这项研究提出了一种检查，使用多种机器学习和深度学习方法，基于患者在一定时间范围内的电子健康记录对患者的严重程度状态进行分类。建议的方法使用从开源平台收集的EHR数据集来对严重程度进行分类。在本研究中使用了一些工具，如openRefine用于预处理，RapidMiner用于实现三种算法（快速大间隔，广义线性模型，多层前馈神经网络）和Tableau用于可视化数据，对于算法的实现，我们使用Google Colab。在这里，我们实现了几种监督和无监督算法以及半监督和深度学习算法。实验结果表明，超参数调整的随机森林优于所有其他监督的机器学习算法的准确率为76%，广义线性算法达到最高的精度得分为78%，而超参数调整的层次聚类的准确率为86%，高斯混合模型的准确率为61%，优于其他无监督的方法。对于大多数无监督技术来说，简化改进了很多结果。为了实现深度学习，我们采用了前馈神经网络（多层）和快速大间隔方法进行半监督学习。Fast Large Margin的表现非常好，召回率为84%，F1得分为78%。最后，多层前馈神经网络表现出色，准确率为75%，精确率为75%，召回率为87%，F1得分为81%。
英文摘要：
This research presents an examination of categorizing the severity states of patients based on their electronic health records during a certain time range using multiple machine learning and deep learning approaches. The suggested method uses an EHR dataset collected from an open-source platform to categorize severity. Some tools were used in this research, such as openRefine was used to pre-process, RapidMiner was used for implementing three algorithms (Fast Large Margin, Generalized Linear Model, Multi-layer Feed-forward Neural Network) and Tableau was used to visualize the data, for implementation of algorithms we used Google Colab. Here we implemented several supervised and unsupervised algorithms along with semi-supervised and deep learning algorithms. The experimental results reveal that hyperparameter-tuned Random Forest outperformed all the other supervised machine learning algorithms with 76% accuracy as well as Generalized Linear algorithm achieved the highest precision score 78%, whereas the hyperparameter-tuned Hierarchical Clustering with 86% precision score and Gaussian Mixture Model with 61% accuracy outperformed other unsupervised approaches. Dimensionality Reduction improved results a lot for most unsupervised techniques. For implementing Deep Learning we employed a feed-forward neural network (multi-layer) and the Fast Large Margin approach for semi-supervised learning. The Fast Large Margin performed really well with a recall score of 84% and an F1 score of 78%. Finally, the Multi-layer Feed-forward Neural Network performed admirably with 75% accuracy, 75% precision, 87% recall, 81% F1 score.
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）
Curvature-based Feature Selection with Application in Classifying Electronic Health Records
Durham University（杜伦大学）；Teesside University（提赛德大学）；Institute of Microelectronics, Chinese Academy of Sciences（中国科学院微电子研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Indiana University Bloomington（印第安纳大学伯明顿分校）；Renmin University of China（中国人民大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
Georgia Institute of Technology（佐治亚理工学院）；IBM Research（IBM研究院）；Vanderbilt University（范德堡大学）；Sutter Health（萨特健康）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Records
Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University（核心信息学系，信息学研究生学校，大阪 Metropolitan 大学）；Smart Data & Knowledge Services Department, German Research Center for Artificial Intelligence (DFKI GmbH)（智能数据与知识服务部，德国人工智能研究中心（DFKI GmbH））；Department of Hematology and Department of Laboratory Medicine and Medical Informatics, Graduate School of Medicine, Osaka Metropolitan University（血液科和实验室医学与医学信息学系，医学研究生学校，大阪 Metropolitan 大学）
CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record Embeddings
Department of Computer Science and Engineering, University of North Texas（计算机科学与工程系，北卡罗来纳大学达顿分校）；BioDiscovery Institute, University of North Texas（生物发现研究所，北卡罗来纳大学达顿分校）；Center for Computational Life Sciences, University of North Texas（计算生命科学中心，北卡罗来纳大学达顿分校）
GLoC-EHR: Evidence-Cited Clinical Reasoning over Global Context and Local EHR Events
Seoul National University College of Medicine（首尔大学医学院）；Seoul National University Hospital（首尔大学医院）；Healthcare AI Research Institute, Seoul National University Hospital（首尔大学医院医疗AI研究所）
Large Language Models are Powerful Electronic Health Record Encoders
Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林夏里特医学院健康研究所）；BIH Biomedical Innovation Academy, BIH Charité Digital Clinician Scientist Program（BIH生物医学创新学院，BIH夏里特数字临床科学家计划）；Deutsches Herzzentrum der Charité – Medical Heart Center of Charité and German Heart Institute Berlin（夏里特德国心脏中心 – 夏里特医学院和德国心脏研究所柏林）；Layer Health, Inc.（Layer Health公司）
Customizing Open Source LLMs for Quantitative Medication Attribute Extraction across Heterogeneous EHR Systems
Department of Statistics, UC Riverside（统计学系，加州大学河滨分校）；Department of Electrical and Computer Engineering, UCLA（电气与计算机工程系，加州大学洛杉矶分校）；Department of Psychiatry and Biobehavioral Sciences, UCLA（精神病学与生物行为科学系，加州大学洛杉矶分校）
Time-Aware Attention for Enhanced Electronic Health Records Modeling
Department of Statistics and Data Science, National University of Singapore（统计与数据科学系，新加坡国立大学）；School of Computing, National University of Singapore（计算学院，新加坡国立大学）；Department of Biostatistics, Harvard T.H. Chan School of Public Health, USA（生物统计学系，哈佛T.H. Chan公共卫生学院，美国）
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）
Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
The Pennsylvania State University（宾夕法尼亚州立大学）；Dalian University of Technology（大连理工大学）；Purdue University（普渡大学）；Iowa State University（爱荷华州立大学）；GE Healthcare（GE医疗）
Contrastive Learning on Multimodal Analysis of Electronic Health Records
Harvard T.H. Chan School of Public Health（哈佛T.H. 柏林公共卫生学院）；Harvard Medical School（哈佛医学院）；Rutgers University（罗格斯大学）；National University of Singapore（新加坡国立大学）
