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IBM J. of Research and Development     Hybrid Journal   (Followers: 18, SJR: 0.636, h-index: 82)
Journal Cover IBM Journal of Research and Development
  [SJR: 0.636]   [H-I: 82]   [18 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0018-8646
   Published by IBM Homepage  [1 journal]
  • Preface: User-generated health data and applications
    • Authors: Ching-Hua Chen;Kenney Ng;
      Pages: 1 - 3
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
  • A homomorphic encryption-based system for securely managing personal
           health metrics data
    • Authors: R. Bocu;C. Costache;
      Pages: 1:1 - 1:10
      Abstract: Hardware and software solutions for the collection of personal health information continue to evolve. The reliable gathering of personal health information, previously usually possible only in dedicated medical settings, has recently become possible through wearable specialized medical devices. Among other drawbacks, these devices usually do not store the data locally and offer, at best, limited basic data processing features and few advanced processing capabilities for the collected personal health data. In this paper, we describe an integrated personal health information system that allows secure storage and processing of medical data in the cloud by using a comprehensive homomorphic encryption model to preserve data privacy. The system collects the user data through a client application module, typically installed on the user's smartphone or smartwatch, and securely transports the data to the cloud backend powered by IBM Bluemix. The data are stored by the IBM Cloudant infrastructure, while the homomorphic processing of the encrypted data is performed using the Apache Spark service, which is also made available by the IBM Bluemix platform. The event-based handlers are triggered by the IBM OpenWhisk programming service. The initial prototype has been tested using a real-world use case, which is described.
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
  • Curating and integrating user-generated health data from multiple sources
           to support healthcare analytics
    • Authors: C. Kakkanatt;M. Benigno;V. M. Jackson;P. L. Huang;K. Ng;
      Pages: 2:1 - 2:7
      Abstract: As the volume and variety of healthcare-related data continue to grow, the analysis and use of this data will increasingly depend on the ability to appropriately collect, curate, and integrate disparate data from many different sources including user-generated health data. We describe our approach to, and highlight our experiences with, the development of a robust data curation process that supports healthcare analytics. The process consists of the following steps: collection, understanding, validation, cleaning, integration, enrichment, and storage. It has been successfully applied to the processing of a variety of data types including clinical data from electronic health records and observational studies, genomic data, microbiome data, self-reported data from surveys, and self-tracked data from wearables from more than 600 subjects. The curated data have been used to support a number of healthcare analytic applications, including descriptive analytics, data visualization, patient stratification, and predictive modeling.
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
  • Data quality challenges for person-generated health and wellness data
    • Authors: J. Codella;C. Partovian;H.-Y. Chang;C.-H. Chen;
      Pages: 3:1 - 3:8
      Abstract: Person-generated health data (PGHD) generated by wearable devices and smartphone applications are growing rapidly. There is increasing effort to employ advanced analytical methods to generate insights from these data in order to help people change their lifestyle and improve their health. PGHD—such as step counts, exercise logs, nutritional diaries, and sleep records—are often incomplete, inaccurate, and collected over too short a duration. Insufficient user engagement with wearable and mobile technologies, as well as lack of sensor validation, standardization of data collection, transparency of data processing assumptions, and accessibility to relevant data from consumer-grade sensors, also negatively affects data quality. The literature on data quality for PGHD is sparse and fragmented, providing little guidance to data analysts on how to assess and prioritize data quality concerns. In this paper, we summarize our experiences as data analysts working with PGHD, outline some of the challenges in using PGHD for insight generation, and discuss some established methods for addressing these challenges. We review the literature on PGHD data quality, identify the major stakeholders in the PGHD ecosystem, and apply an established data quality framework to present the most relevant data quality challenges for each stakeholder.
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
  • An interpretable health behavioral intervention policy for mobile device
    • Authors: X. Hu;P.-Y. S. Hsueh;C.-H. Chen;K. M. Diaz;F. E. Parsons;I. Ensari;M. Qian;Y.-K. K. Cheung;
      Pages: 4:1 - 4:6
      Abstract: An increasing number of people use mobile devices to monitor their behavior, such as exercise, and record their health status, such as psychological stress. However, these devices rarely provide ongoing support to help users understand how their behavior contributes to changes in their health status. To address this challenge, we aim to develop an interpretable policy for physical activity recommendations that reduce a user's perceived psychological stress, over a given time horizon. We formulate this problem as a sequential decision-making problem and solve it using a new method that we refer to as threshold Q-learning (TQL). The advantage of the TQL method over traditional Q-learning is that it is “doubly robust” and interpretable. This interpretability is achieved by making model assumptions and incorporating threshold selection into the learning process. Our simulation results indicate that the TQL method performs better than the Q-learning method given model misspecification. Our analyses are performed on data collected from 79 healthy adults over a 7 week period, where the data comprise physical activity patterns collected from mobile devices and self-assessed stress levels of the users. This work serves as a first step toward a computational health coaching solution for mobile device users.
