1 MEDICAL DATA MINING Timothy Hays, PhD Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012
2 2 Healthcare in America Is a VERY Large Domain with Enormous Opportunities for Data Mining US Healthcare (2009) $2.5 Trillion 17.3% of GDP Healthcare system: Providers, Payers and Patients, Government (Federal and State) and Private/Commercial, Research to (Best) Practice, Regulations, Laws, and Policies (i.e. Affordable Care Act, etc.)
3 3 Healthcare / Medical Data Mining Patients and Consumers Providers: Government, Private or Commercial, hospitals, pharmacies, clinics, doctors offices, and other provider services Payers: employers, insurance carriers, other third-party payers, health plan sponsors (employers, unions, DOD, VA, HHS, etc.) Areas where data mining can help!
4 4 Healthcare / Medical Data Mining Healthcare crosscuts: Regulatory: laws, regulations, coding, guidelines, best practices, performance, costs, reporting (i.e., adverse event), etc. Research: basic research, pharmaceuticals, medical devices, genetics, drug-drug interaction, diagnostic test decision support, biomedical research data mining (basic or clinical results), etc. IT systems: interoperability, software development, information/data storage, security and access, reporting, Big Data and small data, usability, data transfer, training/educating/communicating, and so much more! Areas where data mining can help!
5 5 So How Do We Get There? Understanding and Then Tackling the Pieces!
6 6 Medical Data Mining Where are the opportunities?
7 7 Build On History and Knowledge Practice domains / Fields we can learn from: Knowledge management is the theory behind knowledge capture and use Informatics is the science of information, the practice of information processing, and the engineering of information systems Analytics is the practical application of tools (i.e. algorithms) upon information to gain new insights. Others: Business Intelligence Competitive Intelligence Computational Science Bioinformatics Health Informatics Predictive Modeling Decision Support Artificial Intelligence, etc.
8 8 Data Mining Effective Use of Data, Tools, and Analyses Decisions, Plans, Actions Knowledge Discovery and Analysis The application and productive use of information Information Data organized into meaningful patterns Data Unstructured, structured, Multiple sources
9 9 Medical Data Mining Leads to: Question based answers Anomaly based discovery New Knowledge discovery Informed decisions Probability measures Predictive modeling Decision support Improved health Personalized medicine
10 10 So, Again, How Do We Get There? What questions are you trying to answer? Ultimately, identify answers to questions we didn t know we had Do you have data, tools and analyses to answer the question? Example areas: Healthcare management (provider care practices) Fraud and abuse Treatment effectiveness Patient involvement and relationship Understanding and Then Tackling the Pieces!
11 11 Dilbert on Data There are Exabyte s of data! But is it the right data to answer your question?
12 So, Again, How Do We Get There? 12
13 13 Evolution of the Lung Cancer Portfolio: Abstracts
14 14 So, Again, How Do We Get There? Data: the V s (Volume, Velocity, Variety, Veracity, and Visibility) Tools/Applications: Search algorithms, text mining, natural language processing (NLP), machine learning, etc. clustering, predictive modeling, relationship and link analysis, taxonomy generation, statistical analysis, neural networks, visualization, heat maps, etc. Analysis: Methodologies (exploration and drill down) and subject matter/domain experts Understanding and Then Tackling the Pieces!
15 15 The Data V s Data Volume - large, small, combined, separate, etc. Velocity transfer: capture and retrieval includes streaming, batch processing, utilization, etc. Variety - text [structured unstructured], images, audio, video, etc. Veracity - meaningfulness [use], value, variability, quality, etc. Visibility - access, security, Understanding and Then Tackling the Pieces!
16 16 Tools / Applications 1000 s of Tools Integrated or add-on Question/Domain/Analysis specific Use with Repositories and Platforms Helpful but not necessary for analysis Relevant to data veracity/quality Understanding and Then Tackling the Pieces!
17 17 Analysis Methodologies Research Algorithms Proprietary Subject matter experts Domain (healthcare centric) expertise Analysis expertise Tool and application experience Understanding and Then Tackling the Pieces!
