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相关概念视频

Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Improving Translational Accuracy02:07

Improving Translational Accuracy

9.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.0K
Language and Cognition01:27

Language and Cognition

323
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
323
Language Development01:22

Language Development

317
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
317
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

276
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
276
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

188
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
188

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相关实验视频

Updated: Jun 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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CPLLM:使用大型语言模型进行临床预测.

Ofir Ben Shoham1, Nadav Rappoport1

  • 1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Israel.

PLOS digital health
|December 6, 2024
PubMed
概括

使用大型语言模型 (CPLLM) 的临床预测准确地预测疾病诊断和医院再接收. 这种新的方法在不需要先前的医疗数据培训的情况下优于现有模型.

科学领域:

  • 人工智能的人工智能
  • 临床信息学 临床信息学
  • 医学数据科学 医学数据科学

背景情况:

  • 预测临床疾病和再入院对于患者护理和医疗管理至关重要.
  • 分析电子健康记录 (EHR) 的现有方法通常需要专门的预训练或与时间数据作斗争.
  • 大型语言模型 (LLM) 在处理复杂数据方面表现有前途,但需要适应临床预测任务.

研究的目的:

  • 引入用大型语言模型 (CPLLM) 进行临床预测,这是一种用于疾病诊断和医院再接收预测的新方法.
  • 根据已建立的基线模型评估CPLLM的表现,包括最先进的Med-BERT.
  • 为了证明CPLLM的有效性,而不需要在特定的医疗数据集上进行预先培训.

主要方法:

  • 使用量化和基于提示的技术微调预训练的大型语言模型 (LLM).
  • 利用患者历史病历进行诊断预测 (下一次访问或后续诊断).
  • 评估CPLLM用于医院再接收预测,并与基准模型进行比较.

主要成果:

  • 与所有测试的基线模型相比,CPLLM实现了更高的性能.
  • 该方法在疾病预测和医院再入院预测方面都取得了最先进的结果,通过PR-AUC和ROC-AUC指标测量.

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  • 在不需要对医疗数据进行预先培训的情况下,CPLLM成功预测了临床结果.

    • 结论:

    • CPLLM为临床预测任务提供了一种强大而有效的方法,包括疾病诊断和医院再接收.
    • 该方法在没有医疗预训练的情况下执行的能力简化了其实施和集成到临床工作流程中.
    • CPLLM有可能在计划患者护理和干预方面显著帮助医疗保健提供者.