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
  • Decomposition of complex movements into primitives for Parkinson's disease
    • Authors: E. K. Pissadaki;A. G. S. Abrami;S. J. Heisig;E. Bilal;M. Cavallo;P. W. Wacnik;K. Erb;D. R. Karlin;P. R. Bergethon;S. P. Amato;H. Zhang;V. L. Ramos;F. Hameed;J. J. Rice;
      Pages: 5:1 - 5:11
      Abstract: Recent advances in technology present an important opportunity in medicine to augment episodic, expert-based observations of patients’ disease signs, obtained in the clinic, with continuous and sensitive measures using wearable and ambient sensors. In Parkinson's disease (PD), such technology-based objective measures have shown exciting potential for passively monitoring disease signs, their fluctuation, and their progression. We are developing a system to passively and continuously capture data from people with PD in their daily lives, and provide a real-time estimate of their motor functions, that is analogous to scores obtained during Part III of the human-administered Movement Disorder Society's Unified Parkinson's Disease assessment (MDS-UPDRS3). Our hypothesis is that complex human movements can be decomposed into movement primitives related to the performance of the MDS-UPDRS3 motor assessment. Toward this hypothesis, we developed a system for integrating and analyzing multiple streams of sensor data collected from volunteers executing the tasks based on the MDS-UPDRS3. In this paper, we show how we can leverage the data collected from MDS-UPDRS3 tasks to develop machine learning models that can identify movement primitives in activities of daily living.
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
  • Evaluating speech-based question–answer interactions for elder-care
    • Authors: H. Takagi;M. Ohno;M. Kobayashi;T. Nakada;
      Pages: 6:1 - 6:10
      Abstract: Japan has the highest percentage of population deemed elderly in the world (i.e., people over the age of 65). The portion is predicted to reach 30% by 2025. An aging society poses various societal challenges including societal isolation. Mobile devices, such as smartphones and tablets, can play an important role in connecting elderly persons with family members and can provide cost-effective daily services and opportunities to join community activities. However, lack of experience with mobile devices often prevents elderly persons from adopting such services. It is thus necessary to assist the elderly by giving them answers to questions they may have about their devices and services, to enable large-scale deployment of mobile devices to the aging population. In this study, we evaluated a speech-based question-and-answer system that we designed for elderly novice users of mobile devices through a pilot study on an eldercare service platform with 1,011 elderly participants who were encouraged to use health-check services, video telephony, and other services on a tablet device. The participants could ask questions about the services, the application, and device by using speech whenever they wanted. The results suggest the feasibility of using speech-based interfaces as the main interaction medium for the elderly.
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
  • Detection of suicide-related posts in Twitter data streams
    • Authors: M. Johnson Vioulès;B. Moulahi;J. Azé;S. Bringay;
      Pages: 7:1 - 7:12
      Abstract: Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
  • Building a cognitive platform for the managed IT services lifecycle
    • Authors: K. Kloeckner;C. M. Adam;N. Anerousis;N. Ayachitula;M. F. Bulut;G. Dasgupta;Y. Deng;Y. Diao;N. Fuller;S. Gopisetty;M. Hernandez;J. Hwang;P. Iannucci;A. K. Kalia;G. Lanfranchi;D. Lanyi;H. Ludwig;A. Mahamuni;R. Mahindru;F. J. Meng;H. R. Motahari Nezhad;K. Murthy;T. Nakamura;A. Paradkar;D. Perpetua;B. Pfitzmann;D. Rosu;L. Shwartz;Z. Su;M. Surendra;S. Tao;H. Völzer;M. Vukovic;D. Wiesmann;S. Wozniak;G. Wright;J. Xiao;S. Zeng;
      Pages: 8:1 - 8:11
      Abstract: In this paper, we present IBM's cognitive strategy for service delivery transformation to a services integration approach for IT service management. This transformation is fueled by three fundamental technological paradigms: cognitive, cloud, and data and digital content. At the foundation of this approach is the IBM Services Platform with Watson—an IBM Cloud and Watson-based platform that uses machine learning, natural language, discovery, and various Watson application programming interfaces to design superior client solutions, achieve an exceptional level of autonomic service management, and facilitate a healthy “always-on” environment. It continuously learns and optimizes information technology performance to enable enhanced client business outcomes.
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
  • Reengineering a server ecosystem for enhanced portability and performance
    • Authors: M. Gschwind;U. Weigand;
      Pages: 9:1 - 9:13
      Abstract: We describe the design and implementation of the new, little-endian Linux on Power operating environment, with a particular emphasis on its application interfaces. The new environment was designed to simplify porting of applications from other processor architectures to Power. It uses little-endian data formats to reduce the need for code adaptation and debugging during porting, and to simplify the use of commodity off-the-shelf system components, such as I/O adapters and accelerators. We took advantage of introducing a new operating environment to optimize the platform application binary interface (ABI) for new programming patterns and paradigms, resulting in enhanced out-of-the-box performance. The new ABI responds to increasing componentization of applications by reducing the overhead involved in function calls with an emphasis on accelerating calls to short functions as well as calls to runtime-resolved functions, i.e., functions in shared libraries, function pointers, virtual methods, and interface functions.
      PubDate: Jan.-Feb. 1 2018
      Issue No: Vol. 62, No. 1 (2018)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
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