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Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

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Updated: Mar 3, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

参数高效转移学习自杀企图和想法检测的学习.

Bhanu Pratap Singh Rawat¹, Hong Yu^1,2,3

¹CICS, UMass-Amherst.

Proceedings of the ... International Workshop on Health Text Mining and Information Analysis

|March 2, 2026

概括

此摘要是机器生成的。

参数效率转移学习显著增强了临床自然语言处理模型,用于检测电子健康记录中的自杀企图和想法,以最小的参数调整提高性能.

相关实验视频

Last Updated: Mar 3, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

科学领域:

临床自然语言处理临床自然语言处理
医疗保健中的人工智能
机器学习用于临床决策支持

背景情况:

预训练语言模型 (LMs) 是临床自然语言处理 (NLP) 的最新技术.
由于有限的数据资源,模型通用性在临床领域至关重要.
在电子健康记录 (EHR) 中检测自杀企图 (SA) 和自杀想法 (SI) 是一个关键的临床应用.

研究的目的:

评估EHR中SA和SI检测的参数效率转移学习技术.
在新的医院数据集上评估预训练模型 (ScANER) 的性能改进.
为了研究微调小比例模型参数的影响.

主要方法:

通过使用Scan指南,对两个EHR数据集进行了注释.
使用五个参数效率转移学习技术微调了ScANER模型.
评估了基于适配器的学习和软提示调方法.

主要成果:

扫描仪在没有微调的情况下实现了0.85 (SA) 和0.87 (SI) 的基线宏观F1分数.
对不到2%的参数进行微调,SA-SI检测F1分数在数据集中分别提高了3%和5%.
参数效率转移学习增强了对新医院数据的模型性能.

结论:

参数有效的转移学习有效地提高了临床NLP模型的性能.
这些方法为适应模型适应具有有限注释的新临床数据集提供了可行的解决方案.
这种方法支持在各种医疗保健环境中部署强大的SA和SI检测工具.