledge.
- Applied data sampling strategies (ideal, single, complete) to address imbalanced comorbidity data.
Main Results:
- The decision-making model demonstrated improved clinician diagnostic performance by 3% (77.93%).
- Reduced diagnostic time by 20% and provided up to 10 pieces of evidence for decision explanation.
- Expert agreement on diagnoses was moderate (Fleiss' Kappa = 0.48).
Conclusions:
- An explainable decision-making model enhances diagnostic performance for clinicians, particularly those with less experience.
- The developed framework offers a valuable tool for managing complex allergy cases.
More Related Videos
Related Concept Videos
01:09Decision Making: P-value Method
5.6K
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...
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...
5.6K
01:14Decision Making: Traditional Method
4.1K
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...
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...
4.1K
01:20Decision Making
182
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...
Automatic decision-making is fast, intuitive, and relies on gut feelings...
182
01:37Reason and Intuition
6.6K
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...
6.6K
01:02Ranks
275
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
275
01:25The Anchoring-and-Adjustment Heuristic
7.4K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.4K