18 18 Medical Data Mining Spectrum Complexity Planning/Forecasting Modeling/Predicting Decision Support / Optimizing Dashboards Reporting Capability
19 Medical Data Mining 19 It sounds good, but are there standards for data capture, use, definitions, sharing, etc.? Are standards needed (yet)? Are there sufficient tools, applications, analyses and staff available to identify valuable information to improve healthcare? Are there sufficient benefits and incentives in core areas where data mining is essential? Personalized and predictive medicine Fraud and abuse Research advancements Improved treatments and medical devices
20 Dilbert on Federal & Corporate Realities 20 An integrated approach is key!
21 NIH Research, Condition, and Disease Categorization Project
22 22 National Institutes of Health (NIH): Case Study The purpose of the Research, Condition, and Disease Categorization (RCDC) project is to 1. Consistently categorize NIH-funded research projects according to research areas/categories 2. Use an automated process, and 3. Make the results available to Congress and the public
23 23 How does it do that? NIH: Case Study RCDC system uses Elsevier s Collexis technology to text mine biomedical concepts from research descriptions NIH research experts define a weighted classification system for each of 238 categories All NIH research is then categorized through an automated process The output is reported publicly at Report.NIH.Gov
24 24 NIH: Case Study The Research, Condition, and Disease Categorization (RCDC) project is an example of providing new, timely information out of unstructured and structured data RCDC pulls data from 7 databases (all containing well over 4 terabytes of content) that gets routed, compartmentalized, validated and ultimately used for regular reports and on-demand queries Allows research information to be explored proactively Is adaptable as 1) Science evolves over time and 2) Increased needs for data usage are identified
25 How Does RCDC Work? 25 Category Definition developed by NIH staff Matching Process Projects with matching categories Project Description Matching compares individual project descriptions to the category definition the resultant category matching score for a project is a reflection of how closely the project is related to a particular category
26 Category Definition Created in the New System 26 A definition is a list of scientific terms from a thesaurus (300,000 terms and synonyms). Terms are selected by NIH Scientific Experts to define that research category. Terms are weighted to fine-tune the matching process. Terms from grants/projects are matched against definitions to produce category project lists.
27 Sample: Sleep Research Draft Fingerprint 27 Analytical Tool
28 Prior View of Data (FY 2009) 28
29 29 Summary level data
30 30 Drill down level data, not available on web in the past
31 31 NIH: Case Study Ahead of its time RCDC opened the door for analysis and review of research that was not previously possible (including decision intelligence practices) Additional uses of new analytical, visualization, and exploration technologies are now taking place because the platform exists!
32 Benefits Enhanced NIH s ability to: Leverage existing information and processes Conduct text mining and perform scientific portfolio analysis Provide transparency into government spending on research Greatly improved process Consistent methodology (one definition per category) Reproducible numbers New open platform to support decision intelligence Improved public understanding of NIH spending Access to project listings not available previously Searchable, accessible query tools and reports 32
33 33 Summary The opportunity and future for Medical Data Mining is HUGE! Practice areas cover the landscape: Patient, Provider, Payer, Research, Regulatory and IT Tackle it in chucks! Question based data mining Don t try to build the be-all end-all data source use what s available to begin to answer critical questions sooner rather than later Aspects of Data are critical The right Tool for the right job Analysis requires well trained analysts
