Automating Delirium Identification and Risk Prediction in Electronic Health Records (Supplement)
电子健康记录中谵妄的自动化识别和风险预测(补充)
基本信息
- 批准号:10410694
- 负责人:
- 金额:$ 26.65万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-02-15 至 2022-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
ABSTRACT
Delirium, or acute confusional state, affects 30-40% of hospitalized older adults, with the added cost of care estimated to be up to $7 billion. Although originally conceptualized as a transient disorder, delirium is now recognized to have significant consequences, including increased risk of death, functional decline, and long-term cognitive impairment. As up to 75% of cases are not recognized by providers, there is an critical need for advanced methods to identify delirium for clinical and research purposes, and to stratify patients based on delirium risk. Unfortunately, surveillance of delirium is time consuming, resulting in few institutions implementing systematic screening procedures on all older adults. Research studies that use regular delirium screening assessments are typically small, single institution studies in a subset of patients that do not release their data for replication due to concern over privacy, lack of technical expertise, or other concerns. Epidemiological studies that do not use delirium screening assessments rely on proxy measures, such administrative codes with sensitivity as low as 3%, instead of chart review which can recover up to 74% of delirium cases. Advanced methods such as natural language processing (NLP) and machine learning (ML) have the potential to automate this chart review process and facilitate large-scale studies of delirium, but are hampered by lack of suitable data for algorithm development. We propose to leverage systematic delirium screening available through the University of Alabama of Birmingham (UAB) Virtual Acute Care for Elders (ACE) quality improvement program to create and release a de-identified delirium dataset to address the data and diagnosis gap in epidemiological studies of delirium. Our Virtual ACE program has determined delirium status on more than 33,000 patients across a six-year period, providing a rich set of data from which this project will draw. We will test the hypothesis that our transfer learning based deidentification method can assist annotators to more rapidly de-identify clinical text, opening the door to larger, faster, more widely available dataset releases. To validate the utility of the full dataset, we will determine the statistical power of our de-identified corpus to detect differences between participants with and without delirium in commonly used ML study designs.
Our delirium dataset release, containing 3,000 de-identified clinical notes and associated structural data, will be one of the largest text corpora ever released and the only text inclusive corpora specifically for the study of delirium. The proposed dataset will be available for download on Physionet with a Data Use Agreement (DUA) to facilitate further development of NLP and ML approaches for determining delirium status, risk factors, and sequelae at other institutions and in other populations by transfer learning. Release of our de-identification algorithms and methodology will also facilitate the development and release of large-scale text corpora in other
disorders
摘要
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
RICHARD E KENNEDY其他文献
RICHARD E KENNEDY的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('RICHARD E KENNEDY', 18)}}的其他基金
Automating Delirium Identification and Risk Prediction in Electronic Health Records
电子健康记录中谵妄的自动化识别和风险预测
- 批准号:
10341053 - 财政年份:2019
- 资助金额:
$ 26.65万 - 项目类别:
Automating Delirium Identification and Risk Prediction in Electronic Health Records
电子健康记录中谵妄的自动化识别和风险预测
- 批准号:
10091381 - 财政年份:2019
- 资助金额:
$ 26.65万 - 项目类别:
In Silico Screening of Medications for Slowing Alzheimer's Disease Progression.
减缓阿尔茨海默病进展药物的计算机筛选。
- 批准号:
9884696 - 财政年份:2017
- 资助金额:
$ 26.65万 - 项目类别:
Mixed Effects Modeling of Microarrays Using the S-score
使用 S 分数对微阵列进行混合效应建模
- 批准号:
6935669 - 财政年份:2005
- 资助金额:
$ 26.65万 - 项目类别:
Mixed Effects Modeling of Microarrays Using the S-score
使用 S 分数对微阵列进行混合效应建模
- 批准号:
7121993 - 财政年份:2005
- 资助金额:
$ 26.65万 - 项目类别:
Mixed Effects Modeling of Microarrays Using the S-score
使用 S 分数对微阵列进行混合效应建模
- 批准号:
7272023 - 财政年份:2005
- 资助金额:
$ 26.65万 - 项目类别:
相似海外基金
Developing Real-world Understanding of Medical Music therapy using the Electronic Health Record (DRUMMER)
使用电子健康记录 (DRUMMER) 培养对医学音乐治疗的真实理解
- 批准号:
10748859 - 财政年份:2024
- 资助金额:
$ 26.65万 - 项目类别:
Evaluation of the Caring Letters Suicide Prevention Intervention after Removal of an Electronic Health Record Flag for Suicide Risk: An Effectiveness-Implementation Hybrid Type 2 Trial
移除电子健康记录自杀风险标记后关怀信自杀预防干预的评估:有效性-实施混合 2 型试验
- 批准号:
10753299 - 财政年份:2023
- 资助金额:
$ 26.65万 - 项目类别:
Predicting firearm suicide in military veterans outside the VA health system using linked civilian electronic health record data
使用链接的民用电子健康记录数据预测退伍军人管理局卫生系统外退伍军人的枪支自杀
- 批准号:
10655968 - 财政年份:2023
- 资助金额:
$ 26.65万 - 项目类别:
Leveraging the Electronic Health Record and Integrating Social and Biological Data to Expand Dementia Research in Understudied Populations in Los Angeles County
利用电子健康记录并整合社会和生物数据,扩大洛杉矶县未受研究人群的痴呆症研究
- 批准号:
10729950 - 财政年份:2023
- 资助金额:
$ 26.65万 - 项目类别:
Enhancing sociodemographic data in a province-wide electronic health record
增强全省电子健康记录中的社会人口统计数据
- 批准号:
494408 - 财政年份:2023
- 资助金额:
$ 26.65万 - 项目类别:
Operating Grants
Development of a predictive model and electronic health record-based probability scoring system and dashboard for postoperative respiratory failure
开发术后呼吸衰竭的预测模型和基于电子健康记录的概率评分系统和仪表板
- 批准号:
10643357 - 财政年份:2023
- 资助金额:
$ 26.65万 - 项目类别:
Optimizing Employee Well-being and Retention during Electronic Health Record Modernization
在电子健康记录现代化过程中优化员工福祉和保留率
- 批准号:
10537213 - 财政年份:2023
- 资助金额:
$ 26.65万 - 项目类别:
Machine learning-based methods for phenotyping dementia patients from electronic health record data
基于机器学习的方法,根据电子健康记录数据对痴呆症患者进行表型分析
- 批准号:
10720916 - 财政年份:2023
- 资助金额:
$ 26.65万 - 项目类别:
Demonstrating the potential for electronic health record interoperability to improve patient safety research of older adults over the acute episode of care.
展示电子健康记录互操作性的潜力,以改善老年人急性护理期间的患者安全研究。
- 批准号:
10728699 - 财政年份:2023
- 资助金额:
$ 26.65万 - 项目类别:
Improving the Detection of Hypertrophic Cardiomyopathy Using Machine Learning Applied to Electronic Health Record Data
利用机器学习应用于电子健康记录数据来改善肥厚型心肌病的检测
- 批准号:
10740278 - 财政年份:2023
- 资助金额:
$ 26.65万 - 项目类别: