2017 IEEE International Conference on Healthcare Informatics (ICHI)最新文献

{"title":"Multivariate Hidden Markov Models for Personal Smartphone Sensor Data: Time Series Analysis","authors":"William van der Kamp, N. Osgood","doi":"10.1109/ICHI.2017.84","DOIUrl":"https://doi.org/10.1109/ICHI.2017.84","url":null,"abstract":"Smartphone-based human activity recognition (HAR) offers growing value for health research. We applied offline Hidden Markov Models (HMMs) to multivariate smartphone sensor data, classifying individual behaviour into a time series of states. We used supervised HMMs, validated using ground-truth data from a small self-report study. The HMMs achieved reasonable accuracy in classifying phone off-person vs. phone on-person, off-vehicle vs. on-vehicle, and phone off-person vs. sitting vs. standing vs. walking, for some participants. Strong evidence suggests that poor accuracy in other cases was caused by participant mislabeling, though HMM shortcomings contributed.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115656898","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":"Making Sense in the Long Run: Long-Term Health Monitoring in Real Lives","authors":"Jochen Meyer, Elke Beck, Merlin Wasmann, Susanne CJ Boll","doi":"10.1109/ICHI.2017.11","DOIUrl":"https://doi.org/10.1109/ICHI.2017.11","url":null,"abstract":"Long term self monitoring with connected personal health devices offers tremendous opportunities for wellbeing, health, and prevention. However, to date it is not fully understood how users perceive monitoring in the long term and how they implement it in their daily lives. We observed 7 participants that used a comprehensive set of connected personal health devices for 9 months and inquired their opinions and experiences. Users varied broadly in how they used the devices and how they engaged with the collected data. Implementation and use of long-monitoring evolved over time, leading to subtle but distinct differences to short term use. We found five relevant use cases: behavior support, improved self understanding, identification of trends and relations, decision making, and data collection for future use.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123021674","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":"Personal Health Assistance for Elderly People via Smartwatch Based Motion Analysis","authors":"Rainer Lutze, K. Waldhör","doi":"10.1109/ICHI.2017.79","DOIUrl":"https://doi.org/10.1109/ICHI.2017.79","url":null,"abstract":"A new approach is presented for a personal health assistant for elderly people utilizing smartwatches. On the smartwatch, an app featuring an artificial neuronal net (ANN) analyzes the motion patterns of the smartwatch wearer. The ANN recognizes health relevant events and activities of daily living (EDLs, ADL). The system architecture of the app, the data acquisition process, the selection and design of suitable data models and the advantages of ANNs versus other recognition engines are elaborated. The characteristics of the recognized ADLs will be utilized for continuously calculating the wellbeing of the smartwatch wearer, safeguarding a self-determined living in the familiar home up to the very old age.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127214347","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":"Single Sensor Techniques for Sleep Apnea Diagnosis Using Deep Learning","authors":"Rahul Krishnan Pathinarupothi, J. DharaPrathap, E. Rangan, E. Gopalakrishnan, R. Vinaykumar, P. SomanK.","doi":"10.1109/ICHI.2017.37","DOIUrl":"https://doi.org/10.1109/ICHI.2017.37","url":null,"abstract":"A large number of obstructive sleep apnea (OSA) cases are under-diagnosed due unavailability, inconvenience or expense of sleep labs. Hence, an automated detection by applying computational techniques to multivariate signals has already become a well-researched subject. However, the best-known techniques that use various features have not achieved the gold standard of polysomnography (PSG) tests. In this paper, we substantiate the medical conjecture that OSA directly impacts body parameters such as Instantaneous Heart Rate (IHR) and blood oxygen saturation (SpO2). We then use a deep learning technique called LSTM-RNN (long short-term memory recurrent neural networks) to experimentally prove that OSA severity detection can be solely based on either IHR or SpO2 signals, which can be easily, obtained using off-the-shelf non-intrusive wearable single sensors. The results obtained from LSTM-RNN model shows an area under curve (AUC) of 0.98 associated with very high accuracy on a dataset of more than 16,000 apnea non-apnea minutes. These results have encouraged our collaborating doctors to further come up with a diagnostic protocol that is based on LSTM-RNN, SpO2, and IHR, thereby increasing the chances of larger adoption among medical community.