ACM transactions on computing for healthcare最新文献_第5页

Security and Privacy Requirements for Electronic Consent 电子同意的安全和隐私要求

ACM transactions on computing for healthcare Pub Date : 2021-03-21 DOI: 10.1145/3433995

Stef Verreydt, Koen Yskout, W. Joosen

{"title":"Security and Privacy Requirements for Electronic Consent","authors":"Stef Verreydt, Koen Yskout, W. Joosen","doi":"10.1145/3433995","DOIUrl":"https://doi.org/10.1145/3433995","url":null,"abstract":"Electronic consent (e-consent) has the potential to solve many paper-based consent approaches. Existing approaches, however, face challenges regarding privacy and security. This literature review aims to provide an overview of privacy and security challenges and requirements proposed by papers discussing e-consent implementations, as well as the manner in which state-of-the-art solutions address them. We conducted a systematic literature search using ACM Digital Library, IEEE Xplore, and PubMed Central. We included papers providing comprehensive discussions of one or more technical aspects of e-consent systems. Thirty-one papers met our inclusion criteria. Two distinct topics were identified, the first being discussions of e-consent representations and the second being implementations of e-consent in data sharing systems. The main challenge for e-consent representations is gathering the requirements for a “valid” consent. For the implementation papers, many provided some requirements but none provided a comprehensive overview. Blockchain is identified as a solution to transparency and trust issues in traditional client-server systems, but several challenges hinder it from being applied in practice. E-consent has the potential to grant data subjects control over their data. However, there is no agreed-upon set of security and privacy requirements that must be addressed by an e-consent platform. Therefore, security- and privacy-by-design techniques should be an essential part of the development lifecycle for such a platform.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3433995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41760627","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

A Survey of Computational Methods for Online Mental State Assessment on Social Media 社交媒体在线心理状态评估计算方法研究

ACM transactions on computing for healthcare Pub Date : 2021-03-17 DOI: 10.1145/3437259

E. A. Ríssola, D. Losada, F. Crestani

{"title":"A Survey of Computational Methods for Online Mental State Assessment on Social Media","authors":"E. A. Ríssola, D. Losada, F. Crestani","doi":"10.1145/3437259","DOIUrl":"https://doi.org/10.1145/3437259","url":null,"abstract":"Mental state assessment by analysing user-generated content is a field that has recently attracted considerable attention. Today, many people are increasingly utilising online social media platforms to share their feelings and moods. This provides a unique opportunity for researchers and health practitioners to proactively identify linguistic markers or patterns that correlate with mental disorders such as depression, schizophrenia or suicide behaviour. This survey describes and reviews the approaches that have been proposed for mental state assessment and identification of disorders using online digital records. The presented studies are organised according to the assessment technology and the feature extraction process conducted. We also present a series of studies which explore different aspects of the language and behaviour of individuals suffering from mental disorders, and discuss various aspects related to the development of experimental frameworks. Furthermore, ethical considerations regarding the treatment of individuals’ data are outlined. The main contributions of this survey are a comprehensive analysis of the proposed approaches for online mental state assessment on social media, a structured categorisation of the methods according to their design principles, lessons learnt over the years and a discussion on possible avenues for future research.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 31"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3437259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42482012","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: 32

Interpretable Bias Mitigation for Textual Data: Reducing Genderization in Patient Notes While Maintaining Classification Performance 文本数据的可解释偏见缓解：在保持分类性能的同时减少患者笔记中的性别化

ACM transactions on computing for healthcare Pub Date : 2021-03-10 DOI: 10.1145/3524887

J. Minot, N. Cheney, Marc E. Maier, Danne C. Elbers, C. Danforth, P. Dodds

{"title":"Interpretable Bias Mitigation for Textual Data: Reducing Genderization in Patient Notes While Maintaining Classification Performance","authors":"J. Minot, N. Cheney, Marc E. Maier, Danne C. Elbers, C. Danforth, P. Dodds","doi":"10.1145/3524887","DOIUrl":"https://doi.org/10.1145/3524887","url":null,"abstract":"Medical systems in general, and patient treatment decisions and outcomes in particular, can be affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how differences in gender-specific word frequency distributions and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of dataset bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce biases in natural language processing pipelines.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"240 1","pages":"1 - 41"},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41264692","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: 21

Online Algorithm for Differentially Private Genome-wide Association Studies 用于差异私有全基因组关联研究的在线算法

ACM transactions on computing for healthcare Pub Date : 2021-03-05 DOI: 10.1145/3431504

