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The Supreme Court recently overturned settled case law that affirmed a pregnant individual’s Constitutional right to an abortion. While many states will commit to protect this right, a large number of others have enacted laws that limit or outright ban abortion within their borders. Additional efforts are underway to prevent pregnant individuals from seeking care outside their home state. These changes have significant implications for delivery of healthcare as well as for patient-provider confidentiality. In particular, these laws will influence how information is documented in and accessed via electronic health records and how personal health applications are utilized in the consumer domain. We discuss how these changes may lead to confusion and conflict regarding use of health information, both within and across state lines, why current health information security practices may need to be reconsidered, and what policy options may be possible to protect individuals’ health information. 相似文献
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Waqas Amin Fuchiang Tsui Charles Borromeo Cynthia H Chuang Jeremy U Espino Daniel Ford Wenke Hwang Wishwa Kapoor Harold Lehmann G Daniel Martich Sally Morton Anuradha Paranjape William Shirey Aaron Sorensen Michael J Becich Rachel Hess the PaTH network team 《J Am Med Inform Assoc》2014,21(4):633-636
The PaTH (University of Pittsburgh/UPMC, Penn State College of Medicine, Temple University Hospital, and Johns Hopkins University) clinical data research network initiative is a collaborative effort among four academic health centers in the Mid-Atlantic region. PaTH will provide robust infrastructure to conduct research, explore clinical outcomes, link with biospecimens, and improve methods for sharing and analyzing data across our diverse populations. Our disease foci are idiopathic pulmonary fibrosis, atrial fibrillation, and obesity. The four network sites have extensive experience in using data from electronic health records and have devised robust methods for patient outreach and recruitment. The network will adopt best practices by using the open-source data-sharing tool, Informatics for Integrating Biology and the Bedside (i2b2), at each site to enhance data sharing using centrally defined common data elements, and will use the Shared Health Research Information Network (SHRINE) for distributed queries across the network. 相似文献
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目的 探讨不同扩散敏感系数(b值)的弥散加权成像(DWI)对基于生成对抗网络(GAN)的前列腺癌(PCa)检测影响的价值。方法 回顾性收集2012年1月—2018年6月同济大学附属同济医院就诊的前列腺疾病病例446例,其中PCa有174例、前列腺增生(BPH)有272例,所有病例均采用Siemens Verio 3.0T MRI扫描并经直肠超声引导下前列腺穿刺活检或前列腺根治术后病理证实。MRI成像序列包括横断位、矢状位高分辨T2加权成像(T2WI),扩散敏感系数(b值)分别为0、500、1000s/mm2横断位弥散加权成像(DWI)及动态对比增强(DCE)扫描,通过Matlab后处理计算化合成b分别为1500、2000s/mm2的DWI图像。本研究提出一个新型神经网络模型SegDenseAN,并结合不同b值DWI图像进行检测。将不同b值DWI与ADC影像的组合作为SegDenseAN网络的输入,各组合分别为: 组合1: ADC图;组合2: ADC+DWI0+DWI500;组合3: ADC+DWI0+DWI1000;组合4: ADC+DWI0+DWI1500;组合5: ADC+DWI1000+DWI1500;组合6: ADC+DWI1000+DWI2000,分析比较不同组合对准确率的影响。结果 组合1~6的准确率分别为0.871、0.887、0.903、0.903、0.903、0.935;组合1~6的灵敏度分别为0.935、0.935、0.968、0.968、0.968、0.968;组合1~6的特异度分别为0.806、0.839、0.839、0.839、0.839、0.903;组合6的前列腺癌病灶区域识别最接近于前列腺癌标注的金标准。结论 SegDenseAN 可以实现对于病灶区域的自动分割进而有助于前列腺癌的自动检测;多b值尤其是多高b值DWI与ADC影像的不同结合对算法的检测效果有影响,多个高b值DWI图像与ADC图结合有助于提高前列腺癌的智能检测结果。 相似文献
