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相关概念视频

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.1K
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...
5.1K
Classification of Systems-I01:26

Classification of Systems-I

545
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
545
Decision Making01:20

Decision Making

884
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...
884
Classification of Systems-II01:31

Classification of Systems-II

457
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
457
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
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...
6.8K
Deductive Reasoning01:16

Deductive Reasoning

64.1K
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.
For example, a researcher can deduce specific predictions...
64.1K

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相关实验视频

Updated: Jan 14, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

使用基于规则和AI方法的混合决策支持系统:基于知识的OnCATs框架.

Nuno Soares Domingues1

  • 1Instituto Politécnico de Lisboa/Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Emidio Navarro, 1, 1959-007 Lisbon, Portugal.

International journal of medical informatics
|October 22, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了OnCATs,这是一种可解释的前列腺癌临床决策支持系统 (CDSS),它使用透明的,基于规则的推理成功地复制了基于指南的决策.

相关实验视频

Last Updated: Jan 14, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

科学领域:

  • 在瘤学瘤学.
  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能

背景情况:

  • 临床决策支持系统 (CDSS) 经常使用不透明的AI,阻碍了瘤学的透明度和可重复性.
  • 前列腺癌的护理需要适应性决策支持,因为复杂的因素,如瘤阶段,PSA,格里森得分,并伴随性疾病.
  • 需要在CDSS中弥合可解释性和适应性,以获得可靠的前列腺癌管理.

研究的目的:

  • 开发和评估OnCATs,一种新的模块化和可解释的前列腺癌CDSS.
  • 将国际前列腺癌管理指南编码成机器可读和可审计的格式.
  • 创建一个能够整合未来进步的混合型CDSS.

主要方法:

  • 从23个国际指导方针中将证据正式化为基于JSON的规则基础.
  • 使用前链推断引擎来执行OnCATs中的规则.
  • 实施了三个决策层:风险分层,治疗途径建议和处方级别的协助.
  • 使用十篇已发表的案例报告和标准指标 (精度,回忆,F1) 评估可行性和性能.

主要成果:

  • 在风险分层方面,OnCATs实现了完美的一致性 (F1=1.00).
  • 治疗途径推一致性达到了F1得分0.80.
  • 处方级别的辅助显示了从0.67到0.75的协议 (平均F1=0.71).
  • 观察到的差异归因于简化寿命建模和不完整的病例数据.

结论:

  • OnCATs成功地展示了透明的,基于规则的推理,用于指导方针定义的可追溯性前列腺癌决策.
  • 该系统将多源指导方针运行到一个可解释和模块化的CDSS中.
  • 在前列腺癌治疗中,OnCATs为集成先进的人工智能和机器学习方法提供了可重复的基础.