A method to screen for jaundice in neonates using a digital image of the sclera is proposed. The RGB pixel values from a raw format image are used to derive an estimate for the total serum bilirubin (TSB). A study at UCH Neonatal Unit found a correlation of r=0.71 (p<0.01) between measured TSB and TSB estimated by this method. The advantages of using a smartphone camera as a mobile screening device are discussed.
{"title":"Screening for Neonatal Jaundice with a Smartphone","authors":"Felix Outlaw, J. Meek, L. MacDonald, T. Leung","doi":"10.1145/3079452.3079488","DOIUrl":"https://doi.org/10.1145/3079452.3079488","url":null,"abstract":"A method to screen for jaundice in neonates using a digital image of the sclera is proposed. The RGB pixel values from a raw format image are used to derive an estimate for the total serum bilirubin (TSB). A study at UCH Neonatal Unit found a correlation of r=0.71 (p<0.01) between measured TSB and TSB estimated by this method. The advantages of using a smartphone camera as a mobile screening device are discussed.","PeriodicalId":245682,"journal":{"name":"Proceedings of the 2017 International Conference on Digital Health","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127355167","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}
Chronic Kidney Disease (CKD) is one of the deadliest diseases in the world, with 10% of the global population affected by the disease. Identifying subpopulations with characteristic disease progressions is important to find more efficient treatments for patients with this disease. The abundance of electronic health records (EHR) data can be used to find meaningful subtypes for CKD but comes with challenges during analysis, including irregular data sampling, and skewness in the data collected over time. In this paper, multiple regression techniques were used to fill in the missing estimated glomerular filtration rate (or eGFR -- a key measure for kidney function) trajectory data, so it can be clustered effectively. Clustering is applied to the enhanced data to obtain six subtypes, which capture crucial trends in the disease progression of patients. Moreover, the characteristics of patients in each of the subtypes had minor differences from others. These characteristics demonstrate risk factors and positive lifestyles choices of patients with CKD, which can help develop new treatments for CKD.
{"title":"Automatic Extraction of Deep Phenotypes for Precision Medicine in Chronic Kidney Disease","authors":"Prerna Singh, V. Chandola, C. Fox","doi":"10.1145/3079452.3079489","DOIUrl":"https://doi.org/10.1145/3079452.3079489","url":null,"abstract":"Chronic Kidney Disease (CKD) is one of the deadliest diseases in the world, with 10% of the global population affected by the disease. Identifying subpopulations with characteristic disease progressions is important to find more efficient treatments for patients with this disease. The abundance of electronic health records (EHR) data can be used to find meaningful subtypes for CKD but comes with challenges during analysis, including irregular data sampling, and skewness in the data collected over time. In this paper, multiple regression techniques were used to fill in the missing estimated glomerular filtration rate (or eGFR -- a key measure for kidney function) trajectory data, so it can be clustered effectively. Clustering is applied to the enhanced data to obtain six subtypes, which capture crucial trends in the disease progression of patients. Moreover, the characteristics of patients in each of the subtypes had minor differences from others. These characteristics demonstrate risk factors and positive lifestyles choices of patients with CKD, which can help develop new treatments for CKD.","PeriodicalId":245682,"journal":{"name":"Proceedings of the 2017 International Conference on Digital Health","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752155","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}
M. C. Martin, M. Bouamrane, K. Kavanagh, P. Woolman
'Closer to Home' is a co-ordinated programme in NHS Forth Valley aiming to improve the provision of community care for frail and older people at increased risk of unscheduled hospital admission. Evaluating the cost-effectiveness of such initiatives is essential towards shaping policies and optimising elderly care provision in the community. This paper underlines the early stages of the development of a data-analytics evaluation framework for "Closer to Home," incorporating all dimensions of the RE-AIM model for evaluating public health impact in health promotion interventions. The evaluation framework currently consists in: (1) understanding and documenting the context, (2) identifying the range of relevant data, (3) identifying outcomes and performance measures, and (4) synthesising all of the above into a coherent evaluation of the effectiveness of the programme. The purpose of this paper is to share our experience of the challenges and opportunities which arise in the early stages of developing a multi-faceted quality improvement programme evaluation framework.