Md Momin Al Aziz, Shahin Kamali, N. Mohammed, Xiaoqian Jiang

{"title":"Online Algorithm for Differentially Private Genome-wide Association Studies","authors":"Md Momin Al Aziz, Shahin Kamali, N. Mohammed, Xiaoqian Jiang","doi":"10.1145/3431504","DOIUrl":"https://doi.org/10.1145/3431504","url":null,"abstract":"Digitization of healthcare records contributed to a large volume of functional scientific data that can help researchers to understand the behaviour of many diseases. However, the privacy implications of this data, particularly genomics data, have surfaced recently as the collection, dissemination, and analysis of human genomics data is highly sensitive. There have been multiple privacy attacks relying on the uniqueness of the human genome that reveals a participant or a certain group’s presence in a dataset. Therefore, the current data sharing policies have ruled out any public dissemination and adopted precautionary measures prior to genomics data release, which hinders timely scientific innovation. In this article, we investigate an approach that only releases the statistics from genomic data rather than the whole dataset and propose a generalized Differentially Private mechanism for Genome-wide Association Studies (GWAS). Our method provides a quantifiable privacy guarantee that adds noise to the intermediate outputs but ensures satisfactory accuracy of the private results. Furthermore, the proposed method offers multiple adjustable parameters that the data owners can set based on the optimal privacy requirements. These variables are presented as equalizers that balance between the privacy and utility of the GWAS. The method also incorporates Online Bin Packing technique [1], which further bounds the privacy loss linearly, growing according to the number of open bins and scales with the incoming queries. Finally, we implemented and benchmarked our approach using seven different GWAS studies to test the performance of the proposed methods. The experimental results demonstrate that for 1,000 arbitrary online queries, our algorithms are more than 80% accurate with reasonable privacy loss and exceed the state-of-the-art approaches on multiple studies (i.e., EigenStrat, LMM, TDT).","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":" ","pages":"1 - 27"},"PeriodicalIF":0.0,"publicationDate":"2021-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3431504","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46636696","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: 4

Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels. 从药物标签中提取药物相互作用信息的注意门控图卷积。

ACM transactions on computing for healthcare Pub Date : 2021-03-01 DOI: 10.1145/3423209

Tung Tran, Ramakanth Kavuluru, Halil Kilicoglu

{"title":"Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels.","authors":"Tung Tran, Ramakanth Kavuluru, Halil Kilicoglu","doi":"10.1145/3423209","DOIUrl":"10.1145/3423209","url":null,"abstract":"Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination of drug safety information. Herein, we tackle the problem of jointly extracting mentions of drugs and their interactions, including interaction outcome, from drug labels. Our deep learning approach entails composing various intermediate representations, including graph-based context derived using graph convolutions (GCs) with a novel attention-based gating mechanism (holistically called GCA), which are combined in meaningful ways to predict on all subtasks jointly. Our model is trained and evaluated on the 2018 TAC DDI corpus. Our GCA model in conjunction with transfer learning performs at 39.20% F1 and 26.09% F1 on entity recognition (ER) and relation extraction (RE), respectively, on the first official test set and at 45.30% F1 and 27.87% F1 on ER and RE, respectively, on the second official test set. These updated results lead to improvements over our prior best by up to 6 absolute F1 points. After controlling for available training data, the proposed model exhibits state-of-the-art performance for this task.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3423209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39453024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Smartphone Sonar-Based Contact-Free Respiration Rate Monitoring 基于智能手机声纳的无接触呼吸速率监测

ACM transactions on computing for healthcare Pub Date : 2021-02-09 DOI: 10.1145/3436822

Xuyu Wang, Runze Huang, Chao Yang, S. Mao

引用次数: 12

Machine Learning for Sleep Apnea Detection with Unattended Sleep Monitoring at Home 在家进行无人值守睡眠监测的机器学习睡眠呼吸暂停检测

ACM transactions on computing for healthcare Pub Date : 2021-02-09 DOI: 10.1145/3433987

Stein Kristiansen, K. Nikolaidis, T. Plagemann, V. Goebel, G. Traaen, B. Øverland, L. Aakerøy, T. Hunt, J. P. Loennechen, S. Steinshamn, C. Bendz, O. Anfinsen, L. Gullestad, H. Akre

