Informatics for Precision Medicine and Healthcare.

The past decade has witnessed great advances in biomedical informatics. Biomedical informatics is an emerging field of healthcare that aims to translate the laboratory observation into clinical practice. Smart healthcare has also developed rapidly with ubiquitous sensor and communication technologies. It is able to capture the online patient-centric phenotypic variables, thus providing a rich information base for translational biomedical informatics. Biomedical informatics and smart healthcare represent two interrelated disciplines. On one hand, biomedical informatics translates the bench discoveries into bedside, and, on the other hand, it is reciprocally informed by clinical data generated from smart healthcare. In this chapter, we will introduce the major strategies and challenges in the application of biomedical informatics technology in precision medicine and healthcare. We highlight how the informatics technology will promote the precision medicine and therefore promise the improvement of healthcare.

[1]  F. Rutten,et al.  Compliance with non-pharmacological recommendations and outcome in heart failure patients. , 2010, European heart journal.

[2]  N. Shublaq,et al.  INBIOMEDvision Promoting and Monitoring Biomedical Informatics in Europe Bio-, Medical-and Neuroinformatics Supporting Neurosciences , 2011 .

[3]  Michael M. Maher,et al.  Emergency CT brain: preliminary interpretation with a tablet device: image quality and diagnostic performance of the Apple iPad , 2012, Emergency Radiology.

[4]  Ying Zhu,et al.  Automatic detection of anomalies in blood glucose using a machine learning approach , 2010, Journal of Communications and Networks.

[5]  H. Mewes,et al.  Informatics and Medicine , 2008, Methods of Information in Medicine.

[6]  A Moreau-Gaudry,et al.  Sensor, signal, and imaging informatics: big data and smart health technologies. , 2014, Yearbook of medical informatics.

[7]  Erik J. Nilsson,et al.  Inviting the Public: The Impact on Informatics Arising from Emerging Global Health Research Paradigms , 2015, Pacific Symposium on Biocomputing.

[8]  Melissa A. Basford,et al.  The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future , 2013, Genetics in Medicine.

[9]  Demetrius J Porche,et al.  Precision Medicine Initiative , 2015, American journal of men's health.

[10]  Dimitrios I. Fotiadis,et al.  Extraction and Analysis of features acquired by wearable sensors network , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[11]  Douglas MacFadden,et al.  Application of Information Technology The Shared Health Research Information Network ( SHRINE ) : A Prototype Federated Query Tool for Clinical Data Repositories , 2014 .

[12]  J. Kvedar,et al.  Connected health: a new framework for evaluation of communication technology use in care improvement strategies for type 2 diabetes. , 2007, Current diabetes reviews.

[13]  J. Kvedar,et al.  Connected health as a lever for healthcare reform: dialogue with featured speakers from the 5th Annual Connected Health Symposium. , 2009, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[14]  Amy Loutfi,et al.  Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges , 2013, Sensors.

[15]  Cem Ersoy,et al.  Wireless sensor networks for healthcare: A survey , 2010, Comput. Networks.

[16]  Kamal Jethwani,et al.  The Impact of Using Mobile-Enabled Devices on Patient Engagement in Remote Monitoring Programs , 2013, Journal of diabetes science and technology.

[17]  T Laakko,et al.  Mobile Health and Wellness Application Framework , 2008, Methods of Information in Medicine.

[18]  Isaac S. Kohane,et al.  Integration of Clinical and Genetic Data in the i2b2 Architecture , 2006, AMIA.

[19]  Sheng Zhong,et al.  Body sensor network security: an identity-based cryptography approach , 2008, WiSec '08.

[20]  James Brusey,et al.  Leveraging Knowledge From Physiological Data: On-Body Heat Stress Risk Prediction With Sensor Networks , 2013, IEEE Transactions on Biomedical Circuits and Systems.

[21]  H. Willard,et al.  Genomic and personalized medicine: foundations and applications. , 2009, Translational research : the journal of laboratory and clinical medicine.

[22]  Hilde Eide,et al.  The development and feasibility of a web-based intervention with diaries and situational feedback via smartphone to support self-management in patients with diabetes type 2. , 2012, Diabetes research and clinical practice.

[23]  D Kalra,et al.  Electronic health records: new opportunities for clinical research , 2013, Journal of internal medicine.

[24]  J. Brownstein,et al.  Early detection of disease outbreaks using the Internet , 2009, Canadian Medical Association Journal.

[25]  Hamid Sharif,et al.  Secure Stochastic ECG Signals Based on Gaussian Mixture Model for $e$-Healthcare Systems , 2011, IEEE Systems Journal.

[26]  Catherine Arnott-Smith,et al.  PatientsLikeMe: Consumer Health Vocabulary as a Folksonomy , 2008, AMIA.

[27]  Sun-Yuan Kung,et al.  Low-energy Formulations of Support Vector Machine Kernel Functions for Biomedical Sensor Applications , 2012, Journal of Signal Processing Systems.

[28]  O Ratib,et al.  Imaging Informatics: from Image Management to Image Navigation , 2009, Yearbook of Medical Informatics.

[29]  Sweta Sneha,et al.  Enabling ubiquitous patient monitoring: Model, decision protocols, opportunities and challenges , 2009, Decis. Support Syst..

[30]  W R Hersh,et al.  Telemedicine for the Medicare population: pediatric, obstetric, and clinician-indirect home interventions. , 2001, Evidence report/technology assessment.

[31]  L. Trinquart,et al.  Computerized advice on drug dosage to improve prescribing practice. , 2013, The Cochrane database of systematic reviews.

[32]  Winston Haynes,et al.  Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile , 2013, Metabolites.

[33]  L. Hood,et al.  Predictive, personalized, preventive, participatory (P4) cancer medicine , 2011, Nature Reviews Clinical Oncology.

[34]  I. Sarkar Biomedical informatics and translational medicine , 2010, Journal of Translational Medicine.

[35]  Heather J Ross,et al.  Mobile Phone-Based Telemonitoring for Heart Failure Management: A Randomized Controlled Trial , 2012, Journal of medical Internet research.

[36]  Rita Kukafka,et al.  Public Health Informatics: The Nature of the Field and Its Relevance to Health Promotion Practice , 2005, Health promotion practice.

[37]  Anazida Zainal,et al.  Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues , 2013, Sensors.

[38]  V. Vimarlund,et al.  Big Data, Smart Homes and Ambient Assisted Living , 2014, Yearbook of Medical Informatics.

[39]  Yuan-Ting Zhang,et al.  [M-health: trends in wearable medical devices]. , 2006, Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation.

[40]  Stephen Gardner,et al.  Implementation of Elliptic-Curve Cryptography on Mobile Healthcare Devices , 2007, 2007 IEEE International Conference on Networking, Sensing and Control.

[41]  Anne Holbrook,et al.  Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials , 2009, BMC Medical Informatics Decis. Mak..

[42]  Gregorio López,et al.  A Review on Architectures and Communications Technologies for Wearable Health-Monitoring Systems , 2012, Sensors.

[43]  J. Frost,et al.  Sharing Health Data for Better Outcomes on PatientsLikeMe , 2010, Journal of medical Internet research.

[44]  Illhoi Yoo,et al.  Data Mining in Healthcare and Biomedicine: A Survey of the Literature , 2012, Journal of Medical Systems.

[45]  D. Whellan,et al.  Conceptual model for heart failure disease management. , 2014, The Canadian journal of cardiology.

[46]  Cornelia M. Ruland,et al.  Effects of an Internet Support System to Assist Cancer Patients in Reducing Symptom Distress: A Randomized Controlled Trial , 2013, Cancer nursing.