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124072269","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":"Pattern Recognition for Automated Healthcare Assessment Using Non-invasive, Ambient Sensors","authors":"Dino Nienhold, Rolf Dornberger, S. Korkut","doi":"10.1109/ICHI.2017.71","DOIUrl":"https://doi.org/10.1109/ICHI.2017.71","url":null,"abstract":"In this paper, a solution for an automated healthcare assessment process is proposed. Non-invasive, ambient sensors are retrieving data from patients being in their home care treatment setups. The type of sensors is limited to the tracking of inertia, motion, and alcohol gas. Low-cost sensor prototypes are developed. They constantly measure the movement and the air around the patients. The Big Data generated in this way is used to retrieve patterns of activities. Different pattern recognition algorithms are tested and compared. The highest accuracy and reliability in assessing the data are support vector machines and feedforward neural networks with a performance of 90 % probability in identifying the correct patients’ activities over the test period. In this paper, the setup of the sensor prototypes, the data handling, and the data analytics are discussed.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124423777","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":"Position Article on Integrating Data and Model to Understand Disease Interactions","authors":"M. Nabi, A. Arvay, M. Klenk, Gaurang Gavai, D. Bobrow, J. Kleer","doi":"10.1109/ICHI.2017.100","DOIUrl":"https://doi.org/10.1109/ICHI.2017.100","url":null,"abstract":"Comorbidities - cases in which patients have two or more chronic conditions - impose burden on the health care system as well as society. Causal relationships and interaction among different diseases in the comorbidity set is complex, and not yet completely understood by the medical community. Understanding the causality between diseases is an essential element of science of medicine. Patient treatment would also be more efficient if better knowledge of causality was available. There are different approaches to shed more lights on causality in medicine. In this article, we propose two approaches. One is using statistical causal inference algorithms on electronic medical data to identify potential causal relationships among diseases. In the second approach, we use qualitative modeling techniques to build models of disease mechanisms. Each one of these directions has its own pitfalls. The assumption is integrating the two approaches will minimize the drawbacks of each. The integration involves using qualitative models of underlying disease mechanisms to evaluate and explain the potential causal relationships resulted from the causal inference algorithms. This integration is complex, and require big effort from the community. In this article, we are proposing new research direction based on our preliminary work.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134094448","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":"A Novel Steady-State Visually Evoked Potential (SSVEP) Based Brain Computer Interface Paradigm for Disabled Individuals","authors":"Divya Geethakumari Anil, Krupal Sureshbai Mistry, V. Palande, K. George","doi":"10.1109/ICHI.2017.19","DOIUrl":"https://doi.org/10.1109/ICHI.2017.19","url":null,"abstract":"This study provides an insight into a novel steady state visually evoked potential (SSVEP) brain computer interface (BCI) approach. In this approach, four groups of light emitting diodes (LEDs) that flicker at different frequencies are used and each of these groups consist of three LEDs connected in series. By providing visual attention to these LEDs, corresponding electroencephalograph (EEG) signals were obtained in the visual cortex area of the brain. Using suitable signal processing algorithms, acquired EEG signals were classified at different frequencies and given as inputs to a brain computer interface system that can control the movement of a wheelchair. This method provides a platform for individuals who are affected by neuromuscular degenerative diseases (NMD) such as Amyotrophic Lateral Sclerosis (ALS), Locked-in Syndrome (LIS) etc, to help them lead an independent life. Two different SSVEP approaches were carried out on four healthy subjects for prototype testing. First approach was based on four groups of LEDs flickering at different frequencies ranging from 7 Hz to 15 Hz and the subjects selectively paid attention to one group of LEDs at a time. The second approach was based on simultaneous flickering of two groups of LEDs at different frequency combinations. Five trials were conducted on four subjects to test the performance of the system. The average accuracy obtained with each of the methods was greater than 70% with an average time of less than 10 seconds to trigger a command for BCI based application. The proposed system can thus provide a visual stimulator based on simple and customizable LED for a cost- effective BCI approach. Also, the efficiency and accuracy of the proposed SSVEP approach was compared to audio steady state response (ASSR) approach, where the subjects concentrated to two tones of beat frequencies at 37 Hz and 43 Hz. The average accuracy obtained with ASSR approach was only 47.5% with an average time of 18.72 seconds. Compared to ASSR, SSVEP approach is 23% more efficient.