{"title":"Preventing Frail and Elderly Hospital Admissions: Developing an Evaluation Framework for the \"Closer to Home\" Quality Improvement Programme in NHS Forth Valley","authors":"M. C. Martin, M. Bouamrane, K. Kavanagh, P. Woolman","doi":"10.1145/3079452.3079481","DOIUrl":"https://doi.org/10.1145/3079452.3079481","url":null,"abstract":"'Closer to Home' is a co-ordinated programme in NHS Forth Valley aiming to improve the provision of community care for frail and older people at increased risk of unscheduled hospital admission. Evaluating the cost-effectiveness of such initiatives is essential towards shaping policies and optimising elderly care provision in the community. This paper underlines the early stages of the development of a data-analytics evaluation framework for \"Closer to Home,\" incorporating all dimensions of the RE-AIM model for evaluating public health impact in health promotion interventions. The evaluation framework currently consists in: (1) understanding and documenting the context, (2) identifying the range of relevant data, (3) identifying outcomes and performance measures, and (4) synthesising all of the above into a coherent evaluation of the effectiveness of the programme. The purpose of this paper is to share our experience of the challenges and opportunities which arise in the early stages of developing a multi-faceted quality improvement programme evaluation framework.","PeriodicalId":245682,"journal":{"name":"Proceedings of the 2017 International Conference on Digital Health","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121381738","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}
Starting from our previous work on a digital picture frame - the CARE system - that interleaves a picture display mode with a recommender mode to promote a healthy life-style and to increase well-being of elderly people, this paper investigates the use of gamification as a means to increase user appreciation of the CARE system. To this end, we arranged two co-design workshops with peer-groups of senior citizens. We report on outcomes of the workshops and draw conclusions for a gamified version of CARE.
{"title":"Towards a Gamified Recommender System for the Elderly","authors":"Madita Herpich, T. Rist, A. Seiderer, E. André","doi":"10.1145/3079452.3079500","DOIUrl":"https://doi.org/10.1145/3079452.3079500","url":null,"abstract":"Starting from our previous work on a digital picture frame - the CARE system - that interleaves a picture display mode with a recommender mode to promote a healthy life-style and to increase well-being of elderly people, this paper investigates the use of gamification as a means to increase user appreciation of the CARE system. To this end, we arranged two co-design workshops with peer-groups of senior citizens. We report on outcomes of the workshops and draw conclusions for a gamified version of CARE.","PeriodicalId":245682,"journal":{"name":"Proceedings of the 2017 International Conference on Digital Health","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134439040","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}
People regularly use web search and social media to investigate health related issues. This type of Internet data might contain misinformation i.e incorrect information which contradicts current established medical understanding. If people are influenced by the presented misinformation in these sources, they can make harmful decisions about their health. Our research goal is to investigate the affect of Internet data on people's health. Our current findings suggest that people can be potentially harmed by search engine results. Furthermore, we successfully built a high precision approach to track misinformation in social media. In this paper, we briefly discuss our ongoing work results. Thereafter, we propose a research plan to understand possible mechanisms of misinformation's effect on people and possible impacts of these misinformation on public health.
{"title":"Health Misinformation in Search and Social Media","authors":"Amira Ghenai","doi":"10.1145/3079452.3079483","DOIUrl":"https://doi.org/10.1145/3079452.3079483","url":null,"abstract":"People regularly use web search and social media to investigate health related issues. This type of Internet data might contain misinformation i.e incorrect information which contradicts current established medical understanding. If people are influenced by the presented misinformation in these sources, they can make harmful decisions about their health. Our research goal is to investigate the affect of Internet data on people's health. Our current findings suggest that people can be potentially harmed by search engine results. Furthermore, we successfully built a high precision approach to track misinformation in social media. In this paper, we briefly discuss our ongoing work results. Thereafter, we propose a research plan to understand possible mechanisms of misinformation's effect on people and possible impacts of these misinformation on public health.","PeriodicalId":245682,"journal":{"name":"Proceedings of the 2017 International Conference on Digital Health","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114625295","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}
In addition, the retirement from the working life, and the consequent reduction of physical and social activity, contribute to the increased incidence of falls in older adults. Moreover, elderly people suffer different kinds of cognitive decline, such as dementia or attention problems, which also accentuate gait disorders. Assistive technologies (AT) play a key role in today's society, especially when it comes to the older adults. They aim to maintain or improve individual's functioning and independence and to enhance overall well-being. ATs have enabled improvements in their Quality of Life, extending their autonomy and community living and allowing them to stay active in a safe and independent way. During the last decade, research has focused on developing ATs with sensor systems integrated in the device or located in the human body. Efforts are focused especially on mobility assistance for different targets of people and activity recognition, which could be used, for instance, to monitor elderly population while performing activities of daily living.
