ACM transactions on computing for healthcare最新文献_第6页

Evaluating Alarm Classifiers with High-confidence Data Programming 用高置信度数据编程评估报警分类器

ACM transactions on computing for healthcare Pub Date : 2022-07-22 DOI: 10.1145/3549942

Sydney Pugh, I. Ruchkin, Christopher P. Bonafide, S. Demauro, O. Sokolsky, Insup Lee, James Weimer

{"title":"Evaluating Alarm Classifiers with High-confidence Data Programming","authors":"Sydney Pugh, I. Ruchkin, Christopher P. Bonafide, S. Demauro, O. Sokolsky, Insup Lee, James Weimer","doi":"10.1145/3549942","DOIUrl":"https://doi.org/10.1145/3549942","url":null,"abstract":"Classification of clinical alarms is at the heart of prioritization, suppression, integration, postponement, and other methods of mitigating alarm fatigue. Since these methods directly affect clinical care, alarm classifiers, such as intelligent suppression systems, need to be evaluated in terms of their sensitivity and specificity, which is typically calculated on a labeled dataset of alarms. Unfortunately, the collection and particularly labeling of such datasets requires substantial effort and time, thus deterring hospitals from investigating mitigations of alarm fatigue. This article develops a lightweight method for evaluating alarm classifiers without perfect alarm labels. The method relies on probabilistic labels obtained from data programming—a labeling paradigm based on combining noisy and cheap-to-obtain labeling heuristics. Based on these labels, the method produces confidence bounds for the sensitivity/specificity values from a hypothetical evaluation with manual labeling. Our experiments on five alarm datasets collected at Children’s Hospital of Philadelphia show that the proposed method provides accurate bounds on the classifier’s sensitivity/specificity, appropriately reflecting the uncertainty from noisy labeling and limited sample sizes.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 1","pages":"1 - 24"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46915880","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

Leveraging Mobile Sensing and Bayesian Change Point Analysis to Monitor Community-scale Behavioral Interventions: A Case Study on COVID-19 利用移动传感和贝叶斯变化点分析监测社区行为干预：新冠肺炎病例研究

ACM transactions on computing for healthcare Pub Date : 2022-07-20 DOI: 10.1145/3524886

Shashwat Kumar, Debajyoti Datta, Guimin Dong, Lihua Cai, Laura E. Barnes, M. Boukhechba

{"title":"Leveraging Mobile Sensing and Bayesian Change Point Analysis to Monitor Community-scale Behavioral Interventions: A Case Study on COVID-19","authors":"Shashwat Kumar, Debajyoti Datta, Guimin Dong, Lihua Cai, Laura E. Barnes, M. Boukhechba","doi":"10.1145/3524886","DOIUrl":"https://doi.org/10.1145/3524886","url":null,"abstract":"During pandemics, effective interventions require monitoring the problem at different scales and understanding the various tradeoffs between efficacy, privacy, and economic burden. To address these challenges, we propose a framework where we perform Bayesian change-point analysis on aggregate behavior markers extracted from mobile sensing data collected during the COVID-19 pandemic. Results generated by 598 participants for up to four months reveal rich insights: We observe an increase in smartphone usage around February 10th, followed by an increase in email usage around February 27th and, finally, a large reduction in participant’s mobility around March 13th. These behavior changes overlapped with important news events and government directives such as the naming of COVID-19, a spike in the number of reported cases in Europe, and the declaration of national emergency by President Trump. We also show that our detected change points align with changes in large scale external sources, including number of COVID-19 tweets, COVID-19 search traffic, and a large-scale foot traffic data collected by SafeGraph, providing further validation of our method. Our results show promise towards the feasibility of using mobile sensing to understand communities’ responses to public health interventions.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 1","pages":"1 - 13"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45574105","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

Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion 使用同位置uwb深度传感器融合的被动和环境感知家庭生命体征监测

ACM transactions on computing for healthcare Pub Date : 2022-07-20 DOI: 10.1145/3549941

Zongxing Xie, Bing Zhou, Xi Cheng, E. Schoenfeld, Fan Ye

{"title":"Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion","authors":"Zongxing Xie, Bing Zhou, Xi Cheng, E. Schoenfeld, Fan Ye","doi":"10.1145/3549941","DOIUrl":"https://doi.org/10.1145/3549941","url":null,"abstract":"Basic vital signs such as heart and respiratory rates (HR and RR) are essential bio-indicators. Their longitudinal in-home collection enables prediction and detection of disease onset and change, providing for earlier health intervention. In this article, we propose a robust, non-touch vital signs monitoring system using a pair of co-located Ultra-Wide Band (UWB) and depth sensors. By extensive manual examination, we identify four typical temporal and spectral signal patterns and their suitable vital sign estimators. We devise a probabilistic weighted framework (PWF) that quantifies evidence of these patterns to update the weighted combination of estimator output to track the vital signs robustly. We also design a “heatmap”-based signal quality detector to exclude the disturbed signal from inadvertent motions. To monitor multiple co-habiting subjects in-home, we build a two-branch long short-term memory (LSTM) neural network to distinguish between individuals and their activities, providing activity context crucial to disambiguating critical from normal vital sign variability. To achieve reliable context annotation, we carefully devise the feature set of the consecutive skeletal poses from the depth data, and develop a probabilistic tracking model to tackle non-line-of-sight (NLOS) cases. Our experimental results demonstrate the robustness and superior performance of the individual modules as well as the end-to-end system for passive and context-aware vital sign monitoring.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 1","pages":"1 - 31"},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64053506","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