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134457682","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":"Application of Cognitive Load Theory to the Design and Evaluation of Usability Study of mHealth Applications: Opportunities and Challenges","authors":"Rumei Yang, Wei Wei, M. Cummins","doi":"10.1109/ICHI.2017.77","DOIUrl":"https://doi.org/10.1109/ICHI.2017.77","url":null,"abstract":"There has been little research on using Cognitive load theory (CLT) to guide the design and evaluation mHealth applications. In this presentation, we describe: 1) the historical development of CLT in instructional design, 2) the implications of cognitive load (CL) to principles of designing mHealth applications, and 3) a review of cognitive load measurements in educational studies.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121202040","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":"Determining Associations with Word Embedding in Heterogeneous Network for Detecting Off-Label Drug Uses","authors":"Christopher C. Yang, Mengnan Zhao","doi":"10.1109/ICHI.2017.78","DOIUrl":"https://doi.org/10.1109/ICHI.2017.78","url":null,"abstract":"Off-label drug use is quite common in clinical practice and inevitable to some extent. Such uses might deliver effective treatment and suggest clinical innovation sometimes, however, they have the unknown risk to cause serious outcomes due to lacking scientific support. As gaining information about off-label drug use could present a clue to the stakeholders such as healthcare professionals and medication manufacturers to further the investigation on drug efficacy and safety, it raises the need to develop a systematic way to detect off-label drug uses. Considering the increasing discussions in online health communities (OHCs) among the health consumers, we proposed to harness the large volume of timely information in OHCs to develop an automated method for detecting off-label drug uses from health consumer generated data. From the text corpus, we extracted medical entities (diseases, drugs, and adverse drug reactions) with lexicon-based approaches and measured their interactions with word embedding models, based on which, we constructed a heterogeneous healthcare network. We defined several meta-path-based indicators to describe the drug-disease associations in the heterogeneous network and used them as features to train a binary classifier built on Random Forest algorithm, to recognize the known drug-disease associations. The classification model obtained better results when incorporating word embedding features and achieved the best performance when using both association rule mining features and word embedding features, with F1-score reaching 0.939, based on which, we identified 2,125 possible off-label drug uses and checked their potential by searching evidence in PubMed and FAERS.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128797444","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":"Oro Vision: Deep Learning for Classifying Orofacial Diseases","authors":"Rajaram Anantharaman, Vidya Anantharaman, Yugyung Lee","doi":"10.1109/ICHI.2017.69","DOIUrl":"https://doi.org/10.1109/ICHI.2017.69","url":null,"abstract":"This experiment is an attempt to apply deep learning techniques to orofacial image analysis. Health promotion is recognized as a viable approach to preventing diseases and disorders and promoting changes in health behaviors or practices. Each year, oral cancer kills more people in the US than does cervical cancer, malignant melanoma, or Hodgkin's disease. A first line of defense against oral diseases is an orofacial selfexamination. The goal of this experiment titled \"Oro Vision\" is to provide an assessment tool for field workers to perform initial examinations of orofacial diseases, using a camera enabled mobile phone. For this experiment, we chose to implement Oro Vision to detect mouth sores. The goal is to extend this model to identify several other Oral diseases such as Thrush, Leukoplakia, Lichenplanus, etc. One variety of mouth sore, referred to as the \"cold sore\" is highly contagious and an infected person can easily pass on the infection to another person just through skin to skin contact. \"Oro Vision\" is implemented as an HTML5 mobile responsive web app that can be accessed through any mobile or standard browser. Oro Vision uses deep learning to train a model and subsequently uses this trained model to distinguish a cold sore from a canker sore. In addition, an accurate diagnosis by a trained healthcare professional is required before any kind of treatment is discussed since several other conditions of the mouth including oral cancer may mimic canker sores.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114554803","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}