Pub Date : 2022-10-01Epub Date: 2022-11-03DOI: 10.1145/3531231
Gina Sprint, Diane J Cook, Maureen Schmitter-Edgecombe, Lawrence B Holder
New modes of technology are offering unprecedented opportunities to unobtrusively collect data about people's behavior. While there are many use cases for such information, we explore its utility for predicting multiple clinical assessment scores. Because clinical assessments are typically used as screening tools for impairment and disease, such as mild cognitive impairment (MCI), automatically mapping behavioral data to assessment scores can help detect changes in health and behavior across time. In this paper, we aim to extract behavior markers from two modalities, a smart home environment and a custom digital memory notebook app, for mapping to ten clinical assessments that are relevant for monitoring MCI onset and changes in cognitive health. Smart home-based behavior markers reflect hourly, daily, and weekly activity patterns, while app-based behavior markers reflect app usage and writing content/style derived from free-form journal entries. We describe machine learning techniques for fusing these multimodal behavior markers and utilizing joint prediction. We evaluate our approach using three regression algorithms and data from 14 participants with MCI living in a smart home environment. We observed moderate to large correlations between predicted and ground-truth assessment scores, ranging from r = 0.601 to r = 0.871 for each clinical assessment.
新的技术模式为不露痕迹地收集人们的行为数据提供了前所未有的机会。虽然此类信息有很多用例，但我们要探讨的是它在预测多种临床评估分数方面的效用。由于临床评估通常被用作损伤和疾病（如轻度认知障碍（MCI））的筛查工具，因此将行为数据自动映射到评估分数有助于检测不同时期的健康和行为变化。在本文中，我们旨在从智能家居环境和定制数字记忆笔记本应用程序这两种模式中提取行为标记，并将其映射到与监测 MCI 发病和认知健康变化相关的十项临床评估中。基于智能家居的行为标记反映了每小时、每天和每周的活动模式，而基于应用程序的行为标记则反映了应用程序的使用情况以及从自由形式的日记条目中提取的写作内容/风格。我们介绍了融合这些多模态行为标记并利用联合预测的机器学习技术。我们使用三种回归算法和 14 名生活在智能家居环境中的 MCI 患者的数据对我们的方法进行了评估。我们观察到预测得分和地面实况评估得分之间存在中等到较大的相关性，每项临床评估的相关性从 r = 0.601 到 r = 0.871 不等。
{"title":"Multimodal Fusion of Smart Home and Text-based Behavior Markers for Clinical Assessment Prediction.","authors":"Gina Sprint, Diane J Cook, Maureen Schmitter-Edgecombe, Lawrence B Holder","doi":"10.1145/3531231","DOIUrl":"10.1145/3531231","url":null,"abstract":"<p><p>New modes of technology are offering unprecedented opportunities to unobtrusively collect data about people's behavior. While there are many use cases for such information, we explore its utility for predicting multiple clinical assessment scores. Because clinical assessments are typically used as screening tools for impairment and disease, such as mild cognitive impairment (MCI), automatically mapping behavioral data to assessment scores can help detect changes in health and behavior across time. In this paper, we aim to extract behavior markers from two modalities, a smart home environment and a custom digital memory notebook app, for mapping to ten clinical assessments that are relevant for monitoring MCI onset and changes in cognitive health. Smart home-based behavior markers reflect hourly, daily, and weekly activity patterns, while app-based behavior markers reflect app usage and writing content/style derived from free-form journal entries. We describe machine learning techniques for fusing these multimodal behavior markers and utilizing joint prediction. We evaluate our approach using three regression algorithms and data from 14 participants with MCI living in a smart home environment. We observed moderate to large correlations between predicted and ground-truth assessment scores, ranging from <i>r</i> = 0.601 to <i>r</i> = 0.871 for each clinical assessment.</p>","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9645787/pdf/nihms-1822476.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10608022","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}
Due to the increase in life expectancy in advanced societies leading to an increase in population age, data-driven systems are receiving more attention to support the older people by monitoring their health. Intelligent sensor networks provide the ability to monitor their activities without interfering with routine life. Data collected from smart homes can be used in a variety of data-driven analyses, including behavior prediction. Due to privacy concerns and the cost and time required to collect data, synthetic data generation methods have been considered seriously by the research community. In this article, we introduce a new Generative Adversarial Network (GAN) algorithm, namely, BehavGAN, that applies GAN to the problem of behavior sequence generation. This is achieved by learning the features of a target dataset and utilizing a new application for GANs in the simulation of older people’s behaviors. We also propose an effective reward function for GAN back-propagation by incorporating n-gram-based similarity measures in the reinforcement mechanism. We evaluate our proposed algorithm by generating a dataset of human behavior sequences. Our results show that BehavGAN is more effective in generating behavior sequences compared to MLE, LeakGAN, and the original SeqGAN algorithms in terms of both similarity and diversity of generated data. Our proposed algorithm outperforms current state-of-the-art methods when it comes to generating behavior sequences consisting of limited-space sequence tokens.