SAP Thought Leadership Paper Healthcare and Big Data The Business Case for Using Big Data in Healthcare Exploring How Big Data and Analytics Can Help You Achieve Quality, Value-Based Care Table of Contents
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 655-662 International Research Publications House http://www. irphouse.com /ijict.htm Data
Laura Haas, IBM Fellow IBM Research - Almaden From Data to Foresight: Leveraging Data and Analytics for Materials Research 1 2011 IBM Corporation The road from data to foresight is long? Consumer Reports
Uncovering Value in Healthcare Data with Cognitive Analytics Christine Livingston, Perficient Ken Dugan, IBM Conflict of Interest Christine Livingston Ken Dugan Has no real or apparent conflicts of interest
Dr. Matthias Kaiserswerth Vice President, Europe and Director, IBM Research Big Data, Analytics, Intelligence: Potenziale und Nutzen Market Forces Driving Health Care Transformation Source: If applicable,
Big Data Analytics in Health Care S. G. Nandhini 1, V. Lavanya 2, K.Vasantha Kokilam 3 1 13mss032, 2 13mss025, III. M.Sc (software systems), SRI KRISHNA ARTS AND SCIENCE COLLEGE, 3 Assistant Professor,
Knowledgent White Paper Series Big Data and Healthcare Payers WHITE PAPER Summary With the implementation of the Affordable Care Act, the transition to a more member-centric relationship model, and other
A leader in the development and application of information technology to prevent and treat disease. About MOLECULAR HEALTH Molecular Health was founded in 2004 with the vision of changing healthcare. Today
Find the signal in the noise Electronic Health Records: The challenge The adoption of Electronic Health Records (EHRs) in the USA is rapidly increasing, due to the Health Information Technology and Clinical
Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012 Hexaware E-book on Predictive Analytics What is Data mining? Data mining,
An Introduction to Health Informatics for a Global Information Based Society A Course proposal for 2010 Healthcare Industry Skills Innovation Award Sponsored by the IBM Academic Initiative submitted by
Solutions For Health Plans Information, Insights, and Analysis to Help Manage Business Challenges Solutions for Health Plans Health plans are challenged with controlling medical costs, engaging members
The Canadian Realities of Big Data and Business Analytics Utsav Arora February 12, 2014 Things to think about for today How Important is Big Data for me? Why do I need to implement Big Data and Analytics
STATE OF CONNECTICUT State Innovation Model Health Information Technology (HIT) Council Answers to Questions for Zato The HIT Design Group has identified the set of questions listed below to better understand
What Lies Ahead? Trends to Watch: Health Care Product Development in North America What Lies Ahead? for 2015 DIA has released its third annual What Lies Ahead? report, providing experts insights into the
19th of March 2015 MediSapiens Ltd Because data is not knowledge Bio-IT solutions for improving cancer patient care Sami Kilpinen, Ph.D Co-founder, CEO MediSapiens Ltd Copyright 2015 MediSapiens Ltd. All
June 17, 2015 Innovate Forward Data Isn't Everything The Challenges of Big Data, Advanced Analytics, and Advance Computation Devices for Transportation Agencies. Using Data to Support Mission, Administration,
NextGen Dashboard Gain insight into your practice from every perspective. Deploy a solution that allows your practice to accommodate as many different users, data sets, and sources as you need. No exclusions.
Judith Hurwitz President and CEO Sponsored by Hitachi Introduction Only a few years ago, the greatest concern for businesses was being able to link traditional IT with the requirements of business units.
Extracting Value from Health Care Big Data with Predictive Analytics Gregory Veltri, CIO, Denver Health Mical DeBrow, PhD RN, Siemens Clinical Strategic Consulting DISCLAIMER: The views and opinions expressed
Computer-assisted coding and documentation for a changing healthcare landscape Reality is, in this new ICD-10 environment, providers will have two options: a) work harder; or b) find a new way to do things.