BioLumin: An Immersive Mixed Reality Experience for Interactive Microscopic Visualization and Biomedical Research Annotation BioLumin:一种用于交互式微观可视化和生物医学研究注释的沉浸式混合现实体验

ACM transactions on computing for healthcare Pub Date : 2022-07-18 DOI: 10.1145/3548777

Aviv Elor, Steve Whittaker, Sri Kurniawan, Sam Michael

{"title":"BioLumin: An Immersive Mixed Reality Experience for Interactive Microscopic Visualization and Biomedical Research Annotation","authors":"Aviv Elor, Steve Whittaker, Sri Kurniawan, Sam Michael","doi":"10.1145/3548777","DOIUrl":"https://doi.org/10.1145/3548777","url":null,"abstract":"Many recent breakthroughs in medical diagnostics and drug discovery arise from deploying machine learning algorithms to large-scale data sets. However, a significant obstacle to such approaches is that they depend on high-quality annotations generated by domain experts. This study develops and evaluates BioLumin, a novel immersive mixed reality environment that enables users to virtually shrink down to the microscopic level for navigation and annotation of 3D reconstructed images. We discuss how domain experts were consulted in the specification of a pipeline to enable automatic reconstruction of biological models for mixed reality environments, driving the design of a 3DUI system to explore whether such a system allows accurate annotation of complex medical data by non-experts. To examine the usability and feasibility of BioLumin, we evaluated our prototype through a multi-stage mixed-method approach. First, three domain experts offered expert reviews, and subsequently, nineteen non-expert users performed representative annotation tasks in a controlled setting. The results indicated that the mixed reality system was learnable and that non-experts could generate high-quality 3D annotations after a short training session. Lastly, we discuss design considerations for future tools like BioLumin in medical and more general scientific contexts.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 1","pages":"1 - 28"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47033654","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

SCouT: Synthetic Counterfactuals via Spatiotemporal Transformers for Actionable Healthcare SCouT：通过时空变换器实现可操作医疗保健的合成反制造

ACM transactions on computing for healthcare Pub Date : 2022-07-09 DOI: 10.1145/3617180

Bhishma Dedhia, Roshini Balasubramanian, N. Jha

{"title":"SCouT: Synthetic Counterfactuals via Spatiotemporal Transformers for Actionable Healthcare","authors":"Bhishma Dedhia, Roshini Balasubramanian, N. Jha","doi":"10.1145/3617180","DOIUrl":"https://doi.org/10.1145/3617180","url":null,"abstract":"The Synthetic Control method has pioneered a class of powerful data-driven techniques to estimate the counterfactual reality of a unit from donor units. At its core, the technique involves a linear model fitted on the pre-intervention period that combines donor outcomes to yield the counterfactual. However, linearly combining spatial information at each time instance using time-agnostic weights fails to capture important inter-unit and intra-unit temporal contexts and complex nonlinear dynamics of real data. We instead propose an approach to use local spatiotemporal information before the onset of the intervention as a promising way to estimate the counterfactual sequence. To this end, we suggest a Transformer model that leverages particular positional embeddings, a modified decoder attention mask, and a novel pre-training task to perform spatiotemporal sequence-to-sequence modeling. Our experiments on synthetic data demonstrate the efficacy of our method in the typical small donor pool setting and its robustness against noise. We also generate actionable healthcare insights at the population and patient levels by simulating a state-wide public health policy to evaluate its effectiveness, an in silico trial for asthma medications to support randomized controlled trials, and a medical intervention for patients with Friedreich’s ataxia to improve clinical decision-making and promote personalized therapy.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42581687","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

Obesity Prediction with EHR Data: A deep learning approach with interpretable elements. 用电子病历数据预测肥胖:一种具有可解释元素的深度学习方法。

ACM transactions on computing for healthcare Pub Date : 2022-07-01 Epub Date: 2022-04-07 DOI: 10.1145/3506719