{"title":"Synthetic Behavior Sequence Generation Using Generative Adversarial Networks","authors":"Fatemeh Akbari, K. Sartipi, Norm Archer","doi":"10.1145/3563950","DOIUrl":"https://doi.org/10.1145/3563950","url":null,"abstract":"Due to the increase in life expectancy in advanced societies leading to an increase in population age, data-driven systems are receiving more attention to support the older people by monitoring their health. Intelligent sensor networks provide the ability to monitor their activities without interfering with routine life. Data collected from smart homes can be used in a variety of data-driven analyses, including behavior prediction. Due to privacy concerns and the cost and time required to collect data, synthetic data generation methods have been considered seriously by the research community. In this article, we introduce a new Generative Adversarial Network (GAN) algorithm, namely, BehavGAN, that applies GAN to the problem of behavior sequence generation. This is achieved by learning the features of a target dataset and utilizing a new application for GANs in the simulation of older people’s behaviors. We also propose an effective reward function for GAN back-propagation by incorporating n-gram-based similarity measures in the reinforcement mechanism. We evaluate our proposed algorithm by generating a dataset of human behavior sequences. Our results show that BehavGAN is more effective in generating behavior sequences compared to MLE, LeakGAN, and the original SeqGAN algorithms in terms of both similarity and diversity of generated data. Our proposed algorithm outperforms current state-of-the-art methods when it comes to generating behavior sequences consisting of limited-space sequence tokens.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"4 1","pages":"1 - 23"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46099276","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}
K. Amaral, Zihan Li, W. Ding, S. Crouter, Ping Chen
SummerTime seeks to summarize global time-series signals and provides a fixed-length, robust representation of the variable-length time series. Many machine learning methods depend on data instances with a fixed number of features. As a result, those methods cannot be directly applied to variable-length time series data. Existing methods such as sliding windows can lose minority local information. Summarization conducted by the SummerTime method will be a fixed-length feature vector which can be used in place of the time series dataset for use with classical machine learning methods. We use Gaussian Mixture models (GMM) over small same-length disjoint windows in the time series to group local data into clusters. The time series’ rate of membership for each cluster will be a feature in the summarization. By making use of variational methods, GMM converges to a more robust mixture, meaning the clusters are more resistant to noise and overfitting. Further, the model is naturally capable of converging to an appropriate cluster count. We validate our method on a challenging real-world dataset, an imbalanced physical activity dataset with a variable-length time series structure. We compare our results to state-of-the-art studies and show high-quality improvement by classifying with only the summarization.