Value of Clinical and Business Data Analytics for Healthcare Payers NOUS INFOSYSTEMS LEVERAGING INTELLECT Abstract As there is a growing need for analysis, be it for meeting complex of regulatory requirements,
Big Analytics: A Next Generation Roadmap Cloud Developers Summit & Expo: October 1, 2014 Neil Fox, CTO: SoftServe, Inc. 2014 SoftServe, Inc. Remember Life Before The Web? 1994 Even Revolutions Take Time
Big Data Applications in Health Suleyman Akbas Overview 1. Papers 2. Introduction 3. Big Data in Health 4. Potential Use Areas 5. Current Applications 6. Privacy Issues 7. Discussion Papers 1. Big Data
SUCCESSES AND CHALLENGES DEVELOPING AN ENTERPRISE CLINICAL ANALYTICS SOLUTION Ambulatory Information Management (AIM) Dennis P. Sweeney, MBA July 16, 2013 Dennis P. Sweeney, MBA Mr. Dennis Sweeney, Tellogic
Augmented Search for Software Testing For Testers, Developers, and QA Managers New frontier in big log data analysis and application intelligence Business white paper May 2015 During software testing cycles,
Solution Overview Fusion Health Advantage Big Data Accelerator Platform Improve Patient Outcomes And Manage Them Cost Effectively Healthcare reform measures have intensified governmental scrutiny, thereby
Chapter 11 Managing Knowledge VIDEO CASES Video Case 1: How IBM s Watson Became a Jeopardy Champion. Video Case 2: Tour: Alfresco: Open Source Document Management System Video Case 3: L'Oréal: Knowledge
PREDICTIVE ANALYTICS FOR THE HEALTHCARE INDUSTRY By Andrew Pearson Qualex Asia Today, healthcare companies are drowning in data. According to IBM, most healthcare organizations have terabytes and terabytes
FIVE INDUSTRIES Where Big Data Is Making a Difference To understand how Big Data can transform businesses, we have to understand its nature. Although there are numerous definitions of Big Data, many will
Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends Spring 2015 Thomas Hill, Ph.D. VP Analytic Solutions Dell Statistica Overview and Agenda Dell Software overview Dell in
locuz.com Big Data Services Big Data At Locuz, we help the enterprise move from being a data-limited to a data-driven one, thereby enabling smarter, faster decisions that result in better business outcome.
Companion for SharePoint Topic Analyst Companion for SharePoint All Your Information Enterprise-ready Enrich SharePoint, your central place for document and workflow management, not only with an improved
Secure Cloud Computing Concepts Supporting Big Data in Healthcare Ryan D. Pehrson Director, Solutions & Architecture Integrated Data Storage, LLC Learning Objectives After this session, the learner should
Business Information Systems IT Enabled Services And Emerging Technologies Chapter 4: Facilitated e-learning Part 1 of 2 CA M S Mehta, FCA 1 Business Information Systems Task Statements 1.6 Consider the
The Five Pillars of Population Health Management Dr. Christopher Mathews Senior Vice President and Chief Medical Officer ZeOmega ZeOmega a forerunner in Population Health Management Transformation into
WHITE PAPER BEYOND THE EHR MEANINGFUL USE, CONTENT MANAGEMENT AND BUSINESS INTELLIGENCE By Richard Nelli, Senior Vice President and Chief Technical Officer, Streamline Health PAGE 2 EXECUTIVE SUMMARY When
Big Data and Real World Evidence Healthcare s most powerful currency: RWE as a patient-centric approach to Big-data Dr. Benjamin Hughes 8/26/2014 Today s take-aways RWE a focused approach to unlock Big
Exploration and Visualization of Post-Market Data Jianying Hu, PhD Joint work with David Gotz, Shahram Ebadollahi, Jimeng Sun, Fei Wang, Marianthi Markatou Healthcare Analytics Research IBM T.J. Watson
Operational Excellence, Data Driven Transformation Now Available at American Hospitals It's Time to Get LEAN White Paper Operational Excellence, Data Driven Transformation Now Available at American Hospitals
Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize
Big Data and Analytics NC Allied Health Regional Skills Summit November 5, 2013 Big Data experience Our Dickson Advanced Analytics (DA²) Vision: Dickson Advanced Analytics Group (DA 2 ) of Carolinas HealthCare
Augmented Search for IT Data Analytics New frontier in big log data analysis and application intelligence Business white paper May 2015 IT data is a general name to log data, IT metrics, application data,
The Business Value of Predictive Analytics Alys Woodward Program Manager, European Business Analytics, Collaboration and Social Solutions, IDC London, UK 15 November 2011 Copyright IDC. Reproduction is