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ObjectiveDue to a complex set of processes involved with the recording of health information in the Electronic Health Records (EHRs), the truthfulness of EHR diagnosis records is questionable. We present a computational approach to estimate the probability that a single diagnosis record in the EHR reflects the true disease.Materials and MethodsUsing EHR data on 18 diseases from the Mass General Brigham (MGB) Biobank, we develop generative classifiers on a small set of disease-agnostic features from EHRs that aim to represent Patients, pRoviders, and their Interactions within the healthcare SysteM (PRISM features).ResultsWe demonstrate that PRISM features and the generative PRISM classifiers are potent for estimating disease probabilities and exhibit generalizable and transferable distributional characteristics across diseases and patient populations. The joint probabilities we learn about diseases through the PRISM features via PRISM generative models are transferable and generalizable to multiple diseases.DiscussionThe Generative Transfer Learning (GTL) approach with PRISM classifiers enables the scalable validation of computable phenotypes in EHRs without the need for domain-specific knowledge about specific disease processes.ConclusionProbabilities computed from the generative PRISM classifier can enhance and accelerate applied Machine Learning research and discoveries with EHR data. 相似文献
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由于尚无明确法律授权,在建立区域卫生信息化项目中,授权电子健康档案的建立及患者隐私控制等方面的问题不容忽视。结合厦门实际,对区域卫生信息化建设实践中如何做好电子健康档案授权及保护公民隐私等方面问题进行探讨。 相似文献
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Curtis L Cole Soumitra Sengupta Sarah Rossetti David K Vawdrey Michael Halaas Thomas M Maddox Geoff Gordon Trushna Dave Philip R O Payne Andrew E Williams Deborah Estrin 《J Am Med Inform Assoc》2021,28(3):646
Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing. 相似文献
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孙杰;金诗晨;石蓉;左传涛;蒋皆恢 《中南大学学报(医学版)》2022,47(8):1001-1008
医学图像跨模态重建是指基于被试某一种模态图像,预测同一被试的另一种模态图像,以实现更精准的个体化医疗。生成对抗网络(generative adversarial networks,GAN)是医学图像跨模态重建中最常见的深度学习技术,该技术通过从遵循真实数据分布的隐式分布中生成医学图像,进而快速重建出其他模态医学图像数据。随着临床对多模态影像数据需求的剧增,GAN技术在磁共振成像、计算机断层扫描和正电子发射型计算机断层扫描等多种不同的医学图像模态之间的跨模态重建任务中均得到广泛的应用,在脑、心等不同部位实现精准高效的跨模态图像重建。此外,虽然GAN在跨模态重建中取得了一定的成功,但其在稳定性、泛化能力和准确度方面仍需要进一步的改进。 相似文献
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ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.Materials and MethodsWe develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm’s classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology. 相似文献
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目的/意义 提出基于国家标准的电子病历数据元抽取方法,以实现电子病历数据的细粒度共享。方法/过程 利用ALBERT、BiLSTM和CRF模型对电子病历进行序列标注,并根据标注结果生成一组候选数据元;针对每个候选数据元,采集其上下文信息并形成一个增强的键向量;计算该向量与标准向量之间的相似度,据此判断候选数据元是否有效。结果/结论 该方法F1值为90.32%,效果较好。 相似文献
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阐述“人工智能+医疗”发展必然性,分析“人工智能+医疗”健康档案数据在深度学习阶段数据利用、健康管理环节数据采集、诊断治疗环节数据分析等方面存在的安全隐患,提出相应隐私安全策略,包括加强顶层设计和数据保护、制定专项法律法规、强化技术保障及宣教等。 相似文献
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目的 探索中医领域利用少量标注语料进行电子病历中医学实体信息的命名实体识别(NER)研究工作,为更复杂的中医电子病历信息处理及深度学习方法在中医领域内的运用提供参考.方法 分析中医电子病历词汇术语与一般的NER任务相比较的特殊性,对比了目前3种NER技术的优缺点,找寻适合中医电子病历医学术语的NER技术.结果 长短时记... 相似文献
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本文详细阐述了我院无线查房系统的前期调研分析,系统的部署实施完成了与医院信息系统的无缝链接。系统的应用实现了医院安全、可管理的无线信号均匀覆盖,提高了医院的运营效率和服务质量。 相似文献
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目的/意义 了解眼病患者就医行为特征,优化眼病智慧医疗服务策略。方法/过程 以安德森医疗服务利用模型为指导,调取某医院17 602人次的眼科电子病历数据,采用卡方检验和谱系聚类方法从4个维度挖掘眼病患者的就医行为特征;调查其中702例眼病患者对智慧医疗服务的满意度,通过有序logistic回归分析主要影响因素。结果/结论 根据眼病患者人口学特征、挂号方式、眼科常见疾病与发病数据、结算方式等特征,从资源布局、技术兼容、服务模式和优化结算4方面提出眼病智慧医疗服务建议与策略。 相似文献
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法律是病案管理发展的重要保障。依据相关的法律法规建立的规章制度,保证病案管理的科学性、有效性。病案管理随医院发展而不断成长和不断完善。病案工作之所以能够有序、有效的延续发展起来,贯穿的核心是依法所建立起来的各项规章制度。电子病案是现代化病案管理发展的方向,电子病案的发展需要技术上的支持与法律的保证。法律的颁布实施促进了医院病案管理的发展;同时,医院病案管理的发展促进法律条款的不断完善。 相似文献
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本文从电子病历的特点、电子病历病案管理系统存在的问题出发,对电子病历病案管理系统的功能需求进行了阐述。提出了电子病历病案管理系统应包括电子病历自动回收、按国际疾病分类标准及手术操作名称进行编码、实时监控病案质量、建立电子病案库、设立病案打印、病案查阅及全面的病案综合查询等功能,从而使电子病历能更好地服务于临床医疗和医院管理。 相似文献