Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes. Despite the hype with AI, the full realization of this potential is seriously hindered by several systemic problems, including data privacy, security, bias, fairness, and explainability. In this paper, we propose a novel canonical architecture for the development of AI models in healthcare that addresses these challenges. This system enables the creation and management of AI predictive models throughout all the phases of their life cycle, including data ingestion, model building, and model promotion in production environments. This paper describes this architecture in detail, along with a qualitative evaluation of our experience of using it on real world problems.
Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records
University of Pittsburgh（匹兹堡大学）；Intelligent Systems Program（智能系统计划）；School of Computing and Information（计算与信息学院）；Department of Medicine（医学院）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Health Information Management（健康信息管理系）；Department of Biomedical Informatics（生物医学信息学系）；Clinical and Translational Science Institute（临床与转化科学研究所）
本文提出 Fair Patient Model（FPM），通过加权损失函数从 EHR 中学习公平患者表示，在 MIMIC-III 的四项预测任务中改善公平性并保持可比准确率。
NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
TUM University Hospital and Technical University of Munich (TUM)（慕尼黑工业大学医院与慕尼黑工业大学）；Institute for Diagnostic and Interventional Radiology, TUM University Hospital（慕尼黑工业大学医院诊断与介入放射学研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Institute for Diagnostic and Interventional Neuroradiology, TUM University Hospital（慕尼黑工业大学医院诊断与介入神经放射学研究所）；University Medical Center Hamburg-Eppendorf（汉堡埃彭多夫大学医学中心）
Patient Similarity Analysis with Longitudinal Health Data
University of Zurich（苏黎世大学）；University Hospital of Zurich（苏黎世大学医院）；University of Applied Sciences and Arts Northwestern Switzerland（西北瑞士应用科学与艺术大学）；Yale University School of Medicine（耶鲁大学医学院）
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；University Hospital Essen (AöR)（埃森大学医院）；University Medicine Essen（埃森大学医学院）；German Cancer Consortium (DKTK)（德国癌症联盟）
High-Fidelity Longitudinal Patient Simulation Using Real-World Data
Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo（东京大学医学系研究生院生物医学信息学部门）；Department of Healthcare Information Management, The University of Tokyo Hospital（东京大学医院医疗信息管理部）；Department of Cardiology and Nephrology, Mie University Graduate School of Medicine（名护大学医学研究生院心内科与肾内科部门）；Department of Emergency and Critical Care Medicine, Nippon Medical School（日本医疗学校急诊与重症医学部）；Artificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, The University of Tokyo（东京大学医学研究生院人工智能与数字孪生在医疗中的应用部门）；Graduate School of Health Data Science, Juntendo University（立命馆大学健康数据科学研究生院）
Machine learning using longitudinal prescription and medical claims for the detection of nonalcoholic steatohepatitis (NASH)
IQVIA（艾昆纬）
目的开发和评估机器学习模型，以检测疑似未确诊的非酒精性脂肪性肝炎（NASH）患者，用于诊断筛查和临床管理。方法在这项回顾性观察性非干预性研究中，使用来自1，463，089例患者的行政医疗索赔数据，训练梯度提升决策树，以从具有肥胖、2型糖尿病（T2 DM）、代谢紊乱或非酒精性脂肪肝（NAFL）病史的高危患者人群中检测可能的NASH患者。对模型进行训练，以检测所有风险患者或既往无NAFL诊断的亚组（非NAFL风险患者）中可能的NASH。使用回顾性医疗索赔数据对模型进行训练和验证，并使用精确召回面积和受试者工作特征曲线（AUPRC，AUROC）进行评估。在索赔数据中，NASH的6个月发病率为1/1，437例高危患者和1/2，127例非NAFL高危患者。训练用于检测所有风险患者中NASH的模型的AUPRC为0.0107（95% CI 0.0104 - 0.011），AUROC为0.84。在10%的召回率下，模型精度为4.3%，是NASH发病率的60倍。训练用于检测非NAFL患者中NASH的模型的AUPRC为0.003（95% CI 0.0029 - 0.0031），AUROC为0.78。在10%的召回率下，模型精度为1%，是NASH发病率的20倍。结论NASH在医疗索赔数据中的低发生率证实了NASH在临床实践中的漏诊模式。基于索赔的机器学习可以促进检测可能的NASH患者，以进行诊断测试和疾…
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Harvard Medical School（哈佛医学院）；Weill Cornell Medicine, Cornell University（康奈尔大学威尔康奈尔医学院）
Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset
Peking University（北京大学）；Health Data Research（健康数据研究所）；University of Edinburgh（爱丁堡大学）；Peking University Third Hospital（北京大学第三医院）；Peking University People’s Hospital（北京大学人民医院）；University of Exeter（埃克塞特大学）
Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）；Weill Cornell Medicine（威尔康奈尔医学院）
Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy
Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University（康奈尔大学罗伯特·F·史密斯化学与生物分子工程学院）；Department of Biochemistry, The Robert Larner, M.D. College of Medicine, University of Vermont Medical Center（佛蒙特大学医学中心罗伯特·拉纳医学院生物化学系）；Department of Obstetrics, Gynecology, and Reproductive Sciences, The Robert Larner, M.D. College of Medicine, University of Vermont Medical Center（佛蒙特大学医学中心罗伯特·拉纳医学院妇产科与生殖科学系）
Longitudinal modeling of MS patient trajectories improves predictions of disability progression
KU Leuven（鲁汶大学）；Hasselt University（哈瑟尔特大学）；Charles University in Prague（布拉格查理大学）；General University Hospital（综合大学医院）；University of Bari（巴里大学）；Hospital Universitario Virgen Macarena（马卡雷纳圣母大学医院）；Dokuz Eylul University（多库兹埃卢尔大学）；University G. d’Annunzio（邓南遮大学）；University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）
Deep Survival Analysis of Longitudinal EHR Data for Joint Prediction of Hospitalization and Death in COPD Patients
B2SLab, Institut de Recerca i Innovació en Salut (IRIS), Universitat Politècnica de Catalunya（B2SLab、创新与健康研究所（IRIS）、巴塞罗那理工大学）；Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络（CIBER-BBN））；Institut de Recerca Sant Joan de Déu（圣约翰德德乌研究所）；Evidenze Health；Servei de Vigilància Epidemiològica i Resposta a Emergències de Salut Pública a Barcelona Sud, Subdirecció Regional a Barcelona, Secretaria de Salut Pública, Departament de Salut, Generalitat de Catalunya（巴塞罗那南部公共卫生流行病监测与应急响应服务，巴塞罗那地区副秘书处，公共卫生秘书处，加泰罗尼亚自治区卫生部门）；Fundació Docència i Recerca Mútua Terrassa, Catalonia, Spain（特拉维撒互惠教育与研究基金会，加泰罗尼亚，西班牙）
EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records
Technical University of Munich（慕尼黑技术大学）；TUM School of Computation, Information and Technology（慕尼黑技术大学计算、信息与技术学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