{"title":"SummerTime: Variable-length Time Series Summarization with Application to Physical Activity Analysis","authors":"K. Amaral, Zihan Li, W. Ding, S. Crouter, Ping Chen","doi":"10.1145/3532628","DOIUrl":"https://doi.org/10.1145/3532628","url":null,"abstract":"SummerTime seeks to summarize global time-series signals and provides a fixed-length, robust representation of the variable-length time series. Many machine learning methods depend on data instances with a fixed number of features. As a result, those methods cannot be directly applied to variable-length time series data. Existing methods such as sliding windows can lose minority local information. Summarization conducted by the SummerTime method will be a fixed-length feature vector which can be used in place of the time series dataset for use with classical machine learning methods. We use Gaussian Mixture models (GMM) over small same-length disjoint windows in the time series to group local data into clusters. The time series’ rate of membership for each cluster will be a feature in the summarization. By making use of variational methods, GMM converges to a more robust mixture, meaning the clusters are more resistant to noise and overfitting. Further, the model is naturally capable of converging to an appropriate cluster count. We validate our method on a challenging real-world dataset, an imbalanced physical activity dataset with a variable-length time series structure. We compare our results to state-of-the-art studies and show high-quality improvement by classifying with only the summarization.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 1","pages":"1 - 15"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48949440","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}
Hieu Hanh Le, Tatsuhiro Yamada, Yuichi Honda, T. Sakamoto, Ryosuke Matsuo, Tomoyoshi Yamazaki, Kenji Araki, H. Yokota
Electronic medical record systems have been adopted by many large hospitals worldwide, enabling the recorded data to be analyzed by various computer-based techniques to gain a better understanding of hospital-based disease treatments. Among such techniques, sequential pattern mining, already widely used for data mining and knowledge discovery in other application domains, has shown great potential for discovering frequent patterns in sequences of disease treatments. However, studies have yet to evaluate the use of medical-order sequence variants, where a “frequent pattern” can include some limited variations to the pattern, or have considered the factors that lead to these variants. Such a study would be meaningful for medical tasks such as improving the quality of a particular treatment method, comparing treatments with multiple hospitals, recommending the best-suited treatment for each patient, and optimizing the running costs in hospitals. This article proposes methods for evaluating medical-order sequence variants and understanding variant factors based on a statistical approach. We consider the safety and efficiency of sequences and related information about the variants, such as gender, age, and test results from hospitals. Our proposal has been demonstrated as effective by experimentally evaluating an electronic medical record system’s real dataset and obtaining feedback from medical workers. The experimental results indicate that the medical treatment history and specimen test results after hospitalization are significant in identifying the factors that lead to variants.
{"title":"Methods for Analyzing Medical-Order Sequence Variants in Sequential Pattern Mining for Electronic Medical Record Systems","authors":"Hieu Hanh Le, Tatsuhiro Yamada, Yuichi Honda, T. Sakamoto, Ryosuke Matsuo, Tomoyoshi Yamazaki, Kenji Araki, H. Yokota","doi":"10.1145/3561825","DOIUrl":"https://doi.org/10.1145/3561825","url":null,"abstract":"Electronic medical record systems have been adopted by many large hospitals worldwide, enabling the recorded data to be analyzed by various computer-based techniques to gain a better understanding of hospital-based disease treatments. Among such techniques, sequential pattern mining, already widely used for data mining and knowledge discovery in other application domains, has shown great potential for discovering frequent patterns in sequences of disease treatments. However, studies have yet to evaluate the use of medical-order sequence variants, where a “frequent pattern” can include some limited variations to the pattern, or have considered the factors that lead to these variants. Such a study would be meaningful for medical tasks such as improving the quality of a particular treatment method, comparing treatments with multiple hospitals, recommending the best-suited treatment for each patient, and optimizing the running costs in hospitals. This article proposes methods for evaluating medical-order sequence variants and understanding variant factors based on a statistical approach. We consider the safety and efficiency of sequences and related information about the variants, such as gender, age, and test results from hospitals. Our proposal has been demonstrated as effective by experimentally evaluating an electronic medical record system’s real dataset and obtaining feedback from medical workers. The experimental results indicate that the medical treatment history and specimen test results after hospitalization are significant in identifying the factors that lead to variants.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"4 1","pages":"1 - 28"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43050142","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}
Yiyuan Wang, Guillaume Wattelez, S. Frayon, C. Caillaud, O. Galy, K. Yacef
Obesity is a global health concern associated with various demographic and lifestyle factors including physical activity (PA). Research studies generally used self-reported PA data or, when accelerometer-based activity trackers were used, highly aggregated data (e.g., daily average). This suggests that the rich potential of detailed activity tracker data is largely under-exploited and that deeper analyses may help better understand such relationships. This is particularly true in children and adolescents who are distinct and engage more in bursts of PA. This article presents ABIPA, a machine learning-based methodology that integrates various aspects of accelerometer-based PA data into weight status prediction for adolescents. We propose a method to derive features regarding the structure of different PA time series using Auto-Regressive Integrated Moving Average (ARIMA). The ARIMA-based PA features are combined with other individual attributes to predict weight status and the importance of these features is further unveiled. We apply ABIPA to a dataset about young adolescents (N = 206) containing, for each participant, a 7-day continuous accelerometer dataset (60 Hz, GENEActiv tracker from ActivInsights) and a range of their socio-demographic, anthropometric, and lifestyle information. The results indicate that our method provides a practical approach for integrating accelerometer-based PA patterns into weight status prediction and paves the way for validating their importance in understanding obesity factors.
