Mining Unstructured Data:Practical Applications, from the Strata O'Reilly Making Data Work Conference March 2012
Alyona Medelyan (Pingar), Anna Divoli (Pingar) presented at Strata O'Reilly Making Data Work Conference on March 1, 2012 The challenge of unstructured data is a top priority for organizations that are looking for ways to search, sort, analyze and extract knowledge from masses of documents they store and create daily. Text mining uses knowledge-driven algorithms to make sense of documents in a similar way a person would do by reading them. Lately, text mining and analytics tools became available via APIs, meaning that organizations can take immediate advantage these tools. We discuss three examples of how such APIs were utilized to solve key business challenges. Most organizations dream of paperless office, but still generate and receive millions of print documents. Digitizing these documents and intelligently sharing them is a universal enterprise challenge. Major scanning providers offer solutions that analyze scanned and OCR’d documents and then store detected information in document management systems. This works well with pre-defined forms, but human interaction is required when scanning unstructured text. We describe a prototype build for the legal vertical that scans stacks of paper documents and on the fly categorizes and generates meaningful metadata. In the area of forensics, intelligence and security, manual monitoring of masses of unstructured data is not feasible. The ability of automatically identify people’s names, addresses, credit card and bank account numbers and other entities is the key. We will briefly describe a case study of how a major international financial institution is taking advantage of text mining APIs in order to comply with a recent legislation act. In healthcare, although Electronic Health Records (EHRs) have been increasingly becoming available over the past two decades, patient confidentiality and privacy concerns have been acting as obstacles from utilizing the incredibly valuable information they contain to further medical research. Several approaches have been reported in assigning unique encrypted identifiers to patients’ ID but each comes with drawbacks. For a number of medical studies consistent uniform ID mapping is not necessary and automated text sanitization can serve as a solution. We will demonstrate how sanitization has practical use in a medical study. And read a full interview with Alyona and Anna at http://radar.oreilly.com/2012/02/unstructured-data-analysis-tools.html
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Mining Unstructured Data:Practical Applications, from the Strata O'Reilly Making Data Work Conference March 2012
- 1. Mining Unstructured Data: Practical Applications Alyona Medelyan @zelandiya Anna Divoli @annadivoli
- 2. Problem 1 New York London How do lawyers scan, file, store & share client’s case documents efficiently? Images: Ambro / FreeDigitalPhotos.net
- 3. slambo_42@flickr Anoto AB@flickr EHR EMR PHR How do doctors, patients & researchers distribute & share medical records efficiently?
- 4. The FATCA Legislation Problem 3 Takes effect 1 January 2013 annual report 30% witholding tax Foreign Financial waiver Ins.tu.on with IRS agreement U.S. account holders U.S. ownership en..es with without Custodian bank waiver waiver without IRS agreement 30% witholding tax How can a financial institution find U.S. citizens in masses of paperwork efficiently?
- 5. How much time do we actually spend on … Searching, gathering info 17 Wri.ng emails 14 Crea.ng docs 13 Analyzing info 10 Reviewing docs 9 Organizing docs 7 Crea.ng presenta.ons 7 Edi.ng images 6 Entering data 6 Translates to annual costs: Search: 17h / week = $37,000 / year Approving docs 4 Publishing docs 4 IDC: Hidden cost of information Transla.ng docs 1 average hours / week
- 6. introduction conclusions unstructured data real life problems compliance unstructured data in finance & text analytics healthcare metadata records issues in legal domain
- 7. Social News Emails Media Audio Images Databases Videos Literature Blogs
- 8. unstructured data Linguistics Search Statistics Data Extraction Text Processing Document Organization Machine Learning Business Intelligence Natural Language Processing Opinion Mining Text Mining
- 9. What can one mine from unstructured data? keywords text text text text text text tags text text text text text text sentiment text text text text text text genre categories taxonomy terms entities names biochemical patterns … entities text text text text text text text text text text text text text text text text text text
- 10. Social News Emails Media Audio Images Databases Videos Literature Blogs
- 11. text text text text text text text text text text text text text text text text text text People U.S. politicians News about U.S. politicians News Structured & unstructured data interplay Unique iden.fiers Structured biological Literature references data Experts’ annota.on (free text)
- 12. introduction conclusions unstructured data real life problems compliance unstructured data in finance & text analytics healthcare metadata records issues in legal domain