{"title":"Machine Learning for Sleep Apnea Detection with Unattended Sleep Monitoring at Home","authors":"Stein Kristiansen, K. Nikolaidis, T. Plagemann, V. Goebel, G. Traaen, B. Øverland, L. Aakerøy, T. Hunt, J. P. Loennechen, S. Steinshamn, C. Bendz, O. Anfinsen, L. Gullestad, H. Akre","doi":"10.1145/3433987","DOIUrl":"https://doi.org/10.1145/3433987","url":null,"abstract":"Sleep apnea is a common and strongly under-diagnosed severe sleep-related respiratory disorder with periods of disrupted or reduced breathing during sleep. To diagnose sleep apnea, sleep data are collected with either polysomnography or polygraphy and scored by a sleep expert. We investigate in this work the use of supervised machine learning to automate the analysis of polygraphy data from the A3 study containing more than 7,400 hours of sleep monitoring data from 579 patients. We conduct a systematic comparative study of classification performance and resource use with different combinations of 27 classifiers and four sleep signals. The classifiers achieve up to 0.8941 accuracy (kappa: 0.7877) when using all four signal types simultaneously and up to 0.8543 accuracy (kappa: 0.7080) with only one signal, i.e., oxygen saturation. Methods based on deep learning outperform other methods by a large margin. All deep learning methods achieve nearly the same maximum classification performance even when they have very different architectures and sizes. When jointly accounting for classification performance, resource consumption and the ability to achieve with less training data high classification performance, we find that convolutional neural networks substantially outperform the other classifiers.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 25"},"PeriodicalIF":0.0,"publicationDate":"2021-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3433987","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43560441","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: 14

Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living 数据驱动的上下文检测利用被动感知的近距物来识别日常生活中的复杂活动

ACM transactions on computing for healthcare Pub Date : 2021-01-04 DOI: 10.1145/3428664

A. Akbari, Reese Grimsley, R. Jafari

{"title":"Data-driven Context Detection Leveraging Passively Sensed Nearables for Recognizing Complex Activities of Daily Living","authors":"A. Akbari, Reese Grimsley, R. Jafari","doi":"10.1145/3428664","DOIUrl":"https://doi.org/10.1145/3428664","url":null,"abstract":"Wearable systems have unlocked new sensing paradigms in various applications such as human activity recognition, which can enhance effectiveness of mobile health applications. Current systems using wearables are not capable of understanding their surroundings, which limits their sensing capabilities. For instance, distinguishing certain activities such as attending a meeting or class, which have similar motion patterns but happen in different contexts, is challenging by merely using wearable motion sensors. This article focuses on understanding user's surroundings, i.e., environmental context, to enhance capability of wearables, with focus on detecting complex activities of daily living (ADL). We develop a methodology to automatically detect the context using passively observable information broadcasted by devices in users’ locale. This system does not require specific infrastructure or additional hardware. We develop a pattern extraction algorithm and probabilistic mapping between the context and activities to reduce the set of probable outcomes. The proposed system contains a general ADL classifier working with motion sensors, learns personalized context, and uses that to reduce the search space of activities to those that occur within a certain context. We collected real-world data of complex ADLs and by narrowing the search space with context, we improve average F1-score from 0.72 to 0.80.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 22"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3428664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45105424","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

A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders. 心血管疾病风险因素传感与分析的挑战与机遇调查》。

ACM transactions on computing for healthcare Pub Date : 2021-01-01 Epub Date: 2020-12-30 DOI: 10.1145/3417958

Nathan C Hurley, Erica S Spatz, Harlan M Krumholz, Roozbeh Jafari, Bobak J Mortazavi

{"title":"A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders.","authors":"Nathan C Hurley, Erica S Spatz, Harlan M Krumholz, Roozbeh Jafari, Bobak J Mortazavi","doi":"10.1145/3417958","DOIUrl":"10.1145/3417958","url":null,"abstract":"Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320445/pdf/nihms-1670305.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39274866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Creating and Evaluating Chatbots as Eligibility Assistants for Clinical Trials 创建和评估聊天机器人作为临床试验的合格助手

ACM transactions on computing for healthcare Pub Date : 2020-12-30 DOI: 10.1145/3403575

C. Chuan, Susan Morgan

{"title":"Creating and Evaluating Chatbots as Eligibility Assistants for Clinical Trials","authors":"C. Chuan, Susan Morgan","doi":"10.1145/3403575","DOIUrl":"https://doi.org/10.1145/3403575","url":null,"abstract":"Clinical trials are important tools to improve knowledge about the effectiveness of new treatments for all diseases, including cancers. However, studies show that fewer than 5% of cancer patients are enrolled in any type of research study or clinical trial. Although there is a wide variety of reasons for the low participation rate, we address this issue by designing a chatbot to help users determine their eligibility via interactive, two-way communication. The chatbot is supported by a user-centered classifier that uses an active deep learning approach to separate complex eligibility criteria into questions that can be easily answered by users and information that requires verification by their doctors. We collected all the available clinical trial eligibility criteria from the National Cancer Institute's website to evaluate the chatbot and the classifier. Experimental results show that the active deep learning classifier outperforms the baseline k-nearest neighbor method. In addition, an in-person experiment was conducted to evaluate the effectiveness of the chatbot. The results indicate that the participants who used the chatbot achieved better understanding about eligibility than those who used only the website. Furthermore, interfaces with chatbots were rated significantly better in terms of perceived usability, interactivity, and dialogue.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 19"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3403575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47567756","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: 6