{"title":"ABIPA: ARIMA-Based Integration of Accelerometer-Based Physical Activity for Adolescent Weight Status Prediction","authors":"Yiyuan Wang, Guillaume Wattelez, S. Frayon, C. Caillaud, O. Galy, K. Yacef","doi":"10.1145/3561611","DOIUrl":"https://doi.org/10.1145/3561611","url":null,"abstract":"Obesity is a global health concern associated with various demographic and lifestyle factors including physical activity (PA). Research studies generally used self-reported PA data or, when accelerometer-based activity trackers were used, highly aggregated data (e.g., daily average). This suggests that the rich potential of detailed activity tracker data is largely under-exploited and that deeper analyses may help better understand such relationships. This is particularly true in children and adolescents who are distinct and engage more in bursts of PA. This article presents ABIPA, a machine learning-based methodology that integrates various aspects of accelerometer-based PA data into weight status prediction for adolescents. We propose a method to derive features regarding the structure of different PA time series using Auto-Regressive Integrated Moving Average (ARIMA). The ARIMA-based PA features are combined with other individual attributes to predict weight status and the importance of these features is further unveiled. We apply ABIPA to a dataset about young adolescents (N = 206) containing, for each participant, a 7-day continuous accelerometer dataset (60 Hz, GENEActiv tracker from ActivInsights) and a range of their socio-demographic, anthropometric, and lifestyle information. The results indicate that our method provides a practical approach for integrating accelerometer-based PA patterns into weight status prediction and paves the way for validating their importance in understanding obesity factors.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"4 1","pages":"1 - 19"},"PeriodicalIF":0.0,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43367433","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}
Keqin Shi, Zhen Chen, Xuejing Li, Z. Xiao, Weiqiang Sun, Weisheng Hu
As an accurate measurement of physical activity, step counts data can be collected expediently by smartphones and wearable devices. Complete and high time-resolution step counts data record the time and intensity of individuals’ physical activity in a day, and can be used to mine activity habits or to recommend customized workout plans. However, sparse step counts data are common in practice due to hardware and software limitations. Understanding the value of sparse step counts data can contribute to its application in healthcare, and also can help us design cost-effective hardware and software. In this article, we aim to infer activity patterns from sparse step counts data. We design a deep learning model based on recurrent neural networks, namely MLP-GRU, which considers bidirectional short-term dependency and long-term regularity of sparse step counts data, and implements data-driven imputation and classification. We also develop an interpretable and elastic method to obtain sparse step counts data labeled with multi-granular activity patterns to train MLP-GRU. Evaluations on real-world datasets reveal that MLP-GRU outperforms other strong baseline methods. The results also show that activity patterns can be inferred from extremely sparse step counts data with high accuracy, provided that proper granularity is used for data of different sparsity.
{"title":"Inferring Activity Patterns from Sparse Step Counts Data with Recurrent Neural Networks","authors":"Keqin Shi, Zhen Chen, Xuejing Li, Z. Xiao, Weiqiang Sun, Weisheng Hu","doi":"10.1145/3560468","DOIUrl":"https://doi.org/10.1145/3560468","url":null,"abstract":"As an accurate measurement of physical activity, step counts data can be collected expediently by smartphones and wearable devices. Complete and high time-resolution step counts data record the time and intensity of individuals’ physical activity in a day, and can be used to mine activity habits or to recommend customized workout plans. However, sparse step counts data are common in practice due to hardware and software limitations. Understanding the value of sparse step counts data can contribute to its application in healthcare, and also can help us design cost-effective hardware and software. In this article, we aim to infer activity patterns from sparse step counts data. We design a deep learning model based on recurrent neural networks, namely MLP-GRU, which considers bidirectional short-term dependency and long-term regularity of sparse step counts data, and implements data-driven imputation and classification. We also develop an interpretable and elastic method to obtain sparse step counts data labeled with multi-granular activity patterns to train MLP-GRU. Evaluations on real-world datasets reveal that MLP-GRU outperforms other strong baseline methods. The results also show that activity patterns can be inferred from extremely sparse step counts data with high accuracy, provided that proper granularity is used for data of different sparsity.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"4 1","pages":"1 - 20"},"PeriodicalIF":0.0,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43316831","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}
Hashini Senaratne, S. Oviatt, K. Ellis, Glenn Melvin
Recently, interest has grown in the assessment of anxiety that leverages human physiological and behavioral data to address the drawbacks of current subjective clinical assessments. Complex experiences of anxiety vary on multiple characteristics, including triggers, responses, duration and severity, and impact differently on the risk of anxiety disorders. This article reviews the past decade of studies that objectively analyzed various anxiety characteristics related to five common anxiety disorders in adults utilizing features of cardiac, electrodermal, blood pressure, respiratory, vocal, posture, movement, and eye metrics. Its originality lies in the synthesis and interpretation of consistently discovered heterogeneous predictors of anxiety and multimodal-multisensor analytics based on them. We reveal that few anxiety characteristics have been evaluated using multimodal-multisensor metrics, and many of the identified predictive features are confounded. As such, objective anxiety assessments are not yet complete or precise. That said, few multimodal-multisensor systems evaluated indicate an approximately 11.73% performance gain compared to unimodal systems, highlighting a promising powerful tool. We suggest six high-priority future directions to address the current gaps and limitations in infrastructure, basic knowledge, and application areas. Action in these directions will expedite the discovery of rich, accurate, continuous, and objective assessments and their use in impactful end-user applications.
