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D22. HOT TOPICS IN BEHAVIORAL SCIENCES AND HEALTH SERVICES RESEARCH最新文献

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Assessing the Robustness of a Machine Learning Model for Predicting Asthma Hospital Encounters During the COVID-19 Pandemic 评估用于预测COVID-19大流行期间哮喘医院就诊的机器学习模型的鲁棒性
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6306
X. Zhang, S. Zeliadt, M. Walker, M. Levitt, B. Ng, G. Luo
Rationale: Asthma imposes a significant burden on healthcare. To better allocate limited preventive care management resources and improve patient outcomes, we recently used machine learning to build the world’s most accurate model to predict asthma hospital encounters (emergency department visits or inpatient stays for asthma) in the subsequent 12 months. Under pre-pandemic conditions, the model reached an area under the receiver operating characteristic curve (AUC) of >0.9. Performance of the model under COVID-19 conditions had not been previously tested. Objective: To assess the robustness of our predictive model during the COVID-19 pandemic. Methods: The patient cohort included all 55,435 adult asthmatic patients who visited the University of Washington Medicine facilities between 2011-2020. For each year t (2011≤ t ≤2020) , the corresponding effective data were of the patients who had asthma in t , included 71 features computed on these patients’ data during 2011-t , and contained these patients’ outcomes in t +1 as the prediction targets. For each outcome year y (2019 ≤ y ≤ 2021), the model was trained on the 2011-( y − 2) effective data and then tested on the y − 1 effective data. Results: The model yielded an AUC of 0.929, 0.892, and 0.904 for the outcome years 2019 (pre-pandemic), 2020 (peri-pandemic
理由:哮喘对医疗保健造成重大负担。为了更好地分配有限的预防性护理管理资源并改善患者预后,我们最近使用机器学习构建了世界上最准确的模型,以预测随后12个月的哮喘医院就诊情况(急诊室就诊或因哮喘住院)。在大流行前的条件下,模型达到接收者工作特征曲线(AUC)下的面积>0.9。该模型在COVID-19条件下的性能此前未经过测试。目的:评估我们的预测模型在COVID-19大流行期间的稳健性。方法:患者队列包括2011-2020年间访问华盛顿大学医学设施的所有55435名成年哮喘患者。每一年t(2011≤t≤2020),对应的有效数据为t年哮喘患者的有效数据,包括71个基于这些患者2011-t年数据计算的特征,并将这些患者在t +1年的结局作为预测目标。对于每个结果年y(2019≤y≤2021),模型在2011-(y−2)有效数据上进行训练,然后在y−1有效数据上进行测试。结果:该模型得出2019年(大流行前)、2020年(大流行前后)的AUC分别为0.929、0.892和0.904
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引用次数: 0
Assessing the Impact of AI Based Prioritization and Triage of Positive Non-CTPAs on Wait Time in the Outpatient Setting 评估基于人工智能的非ctpas阳性的优先级和分类对门诊等待时间的影响
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6307
D. A. Klein
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引用次数: 0
Lung Cancer Screening to Detect and Treat Undiagnosed Cardiovascular Disease and Chronic Obstructive Pulmonary Disease. An Analysis of the Canadian McGill Lung Cancer Screening Pilot Program Cohort 肺癌筛查检测和治疗未确诊的心血管疾病和慢性阻塞性肺疾病。加拿大麦吉尔肺癌筛查试点项目队列分析
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6312
C. Besson, R. Hamed, E. Mugutso, F. Noel, A. Benedetti, B.M. Smith, E. McDonald, A.V. Gonzalez, N. Ezer
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引用次数: 0
An Assessment of Pressurized Metered-dose Inhaler Use in Countries in Europe and the Rest of the World 对欧洲和世界其他国家加压计量吸入器使用情况的评估
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6315
J. P. Bell, A. Ringall, M. Khezrian, J. Kocks, O. Usmani
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引用次数: 1
Variation in Receipt of Imaging Among Patients Presenting With Dyspnea in Emergency and Ambulatory Settings 急诊和门诊呼吸困难患者接受影像学检查的差异
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6304
S. Cabral, M. Lie, E. Beideck, K. Mukamal
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引用次数: 0
Do Prognostic Scores Perform Differently in Surge-strained Hospitals? Impact of Surge Index on COVID-19 Mortality Prediction by the Modified 4C Score at 224 Us Hospitals, March-May 2020 在手术紧张的医院,预后评分表现不同吗?2020年3月- 5月224家美国医院突发指数对修正4C评分预测COVID-19死亡率的影响
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6302
C. Yek, J. Wang, J. Fintzi, M. Keller, S. Warner, S. Kadri
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引用次数: 0
Exploring Provider Conceptualization of Limited Life Expectancy in the Context of Lung Cancer Screening: A Qualitative Study 在肺癌筛查的背景下探讨提供者对有限预期寿命的概念化:一项定性研究
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6309
L. Kearney, R. Bolton, E. Núñez, J. Boudreau, S. Sliwinski, A. Herbst, T. Caverly, C. Slatore, R. Wiener
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引用次数: 0
Endobronchial Ultrasound Transbronchial Needle Aspiration With Vs Without Rapid On-site Evaluation in Diagnosing Mediastinal and Pulmonary Lesions: A Systematic Review and Metanalysis 支气管内超声经支气管针抽吸Vs无快速现场评估诊断纵隔和肺部病变:系统回顾和荟萃分析
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6305
A. Elkhapery, C. Iyer, S. Vallabhajosyula, Z. Abdalla, H. Boppana, J. Bhaskar
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引用次数: 0
Healthy Brain Ageing Education Program for Chronic Lung Disease Is Feasible in Rural Areas, Technological Challenges Notwithstanding - Pilot Randomized Controlled Trial 针对慢性肺病的健康脑老化教育计划在农村地区是可行的,尽管存在技术挑战——试点随机对照试验
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6320
R. Disler, L. Mowszowski, A. Pascoe, L. Coates, S. Roach, K. Glenister, N. Fortunato De Miranda, N. Spiliopoulous, C. McDonald, M. Saling, S. Naismith
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引用次数: 0
Developing and Evaluating the Living Better @home With COVID-19 Model: A Co-designed Home-based Management Model for People From Arabic-speaking Communities 制定和评估COVID-19“居家生活更美好”模式:共同设计的阿拉伯语社区居家管理模式
Pub Date : 2023-05-01 DOI: 10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6321
C. Tang, S.M. Smith, D. Lim, R. Mansour, H. Alduleimi, F. Blackstock
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引用次数: 0
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D22. HOT TOPICS IN BEHAVIORAL SCIENCES AND HEALTH SERVICES RESEARCH
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