1. Project Proposal Presentation on M edicine prescription system based on big data analysis By Ashish Kumar Chakraverti

2. Contents Introduction Objective Current state of literature related to proposed topic Proposed Research work Methodology References By Ashish Kumar Chakraverti

3. Introduction  Initially medicine was being driven by external symptoms basis and some sort of prediction  This new era of medicine is being driven by an explosion in health related data  Now days sequencing human genomes is getting faster and cheaper, the days of truly personalized health care are drawing closer  Analyzing the mountain of health related data helps us discover correlations that were not so obvious before so we can begin refining the way we practice medicine and identify those who are statistically more likely to be a victim of a particular disease. By Ashish Kumar Chakraverti

4. Introduction Contd……  It is being percept that the treatment of disease is most effective when the definition of both the disease and the treatment is individualized at the patient level  Personalized medicine and big data hold the promise to transform oncology practice more and more rapidly, than any thing that has happened in past. By Ashish Kumar Chakraverti

5. Objective  Data is growing and moving faster than healthcare organizations can consume it.  80% of medical data is unstructured and is clinically relevant.  My objective is to design and model a system which can give efficient, predictive and personalized medicine prescription. By Ashish Kumar Chakraverti

6. Current state of literature related to proposed topic  The European Association for Predictive, Preventive and Personalized Medicine(EPMA) launches a new program ‘Horizon 2020’ to provide the long lasting instruments for scientific and technological progress in medical services.  Horizon 2020 provide unlimited room for research and implementation in predictive preventive and personalized medicine. By Ashish Kumar Chakraverti

7. Current state of literature related to proposed topic Contd……..  Big Data analytics in Health care: Us Congress in August 2012 defines big data as “ large volume of high velocity, complex, and variable data that require advance technique and technologies to enable the capture, storage, distribution, management and analysis of the information”  Big data encompasses such characteristics with respect specifically to health care.  Existing analytical techniques can be applied to the vast amount of existing(but currently unanalyzed) patient-related health and medical data to reach a deeper understanding of outcomes, which then can be applied at point of care. By Ashish Kumar Chakraverti

8. Current state of literature related to proposed topic Contd……..  Research and Development 1)Predictive Modeling 2)Statistical Tools and Algorithms 3)Analysis of Clinical trials 4)Analyzing disease patterns 5)Faster development of more accurately targeted vaccines 6)Turning large amount of data in to actionable information 7)Genomic analysis 8)Fraud analysis By Ashish Kumar Chakraverti

9. Current state of literature related to proposed topic Contd……..  Source of Big data and information 1. Web and social media data: Clickstream and interaction data from Facebook, Twitter, LinkedIn, blogs, and the like. It can also include health plan websites, smartphone apps, etc. 2. Machine to machine data: readings from remote sensors, meters, and other vital sign devices. 3. Big transaction data: health care claims and other billing records increasingly available in semi-structured and unstructured formats. By Ashish Kumar Chakraverti

10. Current state of literature related to proposed topic Contd…….. 4. Biometric data: finger prints, genetics, handwriting, retinal scans, x-ray and other medical images, blood pressure, pulse and pulse-oximetry readings, and other similar types of data. 5. Human-generated data: unstructured and semi- structured data such as EMRs, physicians notes, email, and paper documents. By Ashish Kumar Chakraverti

11. Proposed research Work  Here I propose a well structured and efficient system for predictive and personalized medicine prescription system based on big data analysis  The conceptual frame work of proposed research work is given in next slide By Ashish Kumar Chakraverti

12. Proposed research Work Contd….. Fig 1: Big data Analysis based System for predictive and personalized Medicine By Ashish Kumar Chakraverti

13. Proposed research Work Contd…… Research will be carried out in following phases  Collection of data: In this phase the relevant data will be collected from possible recourses like hospitals, doctors, patient etc.  Repository of data: Some data may be found in term of EMD (Electronic Medical data) but mostly will be in terms on written notes, reports and images. All these data will be unstructured so to save the time it will be stored as it is in unstructured but in relevant manner By Ashish Kumar Chakraverti

14. Proposed research Work Contd……  Design and modeling of BASYM: Now the actual process will start in which mapping of the data will be done with some advanced mapping technique (eg Map reduce technique). This system will be design for two different purposes but from outside it will look integrated. These two system will be as follows: By Ashish Kumar Chakraverti

15. Proposed research Work Contd…… 1. BASYM-pd:- BASYM for prediction. 2. BASYM-pr:- BASYM for prescription. By Ashish Kumar Chakraverti

16. Methodology  Propositions: Design of Implications  Variable selection: Selection of Inputs and Generation of Outputs  Data collection: Different relevant data collection  Data transformation: Finding and processing the relevant information from collected data  Platform/tool selection: Hadoop and Linux  Conceptual model: Modeling of process and real time simulation  Analytic techniques: -Association, clustering, classification  Results & insight: Results and Conclusions By Ashish Kumar Chakraverti

17. References [1]. Raghupathi and Raghupathi Health Information Science and Systems 2014, 2:3 Page 2 of 10 http://www.hissjournal.com/content/2/1/3 [2]. Raghupathi W: Data Mining in Health Care. In Healthcare Informatics: Improving Efficiency and Productivity. Edited by Kudyba S. Taylor & Francis; 2010:211–223. [3]. Biomedical Data: Their Acquisition, Storage, and Use EDWARD H. SHORTLIFFE AND G. OCTO BARNETT [4]. Advances in Predictive, Preventive and Personalised Medicine. [http://www.springer.com/series/10051] [5]. HORIZON 2020 - WORK PROGRAMME 2014 – 2015. [http://ec.europa.eu/ research/participants/data/ref/h2020/wp/2014_2015/main/h2020-wp1415-health_en.pdf] By Ashish Kumar Chakraverti

18. References Contd. [6]. Research & Innovation - Horizon 2020 Funding. [https://ec.europa.eu/ research/participants/portal/desktop/en/home.html] [7]. Knowledgent: Big Data and Healthcare Payers; 2013. http://knowledgent. com/mediapage/insights/whitepaper/482.http://knowledgent [8]. Guest Editorial Computational Solutions to Large-Scale Data Management and Analysis in Translational and Personalized Medicine. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 18, NO. 3, MAY 2014 [9]. Halim, A-B. Proficiency Testing for Monitoring Global Laboratory Performance and Identifying Discordance. Winter 2013 Volume 44, Number 1 Lab Medicine e19. [10]. Vanacek, J. (2012) How cloud and big data are impacting the human genome: touching 7 billion lives. Forbes 16 April By Ashish Kumar Chakraverti

19. ? Thank you By Ashish Kumar Chakraverti