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Methods of Documentation VII: EMR

Methods of Documentation VII: EMR

Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...

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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

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基于EHR轨迹的深度学习预测模型:系统审查

Ali Amirahmadi¹, Mattias Ohlsson², Kobra Etminani¹

¹Center for Applied Intelligent Systems Research, Halmstad University, Sweden.

Journal of biomedical informatics

|June 28, 2023

概括

深度学习模型有效地分析电子健康记录 (EHR) 轨迹,以预测患者的风险. 本综述强调了对EHR数据的深度学习的进展,确定了医疗保健分析领域的挑战和未来研究方向.

科学领域:

医疗信息学医疗信息学
人工智能在医学中的应用
计算健康计算健康

关键词:

深度学习是一种深度学习.疾病预测疾病预测电子健康纪录的轨迹电子健康记录电子健康记录系统性审查系统性审查

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Published on: October 10, 2018

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背景情况:

电子健康记录 (EHR) 产生了大量的时间数据,对于预测患者健康风险和实现主动医疗保健至关重要.
深度学习技术擅长分析复杂的EHR轨迹,为早期疾病识别和初级预防策略提供了重大潜力.

结论:

深度学习显著推进了EHR轨迹的建模,在图形神经网络和注意力机制方面取得了显著进展.
需要进行进一步的研究,以解决数据不足问题,并开发能够处理EHR轨迹数据多面性质的模型.
增加公共EHR轨迹数据集的可用性对于标准化模型比较和验证至关重要.