[47]  Carmen C. Y. Poon,et al.  A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health , 2006, IEEE Communications Magazine.

[48]  Teeradache Viangteeravat,et al.  Biomedical Informatics Unit (BMIU): Slim‐Prim System Bridges the Gap Between Laboratory Discovery and Practice , 2009, Clinical and translational science.

[49]  Lawrence Hunter,et al.  Current methodologies for translational bioinformatics , 2010, J. Biomed. Informatics.

[50]  Cornelia M. Ruland,et al.  Evaluation of different features of an eHealth application for personalized illness management support: Cancer patients' use and appraisal of usefulness , 2013, Int. J. Medical Informatics.

[51]  J. Frost,et al.  Social Uses of Personal Health Information Within PatientsLikeMe, an Online Patient Community: What Can Happen When Patients Have Access to One Another’s Data , 2008, Journal of medical Internet research.

[52]  Dimitrios N. Serpanos,et al.  Identifying Chronic Disease Complications Utilizing State of the Art Data Fusion Methodologies and Signal Processing Algorithms , 2011, MobiHealth.

[53]  Yuanhua Liu,et al.  Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties , 2013, BMC Systems Biology.

[54]  Sanjeev Gupta,et al.  Patient 2.0 Empowerment , 2008, SWWS.

[55]  Jeong Hun Lee,et al.  An Assessment of the iPad 2 as a CT Teleradiology Tool Using Brain CT with Subtle Intracranial Hemorrhage Under Conventional Illumination , 2013, Journal of Digital Imaging.

[56]  Alvis Brazma,et al.  Minimum Information About a Microarray Experiment (MIAME) – Successes, Failures, Challenges , 2009, TheScientificWorldJournal.

[57]  Peter M. Kuzmak,et al.  The VA's use of DICOM to integrate image data seamlessly into the online patient record , 1999, AMIA.

[58]  Robyn Tamblyn,et al.  Review Paper: The Impact of Electronic Health Records on Time Efficiency of Physicians and Nurses: A Systematic Review , 2005, J. Am. Medical Informatics Assoc..

[59]  David R. Crosslin,et al.  Copy number variation analysis in the context of electronic medical records and large-scale genomics consortium efforts , 2014, Front. Genet..

[60]  David Haussler,et al.  Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM , 2010, Bioinform..

[61]  C. Sander,et al.  Integrative Subtype Discovery in Glioblastoma Using iCluster , 2012, PloS one.

[62]  Andrew Thornburg,et al.  Enhancing Medical Device Training with Hybrid Physical-Virtual Simulators: Smart Peripherals for Virtual Devices , 2013, MMVR.

[63]  S. Terry Obama's Precision Medicine Initiative. , 2015, Genetic testing and molecular biomarkers.

[64]  B. Kirkwood,et al.  Mobile Health (mHealth) Approaches and Lessons for Increased Performance and Retention of Community Health Workers in Low- and Middle-Income Countries: A Review , 2013, Journal of medical Internet research.

[65]  G. Glass,et al.  Mobile phones improve case detection and management of malaria in rural Bangladesh , 2013, Malaria Journal.

[66]  S. Sharma,et al.  A strategic approach to m-health , 2009, Health Informatics J..

[67]  张元亭,et al.  Investigation on Cardiovascular Risk Prediction Using Physiological Parameters , 2013 .

[68]  Stefan D Anker,et al.  Telemedicine and remote management of patients with heart failure , 2011, The Lancet.

[69]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[70]  Bairong Shen,et al.  Translational Biomedical Informatics in the Cloud: Present and Future , 2013, BioMed research international.

[71]  Marylyn D. Ritchie,et al.  Electronic medical records and genomics (eMERGE) network exploration in cataract: Several new potential susceptibility loci , 2014, Molecular vision.

[72]  Hadi Kharrazi,et al.  Mobile personal health records: An evaluation of features and functionality , 2012, Int. J. Medical Informatics.