Mehak Gupta, Thao-Ly T Phan, H Timothy Bunnell, Rahmatollah Beheshti

{"title":"Obesity Prediction with EHR Data: A deep learning approach with interpretable elements.","authors":"Mehak Gupta, Thao-Ly T Phan, H Timothy Bunnell, Rahmatollah Beheshti","doi":"10.1145/3506719","DOIUrl":"https://doi.org/10.1145/3506719","url":null,"abstract":"Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children's data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this paper, we present a deep learning model designed for predicting future obesity patterns from generally available items on children's medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system in the US. We adopt a general LSTM network architecture and train our proposed model using both static and dynamic EHR data. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp. Our model is used to predict obesity for ages between 3-20 years using the data from 1-3 years in advance. We compare the performance of our LSTM model with a series of existing studies in the literature and show it outperforms their performance in most age ranges.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221869/pdf/nihms-1811884.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40402980","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: 35

Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention. 检测基于智能手表的行为变化，以应对多领域脑健康干预。

IF 4

ACM transactions on computing for healthcare Pub Date : 2022-07-01 Epub Date: 2022-04-07 DOI: 10.1145/3508020

Diane J Cook, Miranda Strickland, Maureen Schmitter-Edgecombe

{"title":"Detecting Smartwatch-based Behavior Change in Response to a Multi-domain Brain Health Intervention.","authors":"Diane J Cook, Miranda Strickland, Maureen Schmitter-Edgecombe","doi":"10.1145/3508020","DOIUrl":"10.1145/3508020","url":null,"abstract":"In this study, we introduce and validate a computational method to detect lifestyle change that occurs in response to a multi-domain healthy brain aging intervention. To detect behavior change, digital behavior markers (DM) are extracted from smartwatch sensor data and a Permutation-based Change Detection (PCD) algorithm quantifies the change in marker-based behavior from a pre-intervention, one-week baseline. To validate the method, we verify that changes are successfully detected from synthetic data with known pattern differences. Next, we employ this method to detect overall behavior change for n=28 BHI subjects and n=17 age-matched control subjects. For these individuals, we observe a monotonic increase in behavior change from the baseline week with a slope of 0.7460 for the intervention group and a slope of 0.0230 for the control group. Finally, we utilize a random forest algorithm to perform leave-one-subject-out prediction of intervention versus control subjects based on digital marker delta values. The random forest predicts whether the subject is in the intervention or control group with an accuracy of 0.87. This work has implications for capturing objective, continuous data to inform our understanding of intervention adoption and impact.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 3","pages":""},"PeriodicalIF":4.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268550/pdf/nihms-1768502.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40582785","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

Food, Mood, Context: Examining College Students’ Eating Context and Mental Well-being 食物、情绪、情境:大学生饮食情境与心理健康的关系

ACM transactions on computing for healthcare Pub Date : 2022-06-01 DOI: 10.1145/3533390

M. B. Morshed, S. S. Kulkarni, Koustuv Saha, Richard Li, L. G. Roper, L. Nachman, Hong Lu, Lucia Mirabella, Sanjeev Srivastava, K. de Barbaro, M. de Choudhury, T. Plötz, G. Abowd

{"title":"Food, Mood, Context: Examining College Students’ Eating Context and Mental Well-being","authors":"M. B. Morshed, S. S. Kulkarni, Koustuv Saha, Richard Li, L. G. Roper, L. Nachman, Hong Lu, Lucia Mirabella, Sanjeev Srivastava, K. de Barbaro, M. de Choudhury, T. Plötz, G. Abowd","doi":"10.1145/3533390","DOIUrl":"https://doi.org/10.1145/3533390","url":null,"abstract":"Deviant eating behavior such as skipping meals and consuming unhealthy meals has a significant association with mental well-being in college students. However, there is more to what an individual eats. While eating patterns form a critical component of their mental well-being, insights and assessments related to the interplay of eating patterns and mental well-being remain under-explored in theory and practice. To bridge this gap, we use an existing real-time eating detection system that captures context during meals to examine how college students’ eating context associates with their mental well-being, particularly their affect, anxiety, depression, and stress. Our findings suggest that students’ irregularity or skipping meals negatively correlates with their mental well-being, whereas eating with family and friends positively correlates with improved mental well-being. We discuss the implications of our study in designing dietary intervention technologies and guiding student-centric well-being technologies.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 1","pages":"1 - 26"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43058553","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: 8

Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges 医疗保健领域的联邦学习——管道、应用和挑战

ACM transactions on computing for healthcare Pub Date : 2022-05-12 DOI: 10.1145/3533708

Madhura Joshi, Ankit Pal, Malaikannan Sankarasubbu

引用次数: 30

SpiroMask: Measuring Lung Function Using Consumer-Grade Masks SpiroMask:使用消费级口罩测量肺功能

ACM transactions on computing for healthcare Pub Date : 2022-01-23 DOI: 10.1145/3570167

Rishiraj Adhikary, Dhruvi Lodhavia, Chris Francis, Rohit Patil, Tanmay Srivastava, Prerna Khanna, Nipun Batra, Joseph Breda, J. Peplinski, Shwetak N. Patel

引用次数: 2