{"title":"A Critical Review of Multimodal-multisensor Analytics for Anxiety Assessment","authors":"Hashini Senaratne, S. Oviatt, K. Ellis, Glenn Melvin","doi":"10.1145/3556980","DOIUrl":"https://doi.org/10.1145/3556980","url":null,"abstract":"Recently, interest has grown in the assessment of anxiety that leverages human physiological and behavioral data to address the drawbacks of current subjective clinical assessments. Complex experiences of anxiety vary on multiple characteristics, including triggers, responses, duration and severity, and impact differently on the risk of anxiety disorders. This article reviews the past decade of studies that objectively analyzed various anxiety characteristics related to five common anxiety disorders in adults utilizing features of cardiac, electrodermal, blood pressure, respiratory, vocal, posture, movement, and eye metrics. Its originality lies in the synthesis and interpretation of consistently discovered heterogeneous predictors of anxiety and multimodal-multisensor analytics based on them. We reveal that few anxiety characteristics have been evaluated using multimodal-multisensor metrics, and many of the identified predictive features are confounded. As such, objective anxiety assessments are not yet complete or precise. That said, few multimodal-multisensor systems evaluated indicate an approximately 11.73% performance gain compared to unimodal systems, highlighting a promising powerful tool. We suggest six high-priority future directions to address the current gaps and limitations in infrastructure, basic knowledge, and application areas. Action in these directions will expedite the discovery of rich, accurate, continuous, and objective assessments and their use in impactful end-user applications.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":"3 1","pages":"1 - 42"},"PeriodicalIF":0.0,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49657787","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}
A national example of open source digital healthcare is critiqued. The code for implementing numeric patient identifiers is surprisingly naïve and bug-ridden, despite patient identifiers being computationally trivial and a critical component of reliable healthcare. The issues raised are shown to be widespread, long term, and apparently unrecognized. Problems are traced back to inadequacies in the relevant standards, and, at every stage, regulation through to development, inadequate Software Engineering input. An important finding is that the relevant healthcare standards are inconsistent and written without sufficient rigor to be at all constructive for implementing digital systems. The widely recognized problems of interoperability may be traced back to diverse (and buggy) interpretations of vague standards.
{"title":"NHS Number Open Source Software: Implications for Digital Health Regulation and Development","authors":"H. Thimbleby","doi":"10.1145/3538382","DOIUrl":"https://doi.org/10.1145/3538382","url":null,"abstract":"A national example of open source digital healthcare is critiqued. The code for implementing numeric patient identifiers is surprisingly naïve and bug-ridden, despite patient identifiers being computationally trivial and a critical component of reliable healthcare. The issues raised are shown to be widespread, long term, and apparently unrecognized. Problems are traced back to inadequacies in the relevant standards, and, at every stage, regulation through to development, inadequate Software Engineering input. An important finding is that the relevant healthcare standards are inconsistent and written without sufficient rigor to be at all constructive for implementing digital systems. The widely recognized problems of interoperability may be traced back to diverse (and buggy) interpretations of vague standards.","PeriodicalId":72043,"journal":{"name":"ACM transactions on computing for healthcare","volume":" ","pages":"1 - 26"},"PeriodicalIF":0.0,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46769784","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}
Sydney Pugh, I. Ruchkin, Christopher P. Bonafide, S. Demauro, O. Sokolsky, Insup Lee, James Weimer
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.
{"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}
Shashwat Kumar, Debajyoti Datta, Guimin Dong, Lihua Cai, Laura E. Barnes, M. Boukhechba
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.
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