Analytics for Banks and Finance Companies November 6, 2016 Outline About AlgoAnalytics Problems we can solve Our experience Technology Page 2 About AlgoAnalytics Analytics Consultancy Work at the intersection
Unlocking the Value of Healthcare s Big Data with Predictive Analytics Background The volume of electronic data in the healthcare industry continues to grow. Adoption of electronic solutions and increased
An Enterprise Framework for Business Intelligence Colin White BI Research May 2009 Sponsored by Oracle Corporation TABLE OF CONTENTS AN ENTERPRISE FRAMEWORK FOR BUSINESS INTELLIGENCE 1 THE BI PROCESSING
Discover more, discover faster. High performance, flexible NLP-based text mining for life sciences It s not information overload, it s filter failure. Clay Shirky Life Sciences organizations face the challenge
Using Predictions to Power the Business Wayne Eckerson Director of Research and Services, TDWI February 18, 2009 Sponsor 2 Speakers Wayne Eckerson Director, TDWI Research Caryn A. Bloom Data Mining Specialist,
Improving Data Processing Speed in Big Data Analytics Using HDFS Method M.R.Sundarakumar Assistant Professor, Department Of Computer Science and Engineering, R.V College of Engineering, Bangalore, India
Analytics: The Key Ingredient for the Success of ACOs Author: Senthil Raja Velusamy Business Analyst Healthcare Center of Excellence Executive Summary Accountable Care Organizations (ACOs) are structured
GE Healthcare Proven revenue cycle management supporting profitability in an era of healthcare reform. Enterprise-ready Profitability, efficiency, and enhanced quality of care A proven, next-generation
Centricity Practice Solution * Build your best practice. Set a new standard. Build new standards of excellence. By building new standards of efficiency. Centricity Practice Solution An integrated electronic
Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:
Whitepaper BPM for Structural Integrity Management in Oil and Gas Industry - Saurangshu Chakrabarty Abstract Structural Integrity Management (SIM) is an ongoing lifecycle process for ensuring the continued
Master of Science in Healthcare Informatics and Analytics Program Overview The program is a 60 credit, 100 week course of study that is designed to graduate students who: Understand and can apply the appropriate
The NIH Commons Summary The Commons is a shared virtual space where scientists can work with the digital objects of biomedical research, i.e. it is a system that will allow investigators to find, manage,
WHITE PAPER Talend Infosense Solution Brief Master Data Management for Health Care Reference Data Table of contents BUSINESS ISSUE: SOCIAL COLLABORATION AND DATA STEWARDSHIP... 5 BUSINESS ISSUE: FEEDBACK
NOUS INFOSYSTEMS LEVERAGING INTELLECT White Paper Importance of Population Health Abstract The revised healthcare regulations in US markets like the Affordable Care Act (ACA) law, the demands of providing
Impact Intelligence Health care organizations continue to seek ways to improve the value they deliver to their customers and pinpoint opportunities to enhance performance. Accurately identifying trends
Auto-Classification for Document Archiving and Records Declaration Josemina Magdalen, Architect, IBM November 15, 2013 Agenda IBM / ECM/ Content Classification for Document Archiving and Records Management
Elsevier ClinicalKey TM FAQs Table of Contents What is ClinicalKey? Where can I access ClinicalKey? What medical specialties are covered in ClinicalKey? What information is available through ClinicalKey?
PRIME DIMENSIONS Revealing insights. Shaping the future. Service Offering Prime Dimensions offers expertise in the processes, tools, and techniques associated with: Data Management Business Intelligence
Penang egovernment Seminar 2014 A New Era Of Analytic Megat Anuar Idris Head, Project Delivery, Business Analytics & Big Data Agenda Overview of Big Data Case Studies on Big Data Big Data Technology Readiness
Martin S. Kohn, MD, MS, FACEP, FACPE Chief Medical Scientist, Care Delivery Systems IBM Research email@example.com Putting IBM Watson to Work In Healthcare 2 SB 1275 Medical data in an electronic or
FALL 2012 VOL.54 NO.1 Thomas H. Davenport, Paul Barth and Randy Bean How Big Data is Different Brought to you by Please note that gray areas reflect artwork that has been intentionally removed. The substantive