{"title":"Learning Human Interaction using a Smart Rollator, the i-Walker","authors":"Àtia Cortés","doi":"10.1145/3079452.3079482","DOIUrl":"https://doi.org/10.1145/3079452.3079482","url":null,"abstract":"In addition, the retirement from the working life, and the consequent reduction of physical and social activity, contribute to the increased incidence of falls in older adults. Moreover, elderly people suffer different kinds of cognitive decline, such as dementia or attention problems, which also accentuate gait disorders. Assistive technologies (AT) play a key role in today's society, especially when it comes to the older adults. They aim to maintain or improve individual's functioning and independence and to enhance overall well-being. ATs have enabled improvements in their Quality of Life, extending their autonomy and community living and allowing them to stay active in a safe and independent way. During the last decade, research has focused on developing ATs with sensor systems integrated in the device or located in the human body. Efforts are focused especially on mobility assistance for different targets of people and activity recognition, which could be used, for instance, to monitor elderly population while performing activities of daily living.","PeriodicalId":245682,"journal":{"name":"Proceedings of the 2017 International Conference on Digital Health","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126427506","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}
J. Albinati, Wagner Meira Jr, G. Pappa, Mauro M. Teixeira, Cecilia A. Marques-Toledo
Epidemiological early warning systems for dengue fever rely on up-to-date epidemiological data to forecast future incidence. However, epidemiological data typically requires time to be available, due to the application of time-consuming laboratorial tests. This implies that epidemiological models need to issue predictions with larger antecedence, making their task even more difficult. On the other hand, online platforms, such as Twitter or Google, allow us to obtain samples of users' interaction in near real-time and can be used as sensors to monitor current incidence. In this work, we propose a framework to exploit online data sources to mitigate the lack of up-to-date epidemiological data by obtaining estimates of current incidence, which are then explored by traditional epidemiological models. We show that the proposed framework obtains more accurate predictions than alternative approaches, with statistically better results for delays greater or equal to 4 weeks.
{"title":"Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data","authors":"J. Albinati, Wagner Meira Jr, G. Pappa, Mauro M. Teixeira, Cecilia A. Marques-Toledo","doi":"10.1145/3079452.3079464","DOIUrl":"https://doi.org/10.1145/3079452.3079464","url":null,"abstract":"Epidemiological early warning systems for dengue fever rely on up-to-date epidemiological data to forecast future incidence. However, epidemiological data typically requires time to be available, due to the application of time-consuming laboratorial tests. This implies that epidemiological models need to issue predictions with larger antecedence, making their task even more difficult. On the other hand, online platforms, such as Twitter or Google, allow us to obtain samples of users' interaction in near real-time and can be used as sensors to monitor current incidence. In this work, we propose a framework to exploit online data sources to mitigate the lack of up-to-date epidemiological data by obtaining estimates of current incidence, which are then explored by traditional epidemiological models. We show that the proposed framework obtains more accurate predictions than alternative approaches, with statistically better results for delays greater or equal to 4 weeks.","PeriodicalId":245682,"journal":{"name":"Proceedings of the 2017 International Conference on Digital Health","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116731607","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}
{"title":"Proceedings of the 2017 International Conference on Digital Health","authors":"","doi":"10.1145/3079452","DOIUrl":"https://doi.org/10.1145/3079452","url":null,"abstract":"","PeriodicalId":245682,"journal":{"name":"Proceedings of the 2017 International Conference on Digital Health","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114353315","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}
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