- 13. Legal document processing pipeline scan save ocr New York metadata London dms Images: Ambro / FreeDigitalPhotos.net
- 14. jacockshaw@flickr Assigning metadata (approximation) 15 docs per day 3 min per doc 0.75 h per day 240 working days per year $200 hourly charge $36,000 per year per lawyer Keyword extraction 0.0027 min per doc 10 min for yearly worth of docs
- 15. Integra.ng metadata extrac.on with scanning h[p://www.youtube.com/watch?v=kluVp25upag
- 16. Efficient (legal) document processing pipeline keywords tags metadata dms
- 17. introduction conclusions unstructured data real life problems compliance in finance unstructured data & text analytics healthcare metadata records issues in legal domain
- 18. EMR PHR EHR slambo_42@flickr Anoto AB@flickr
- 19. Na.onal Alliance for Health Informa.on Technology EMR (NAHIT) defini.ons EHR PHR ? Discon.nued! 1. Name, birth date, blood type 2. Emergency contact(s) 3. Primary caregiver/phone number 4. Medicines, dosages, and how long taken 5. Allergies/allergic reac.ons 6. Date of last physical 7. Dates/results of tests and screenings 8. Major illnesses/surgeries and their dates 9. Chronic diseases PHI 10. Family illness history 11. … h?p://www.nlm.nih.gov/medlineplus/magazine/ de-‐idenHficaHon process
- 20. Medical researchers … records with removed PHI: use pa.ent records informa.on from structured fields for discoveries… but mostly from free text! AMIA 2012
- 21. siliconangle.com/blog/ www.hcpro.com www.informaHon-‐age.com “The Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy and Security Rules” “The Pa.ent Safety and Quality Improvement Act of 2005 (PSQIA) Pa.ent Safety Rule”
- 22. 18 identifiers! PHI Names Vehicle iden.fiers & serial numbers, incl. license Geographic subdivisions plate numbers smaller than a State: street address, city, county, precinct, zip code… Device iden.fiers & Dates (except year): birth, serial numbers admission, discharge… URLs / IP addresses Phone / Fax numbers Email addresses Biometric iden.fiers, including finger and voice prints Social security # Face photo images Medical records # & any comparable images Health plan beneficiary# Any other unique IDs etc. Accounts #
- 23. slambo_42@flickr Thanks for discussions: Nigam Shah, Stanford Eneida Mendonca, UWinscosin, Madison Irena Spasic, Cardiff University text text text text text text text text text text text text text text text text text text keywords tags Anoto AB@flickr
- 24. introduction conclusions unstructured data real life problems compliance in finance unstructured data & text analytics healthcare metadata records issues in legal domain
- 25. The FATCA Legislation Takes effect 1 January 2013 annual report 30% witholding tax waiver Foreign Financial Ins.tu.on with IRS agreement U.S. account holders U.S. ownership en..es with without Custodian bank waiver waiver 30% witholding tax without IRS agreement
- 26. FATCA COMPLIANCE – STEP 1 Detect U.S. citizenship indicators
- 27. Recommended Solution from FATCA Legislation: • “Query an electronic database using standard queries in programming languages” • “Adopt similar approaches as used for the Anti-money-laundering and Know-your-customer requirements” • “Note that information, data, or files are not electronically searchable if they are stored as images”
- 28. walmink, thomwatson@flikr FATCA COMPLIANCE – STEP 2 Contact client for additional info or a waver
- 29. Actual Solution for the FATCA Legislation: link analysis gather the trail client’s data ocr convert all images to text en.ty extrac.on detect loca.ons, bank numbers analysis auto-‐categorize check resolve inconsistencies
- 31. introduction conclusions unstructured data real life problems compliance in finance unstructured data & text analytics healthcare metadata records issues in legal domain
- 32. Alyona Medelyan, PhD Anna Divoli, PhD @zelandiya @annadivoli Natural Language Processing Biomedical Text Mining Text Mining Search User Interfaces Wikipedia Mining Human Factors Machine Learning Knowledge Discovery Try out text analytics provided by the Pingar API! Online demo: apidemo.pingar.com Free Sandbox account: pingar.com/get-the-api
Notes de l'éditeur
- To summarize:In this talk we gave a brief overview of what text analytics is and how powerful it is when dealing with unstructured data.We presented 3 real world examples, where text analytics eliminates manual boring error-prone labor.In the legal domain, keyword and taxonomy term extraction facilitates automated metadata assignment.Healthcare benefits from automated entity extraction for de-identification (sanitization) and mining useful associations.In the area of compliance & forensics, text analytics helpsscanning from massive amounts of data.No matter how much further our technology develops, we will always continue to communicate in human language. The amount of unstructured data will only increase. Already there are areas where manual analytics is not sustainable. And there will be even more need for efficient text analytics in the future.