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Related Concept Videos

Reasoning01:30

Reasoning

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
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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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Deductive Reasoning01:16

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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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 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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Related Experiment Video

Updated: Jul 17, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for

Qianghua Liu, Yu Tian, Tianshu Zhou

    IEEE Journal of Biomedical and Health Informatics
    |September 5, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel explainable artificial intelligence model for diagnosing diseases in primary health care. The cognitive reasoning model based on knowledge graphs improves diagnostic accuracy and aids clinical decision-making.

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    Area of Science:

    • Artificial Intelligence
    • Medical Informatics
    • Cognitive Science

    Background:

    • Primary health care (PHC) quality, particularly in China, requires improvement in diagnostic accuracy.
    • Existing artificial intelligence (AI) tools for clinical decision support lack scalability and explainability.
    • General practice relies heavily on accurate diagnosis and treatment for effective patient care.

    Purpose of the Study:

    • To propose an explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) for general practice decision-making.
    • To enhance the accuracy and explainability of disease diagnosis using electronic health records (EHRs).
    • To simulate human cognitive processes in clinical diagnosis.

    Main Methods:

    • Construction of a semi-automated abdominal disease knowledge graph.
    • Development of the CRKG model incorporating dual process theory, graph neural networks, and attention mechanisms.
    • Utilizing EHRs and knowledge graphs for personalized diagnosis and reasoning.

    Main Results:

    • The CRKG model achieved superior performance compared to baseline methods in disease diagnosis.
    • Achieved precision@1 of 0.7873, recall@10 of 0.9020, and hits@10 of 0.9340 for abdominal disease diagnosis.
    • Visualization of the reasoning process improved clinician comprehension and model explainability.

    Conclusions:

    • The CRKG model offers a promising approach for improving diagnostic accuracy and decision support in general practice.
    • Explainable AI integrated with knowledge graphs can significantly enhance the application of EHRs in clinical settings.
    • This research advances the development of intelligent systems for personalized and explainable medical diagnosis.