相关实验视频

Updated: Jun 15, 2025

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

一个以用户为中心的实现和评估可解释的人工智能的愿景,用于预测医院发病的细菌病

Anna Thalea Hoogestraat¹, Antje Wulff¹

¹Big Data in Medicine, Department of Health Services Research, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.

Studies in health technology and informatics

|August 23, 2024

概括

这项研究提出了一种以用户为中心的解释性人工智能 (XAI) 方法,以预测医院发病的细菌病 (HOB) 风险. 了解用户需求是对人工智能医疗应用的信任的关键.

科学领域:

医疗保健信息学医疗保健信息学
人工智能在医学中的应用
机器学习用于临床预测

背景情况:

人工智能 (AI) 在医疗保健中越来越多地用于疾病诊断和预测等任务.
可解释的人工智能 (XAI) 对用户的信任和对人工智能驱动的医疗决策的理解至关重要.
医院发病的细菌病 (HOB) 是一个重大的临床挑战,需要准确的风险预测.

研究的目的:

提出实施和评估以用户为中心的XAI用于HOB风险预测的愿景.
解决用于临床风险评估的人工智能算法的透明度需求.
增强用户的信任和采用基于人工智能的预测工具在医疗保健环境.

主要方法:

利用机器学习方法来预测HOB的个体风险.
进行了初始需求分析,以了解用户对可解释性的需求.
提出了以用户为中心的XAI的实施和评估的逐步流程.

主要成果:

在AI驱动的风险预测应用中,确定了关键用户对可解释性的需求.
在HOB风险预测中为XAI建立了以用户为中心的评估过程的基础.
强调了用户洞察力在开发可靠的人工智能工具中的重要性.

结论:

以用户为中心的方法对于XAI在临床风险预测中的成功实施至关重要.
了解用户需求是开发有效和可靠的XAI系统的关键第一步.
提出的逐步流程为评估以用户为中心的XAI用于HOB风险预测提供了一个框架.

关键词:

可解释的人工智能机器学习机器学习可以解释性的解释性.在医院发病的细菌病.人与计算机的互动.可以解释的解释性.风险预测风险预测这是一种血症.

更多相关视频

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Published on: April 6, 2021

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

相关概念视频

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

Healthcare Associated Infections II: Preventive Measures

Healthcare Associated Infections II: Preventive Measures

Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...

Healthcare Associated Infections I: Iatrogenic, Exogenic and Endogenic

Healthcare Associated Infections I: Iatrogenic, Exogenic and Endogenic

Healthcare-associated infections (HAIs) occur in a healthcare facility while a person receives care for another ailment. This category also includes work-related infections among healthcare staff.
HAIs significantly increase the cost of health care. Extended stays in healthcare institutions, increased disability, increased costs of medications, including specialized antibiotics, and prolonged recovery times add to the patient's expenses and the healthcare institution and funding bodies.