ACM transactions on computing for healthcare最新文献_第8页

14 Years of Self-Tracking Technology for mHealth—Literature Review: Lessons Learned and the PAST SELF Framework mHealth自跟踪技术的14年——文献综述：经验教训和PAST Self框架

ACM transactions on computing for healthcare Pub Date : 2021-04-23 DOI: 10.1145/3592621

Sofia Yfantidou, Pavlos Sermpezis, A. Vakali

{"title":"14 Years of Self-Tracking Technology for mHealth—Literature Review: Lessons Learned and the PAST SELF Framework","authors":"Sofia Yfantidou, Pavlos Sermpezis, A. Vakali","doi":"10.1145/3592621","DOIUrl":"https://doi.org/10.1145/3592621","url":null,"abstract":"In today’s connected society, many people rely on mHealth and self-tracking (ST) technology to help them adopt healthier habits with a focus on breaking their sedentary lifestyle and staying fit. However, there is scarce evidence of such technological interventions’ effectiveness, and there are no standardized methods to evaluate their impact on people’s physical activity and health. This work aims to help ST practitioners and researchers by empowering them with systematic guidelines and a framework for designing and evaluating technological interventions to facilitate health behavior change and user engagement, focusing on increasing physical activity and decreasing sedentariness. To this end, we conduct a literature review of 129 papers between 2008 and 2022, which identifies the core ST design principles and their efficacy, as well as the most comprehensive list to date of user engagement evaluation metrics for ST. Based on the review’s findings, we propose PAST SELF, a framework to guide the design and evaluation of ST technology that has potential applications in industrial and scientific settings. Finally, to facilitate researchers and practitioners, we complement this article with an open corpus and an online, adaptive exploration tool for the PAST SELF data.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"4 1","pages":"1 - 43"},"PeriodicalIF":0.0,"publicationDate":"2021-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41734916","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

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 Automatic Contact Tracing Approaches Using Bluetooth Low Energy 蓝牙低能耗自动接触追踪方法综述

ACM transactions on computing for healthcare Pub Date : 2021-03-17 DOI: 10.1145/3444847

Leonie Reichert, Samuel Brack, B. Scheuermann

引用次数: 28

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

Saliency-Aware Class-Agnostic Food Image Segmentation 显著性感知的食物图像分割

ACM transactions on computing for healthcare Pub Date : 2021-02-13 DOI: 10.1145/3440274

S. Yarlagadda, D. M. Montserrat, D. Güera, C. Boushey, D. Kerr, F. Zhu

{"title":"Saliency-Aware Class-Agnostic Food Image Segmentation","authors":"S. Yarlagadda, D. M. Montserrat, D. Güera, C. Boushey, D. Kerr, F. Zhu","doi":"10.1145/3440274","DOIUrl":"https://doi.org/10.1145/3440274","url":null,"abstract":"Advances in image-based dietary assessment methods have allowed nutrition professionals and researchers to improve the accuracy of dietary assessment, where images of food consumed are captured using smartphones or wearable devices. These images are then analyzed using computer vision methods to estimate energy and nutrition content of the foods. Food image segmentation, which determines the regions in an image where foods are located, plays an important role in this process. Current methods are data dependent and thus cannot generalize well for different food types. To address this problem, we propose a class-agnostic food image segmentation method. Our method uses a pair of eating scene images, one before starting eating and one after eating is completed. Using information from both the before and after eating images, we can segment food images by finding the salient missing objects without any prior information about the food class. We model a paradigm of top-down saliency that guides the attention of the human visual system based on a task to find the salient missing objects in a pair of images. Our method is validated on food images collected from a dietary study that showed promising results.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"2 1","pages":"1 - 17"},"PeriodicalIF":0.0,"publicationDate":"2021-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3440274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45284937","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: 7

Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge 利用因果知识选择领域适应的治疗效果模型

ACM transactions on computing for healthcare Pub Date : 2021-02-11 DOI: 10.1145/3587695

Trent Kyono, I. Bica, Z. Qian, M. van der Schaar

{"title":"Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge","authors":"Trent Kyono, I. Bica, Z. Qian, M. van der Schaar","doi":"10.1145/3587695","DOIUrl":"https://doi.org/10.1145/3587695","url":null,"abstract":"While a large number of causal inference models for estimating individualized treatment effects (ITE) have been developed, selecting the best one poses a unique challenge, since the counterfactuals are never observed. The problem is challenged further in the unsupervised domain adaptation (UDA) setting where we have access to labeled samples in the source domain but desire selecting an ITE model that achieves good performance on a target domain where only unlabeled samples are available. Existing selection techniques for UDA are designed for predictive models and are sub-optimal for causal inference because they (1) do not account for the missing counterfactuals and (2) only examine the discriminative density ratios between the input covariates in the source and target domain and do not factor in the model’s predictions in the target domain. We leverage the invariance of causal structures across domains to introduce a novel model selection metric specifically designed for ITE models under UDA. We propose selecting models whose predictions of the effects of interventions satisfy invariant causal structures in the target domain. Experimentally, our method selects ITE models that are more robust to covariate shifts on a variety of datasets, including estimating the effect of ventilation in COVID-19 patients.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"4 1","pages":"1 - 29"},"PeriodicalIF":0.0,"publicationDate":"2021-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42443000","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

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