Machine Learning in Health Care最新文献

Latent Temporal Flows for Multivariate Analysis of Wearables Data 可穿戴设备数据多元分析的潜在时间流

Machine Learning in Health Care Pub Date : 2022-10-14 DOI: 10.48550/arXiv.2210.07475

Magda Amiridi, Gregory Darnell, S. Jewell

{"title":"Latent Temporal Flows for Multivariate Analysis of Wearables Data","authors":"Magda Amiridi, Gregory Darnell, S. Jewell","doi":"10.48550/arXiv.2210.07475","DOIUrl":"https://doi.org/10.48550/arXiv.2210.07475","url":null,"abstract":"Increased use of sensor signals from wearable devices as rich sources of physiological data has sparked growing interest in developing health monitoring systems to identify changes in an individual's health profile. Indeed, machine learning models for sensor signals have enabled a diverse range of healthcare related applications including early detection of abnormalities, fertility tracking, and adverse drug effect prediction. However, these models can fail to account for the dependent high-dimensional nature of the underlying sensor signals. In this paper, we introduce Latent Temporal Flows, a method for multivariate time-series modeling tailored to this setting. We assume that a set of sequences is generated from a multivariate probabilistic model of an unobserved time-varying low-dimensional latent vector. Latent Temporal Flows simultaneously recovers a transformation of the observed sequences into lower-dimensional latent representations via deep autoencoder mappings, and estimates a temporally-conditioned probabilistic model via normalizing flows. Using data from the Apple Heart and Movement Study (AH&MS), we illustrate promising forecasting performance on these challenging signals. Additionally, by analyzing two and three dimensional representations learned by our model, we show that we can identify participants' $text{VO}_2text{max}$, a main indicator and summary of cardio-respiratory fitness, using only lower-level signals. Finally, we show that the proposed method consistently outperforms the state-of-the-art in multi-step forecasting benchmarks (achieving at least a $10%$ performance improvement) on several real-world datasets, while enjoying increased computational efficiency.","PeriodicalId":231229,"journal":{"name":"Machine Learning in Health Care","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126884090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis 集成神经网络在脓毒症早期诊断中的预测和隐私改进

Machine Learning in Health Care Pub Date : 2022-09-01 DOI: 10.48550/arXiv.2209.00439

Shigehiko Schamoni, M. Hagmann, S. Riezler

引用次数: 1

Development and Validation of ML-DQA - a Machine Learning Data Quality Assurance Framework for Healthcare ML-DQA的开发和验证——医疗保健机器学习数据质量保证框架

Machine Learning in Health Care Pub Date : 2022-08-04 DOI: 10.48550/arXiv.2208.02670

M. Sendak, Gaurav Sirdeshmukh, Timothy N. Ochoa, H. Premo, Linda Tang, Kira L. Niederhoffer, Sarah Reed, Kaivalya Deshpande, E. Sterrett, M. Bauer, L. Snyder, Afreen I. Shariff, D. Whellan, J. Riggio, D. Gaieski, Kristin M Corey, Megan Richards, M. Gao, M. Nichols, Bradley Heintze, William S Knechtle, W. Ratliff, S. Balu

{"title":"Development and Validation of ML-DQA - a Machine Learning Data Quality Assurance Framework for Healthcare","authors":"M. Sendak, Gaurav Sirdeshmukh, Timothy N. Ochoa, H. Premo, Linda Tang, Kira L. Niederhoffer, Sarah Reed, Kaivalya Deshpande, E. Sterrett, M. Bauer, L. Snyder, Afreen I. Shariff, D. Whellan, J. Riggio, D. Gaieski, Kristin M Corey, Megan Richards, M. Gao, M. Nichols, Bradley Heintze, William S Knechtle, W. Ratliff, S. Balu","doi":"10.48550/arXiv.2208.02670","DOIUrl":"https://doi.org/10.48550/arXiv.2208.02670","url":null,"abstract":"The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically. While clinical researchers cautiously use RWD for clinical investigations, ML for healthcare teams consume public datasets with minimal scrutiny to develop new algorithms. This study bridges this gap by developing and validating ML-DQA, a data quality assurance framework grounded in RWD best practices. The ML-DQA framework is applied to five ML projects across two geographies, different medical conditions, and different cohorts. A total of 2,999 quality checks and 24 quality reports were generated on RWD gathered on 247,536 patients across the five projects. Five generalizable practices emerge: all projects used a similar method to group redundant data element representations; all projects used automated utilities to build diagnosis and medication data elements; all projects used a common library of rules-based transformations; all projects used a unified approach to assign data quality checks to data elements; and all projects used a similar approach to clinical adjudication. An average of 5.8 individuals, including clinicians, data scientists, and trainees, were involved in implementing ML-DQA for each project and an average of 23.4 data elements per project were either transformed or removed in response to ML-DQA. This study demonstrates the importance role of ML-DQA in healthcare projects and provides teams a framework to conduct these essential activities.","PeriodicalId":231229,"journal":{"name":"Machine Learning in Health Care","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114699339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding HiCu:在自动化ICD编码中利用层次结构进行课程学习