Certification In SAS Programming Introduction to SAS Program What Lies Ahead In this session, you will gain answers to: Overview of Analytics Careers in Analytics Why Use SAS? Introduction to SAS System
All-in-one, Integrated HIM Workflow Solution A Venture of Meaningful & Actionable Data Clinical Knowledge Graph Natural Language Processing Clinical Data Normalization HIPAA Compliant Cloud Our proprietary
Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association
How a Vendor-Neutral Archive Can Support You Through Stage 2 Meaningful Use and Beyond By Dr. James Whitfill April 2015 Sponsored by: Hitachi Data Systems Contents Executive Summary... 2 Demystifying Stage
Delivering Real-Time Business Value for Healthcare Providers SAP Business Suite Powered by SAP HANA July 2013 Public The real-time opportunity Best-run healthcare facilities improve patient outcomes by
Big Data Analytics- Innovations at the Edge Brian Reed Chief Technologist Healthcare Four Dimensions of Big Data 2 The changing Big Data landscape Annual Growth ~100% Machine Data 90% of Information Human
New Clinical Research & Care Opportunities Through Big Data Informatics Gregory A. Jones Chief Technology Officer Health Sciences Global Business Unit September 2014 Safe Harbor Statement The following
Mastering the Data Game: Accelerating Integration and Optimization Healthcare systems are breaking new barriers in analytics as they seek to meet aggressive quality and financial goals. Mastering the Data
HEALTH DATA ANALYTICS OPTIMIZED CLINICAL AND FINANCIAL OUTCOMES DELIVERED FOR YOUR POPULATION HEALTH MANAGEMENT PROGRAMS HOW WE HELP YOU MAXIMIZE THE VALUE OF YOUR POPULATION HEALTH INITIATIVES Indegene
Career Opportunities in Healthcare Analytics presented by Kaiser Permanente Northwest Region Friday, May 13 at 1:00 pm Today s Speakers Background Brian Sikora, Director Delilah Moore, Manager Corporate
Association Technique on Prediction of Chronic Diseases Using Apriori Algorithm R.Karthiyayini 1, J.Jayaprakash 2 Assistant Professor, Department of Computer Applications, Anna University (BIT Campus),
Watson to Gain Ability to See with Planned $1B Acquisition of Merge Healthcare Deal Brings Watson Technology Together with Leader in Medical Images Armonk, NY and CHICAGO -- [August 6, 2015]: IBM (NYSE:
Driving Healthcare Efficiency through Transformational Sourcing www.avasant.com Reshaping of the U.S. Health eco-system The signing of the Patient Protection and Affordable Care Act (ACA) into law in 2010
BIG DATA ANALYSIS AND ITS NEED FOR EFFECTIVE E-GOVERNANCE Nishant Agnihotri 1 Dr. Aman Kumar Sharma 2 Research Scholar, Ph.D. Associate Professor, Computer Science Himachal Pradesh University, Shimla Himachal
Beyond listening Driving better decisions with business intelligence from social sources From insight to action with IBM Social Media Analytics State of the Union Opinions prevail on the Internet Social
What you can accomplish with IBMContent Analytics An Enterprise Content Management solution What is IBM Content Analytics? Alex On February 14-16, IBM s Watson computing system made its television debut
III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution
$ BIG DATA IN BANKING Table of contents What is Big Data?... How data science creates value in Banking... Best practices for Banking. Case studies... 3 7 10 1. Fraud detection... 2. Contact center efficiency
INTERSYSTEMS WHITE PAPER Accelerating Clinical Trials Through Shared Access to Patient Records Improved Access to Clinical Data Across Hospitals and Systems Helps Pharmaceutical Companies Reduce Delays
Big Data 101: Harvest Real Value & Avoid Hollow Hype 2 Executive Summary Odds are you are hearing the growing hype around the potential for big data to revolutionize our ability to assimilate and act on
The Power of Risk, Compliance & Security Management in SAP S/4HANA OUR AGENDA Key Learnings Observations on Risk & Compliance Management Current State Current Challenges The SAP GRC and Security Solution
Integrating a Big Data Platform into Government: Drive Better Decisions for Policy and Program Outcomes John Haddad, Senior Director Product Marketing, Informatica Digital Government Institute s Government
Martin S. Kohn, MD, MS, FACEP, FACPE Chief Medical Scientist, Care Delivery Systems IBM Research firstname.lastname@example.org Putting Analytics to Work In Healthcare 2012 International Business Machines Corporation
A Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks Text Analytics World, Boston, 2013 Lars Hard, CTO Agenda Difficult text analytics tasks Feature extraction Bio-inspired