Machine Learning in Health Care Pub Date : 2022-08-03 DOI: 10.48550/arXiv.2208.02301

Weiming Ren, Ruijing Zeng, Tong Wu, Tianshu Zhu, R. G. Krishnan

{"title":"HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding","authors":"Weiming Ren, Ruijing Zeng, Tong Wu, Tianshu Zhu, R. G. Krishnan","doi":"10.48550/arXiv.2208.02301","DOIUrl":"https://doi.org/10.48550/arXiv.2208.02301","url":null,"abstract":"There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of medical code prediction using curriculum learning, which is a training strategy for machine learning models that gradually increases the hardness of the learning tasks from easy to difficult. One of the challenges in curriculum learning is the design of curricula -- i.e., in the sequential design of tasks that gradually increase in difficulty. We propose Hierarchical Curriculum Learning (HiCu), an algorithm that uses graph structure in the space of outputs to design curricula for multi-label classification. We create curricula for multi-label classification models that predict ICD diagnosis and procedure codes from natural language descriptions of patients. By leveraging the hierarchy of ICD codes, which groups diagnosis codes based on various organ systems in the human body, we find that our proposed curricula improve the generalization of neural network-based predictive models across recurrent, convolutional, and transformer-based architectures. Our code is available at https://github.com/wren93/HiCu-ICD.","PeriodicalId":231229,"journal":{"name":"Machine Learning in Health Care","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131566828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images 基于点注释的三维心血管免疫荧光图像弱监督深度核检测

Machine Learning in Health Care Pub Date : 2022-07-29 DOI: 10.48550/arXiv.2208.00098

Nazanin Moradinasab, Y. Sharma, Laura S. Shankman, G. Owens, Donald E. Brown

{"title":"Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images","authors":"Nazanin Moradinasab, Y. Sharma, Laura S. Shankman, G. Owens, Donald E. Brown","doi":"10.48550/arXiv.2208.00098","DOIUrl":"https://doi.org/10.48550/arXiv.2208.00098","url":null,"abstract":"Two major causes of death in the United States and worldwide are stroke and myocardial infarction. The underlying cause of both is thrombi released from ruptured or eroded unstable atherosclerotic plaques that occlude vessels in the heart (myocardial infarction) or the brain (stroke). Clinical studies show that plaque composition plays a more important role than lesion size in plaque rupture or erosion events. To determine the plaque composition, various cell types in 3D cardiovascular immunofluorescent images of plaque lesions are counted. However, counting these cells manually is expensive, time-consuming, and prone to human error. These challenges of manual counting motivate the need for an automated approach to localize and count the cells in images. The purpose of this study is to develop an automatic approach to accurately detect and count cells in 3D immunofluorescent images with minimal annotation effort. In this study, we used a weakly supervised learning approach to train the HoVer-Net segmentation model using point annotations to detect nuclei in fluorescent images. The advantage of using point annotations is that they require less effort as opposed to pixel-wise annotation. To train the HoVer-Net model using point annotations, we adopted a popularly used cluster labeling approach to transform point annotations into accurate binary masks of cell nuclei. Traditionally, these approaches have generated binary masks from point annotations, leaving a region around the object unlabeled (which is typically ignored during model training). However, these areas may contain important information that helps determine the boundary between cells. Therefore, we used the entropy minimization loss function in these areas to encourage the model to output more confident predictions on the unlabeled areas. Our comparison studies indicate that the HoVer-Net model trained using our weakly ...","PeriodicalId":231229,"journal":{"name":"Machine Learning in Health Care","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126676141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods 使用内处理和后处理方法去偏深胸部x线分类器

Machine Learning in Health Care Pub Date : 2022-07-26 DOI: 10.48550/arXiv.2208.00781

Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt

{"title":"Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods","authors":"Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt","doi":"10.48550/arXiv.2208.00781","DOIUrl":"https://doi.org/10.48550/arXiv.2208.00781","url":null,"abstract":"Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiographs. Recently, several works have indicated that these state-of-the-art classifiers can be biased with respect to sensitive patient attributes, such as race or gender, leading to growing concerns about demographic disparities and discrimination resulting from algorithmic and model-based decision-making in healthcare. Fair machine learning has focused on mitigating such biases against disadvantaged or marginalised groups, mainly concentrating on tabular data or natural images. This work presents two novel intra-processing techniques based on fine-tuning and pruning an already-trained neural network. These methods are simple yet effective and can be readily applied post hoc in a setting where the protected attribute is unknown during the model development and test time. In addition, we compare several intra- and post-processing approaches applied to debiasing deep chest X-ray classifiers. To the best of our knowledge, this is one of the first efforts studying debiasing methods on chest radiographs. Our results suggest that the considered approaches successfully mitigate biases in fully connected and convolutional neural networks offering stable performance under various settings. The discussed methods can help achieve group fairness of deep medical image classifiers when deploying them in domains with different fairness considerations and constraints.","PeriodicalId":231229,"journal":{"name":"Machine Learning in Health Care","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125037440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data 不平衡医疗数据中风险预测的密度感知个性化训练

Machine Learning in Health Care Pub Date : 2022-07-23 DOI: 10.48550/arXiv.2207.11382

Zepeng Huo, Xiaoning Qian, Shuai Huang, Zhangyang Wang, Bobak J. Mortazavi

引用次数: 1

ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations ICE-NODE:临床嵌入与神经常微分方程的集成

Machine Learning in Health Care Pub Date : 2022-07-05 DOI: 10.48550/arXiv.2207.01873

Asem Alaa, Erik Mayer, Mauricio Barahona

{"title":"ICE-NODE: Integration of Clinical Embeddings with Neural Ordinary Differential Equations","authors":"Asem Alaa, Erik Mayer, Mauricio Barahona","doi":"10.48550/arXiv.2207.01873","DOIUrl":"https://doi.org/10.48550/arXiv.2207.01873","url":null,"abstract":"Early diagnosis of disease can lead to improved health outcomes, including higher survival rates and lower treatment costs. With the massive amount of information available in electronic health records (EHRs), there is great potential to use machine learning (ML) methods to model disease progression aimed at early prediction of disease onset and other outcomes. In this work, we employ recent innovations in neural ODEs combined with rich semantic embeddings of clinical codes to harness the full temporal information of EHRs. We propose ICE-NODE (Integration of Clinical Embeddings with Neural Ordinary Differential Equations), an architecture that temporally integrates embeddings of clinical codes and neural ODEs to learn and predict patient trajectories in EHRs. We apply our method to the publicly available MIMIC-III and MIMIC-IV datasets, and we find improved prediction results compared to state-of-the-art methods, specifically for clinical codes that are not frequently observed in EHRs. We also show that ICE-NODE is more competent at predicting certain medical conditions, like acute renal failure, pulmonary heart disease and birth-related problems, where the full temporal information could provide important information. Furthermore, ICE-NODE is also able to produce patient risk trajectories over time that can be exploited for further detailed predictions of disease evolution.","PeriodicalId":231229,"journal":{"name":"Machine Learning in Health Care","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124515693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Classifying Unstructured Clinical Notes via Automatic Weak Supervision 基于自动弱监督的非结构化临床笔记分类

Machine Learning in Health Care Pub Date : 2022-06-24 DOI: 10.48550/arXiv.2206.12088

Chufan Gao, Mononito Goswami, Jieshi Chen, A. Dubrawski

{"title":"Classifying Unstructured Clinical Notes via Automatic Weak Supervision","authors":"Chufan Gao, Mononito Goswami, Jieshi Chen, A. Dubrawski","doi":"10.48550/arXiv.2206.12088","DOIUrl":"https://doi.org/10.48550/arXiv.2206.12088","url":null,"abstract":"Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. Due to the unstructured nature of these narratives, providers employ dedicated staff to assign diagnostic codes to patients' diagnoses using the International Classification of Diseases (ICD) coding system. This manual process is not only time-consuming but also costly and error-prone. Prior work demonstrated potential utility of Machine Learning (ML) methodology in automating this process, but it has relied on large quantities of manually labeled data to train the models. Additionally, diagnostic coding systems evolve with time, which makes traditional supervised learning strategies unable to generalize beyond local applications. In this work, we introduce a general weakly-supervised text classification framework that learns from class-label descriptions only, without the need to use any human-labeled documents. It leverages the linguistic domain knowledge stored within pre-trained language models and the data programming framework to assign code labels to individual texts. We demonstrate the efficacy and flexibility of our method by comparing it to state-of-the-art weak text classifiers across four real-world text classification datasets, in addition to assigning ICD codes to medical notes in the publicly available MIMIC-III database.","PeriodicalId":231229,"journal":{"name":"Machine Learning in Health Care","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123159700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data 自我生存:一个开源软件包，用于回归，反事实估计，评估和表型与审查时间到事件的数据

Machine Learning in Health Care Pub Date : 2022-04-15 DOI: 10.48550/arXiv.2204.07276

Chirag Nagpal, Willa Potosnak, A. Dubrawski

引用次数: 8