{"id":4481,"date":"2014-12-12T14:20:19","date_gmt":"2014-12-12T14:20:19","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?page_id=4481"},"modified":"2015-08-19T16:14:21","modified_gmt":"2015-08-19T20:14:21","slug":"paw-conference-videos","status":"publish","type":"page","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/paw-conference-videos\/","title":{"rendered":"2014 Conference Videos"},"content":{"rendered":"

Videos from Predictive Analytics World for Business and Healthcare<\/span><\/a> Boston \u2013 October 5-9, 2014<\/h1>\n

\"\" sessions are at the All Levels<\/strong>
\n\"\" sessions are at the Expert\/Practitioner Level<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\"\"<\/td>\n\"\"<\/td>\n<\/tr>\n
\"\"
\nKeynote: Blackjack Analytics: A Surprising Teacher from Which All Businesses Can Learn<\/span><\/strong>
Sameer Chopra, Orbitz Worldwide <\/strong>
*Presentation not available for viewing.<\/td>\n		\"\"
\nKeynote: Real-Time Modeling of Surgical Site Infections<\/span><\/a><\/strong>
\nJohn Cromwell, University of Iowa Hospitals & Clinics<\/strong><\/td>\n<\/tr>\n
\"\"
\nKeynote: Problems, then Techniques, then Toys. Keeping Your Predictive Analytics Right-side Up<\/span><\/a> <\/strong>
\nJohn Foreman, MailChimp <\/strong><\/td>\n		\"\"
\nKeynote: Big Data and Clinical Decision Support<\/span><\/a><\/strong>
\nDr. Martin Kohn, Jointly Health <\/strong><\/td>\n<\/tr>\n
\"\"
\nKeynote: UPS Analytics – The Road to Optimization<\/span><\/a><\/strong>
\nJack Levis, UPS <\/strong><\/td>\n		\"\"Keynote: Predictive Analytics: Advancing Precision and Population Medicine<\/span><\/a><\/strong>
\nGeorge Savage, Proteus Digital Health<\/strong><\/td>\n<\/tr>\n
\"\"Special Plenary Session:
\nThe Power (and Peril) of Predictive Analytics<\/span><\/a><\/strong>
\nDr. John Elder, Elder Research, Inc.<\/td>\n		\"\"Special Plenary Session:
\nThe Peril of Vast Search (and How Target Shuffling Can Save Science) <\/span><\/a><\/strong>
\nDr. John Elder, Elder Research, Inc.<\/td>\n<\/tr>\n
Expert Panel<\/strong><\/td>\n Expert Panel<\/strong><\/td>\n<\/tr>\n
Necessary Skills of the Quant: Finance, Fraud, and Marketing<\/span><\/a><\/strong>
\nSameer Chopra, Orbitz Worldwide
\nJack Levis, UPS
\nThomas Hill, Dell Software Group \/ StatSoft<\/td>\n		Healthcare Analytics: Potential vs. Reality<\/span><\/a><\/strong>
\nGeorge Savage, MD, Proteus Digital Health
\nJohn Cromwell, MD, University of Iowa
\nKen Yale, JD, DDS, ActiveHealth Management<\/td>\n<\/tr>\n
TOPIC: Advertising Effectiveness
\n\"\" Case Study: Baseball Stadiums <\/em><\/strong><\/td>\n		 TOPIC: Claims Analytics
\n\"\" Case Study: DentaQuest<\/em><\/strong><\/td>\n<\/tr>\n
\"\"
\nA Fresh Look at the Effects of Promotion on Baseball Attendance Using Hierarchical Bayesian Analysis<\/span><\/a> <\/strong>
\nTyler Deutsch, Northwestern University & Sagence<\/td>\n		\"\"
\nImproving Provider Performance and Patient Outcomes with Evidence-Based Scoring<\/span><\/a> <\/strong>
\nDaniel Bailey, Elder Research<\/td>\n<\/tr>\n
\"\"
\nViswanath Srikanth, Northwestern University & IBM<\/td>\n		\"\"
\nShaju Puthussery, DentaQuest<\/td>\n<\/tr>\n
\"\" TOPIC:
\nAlgorithmic Trading<\/strong><\/td>\n		 TOPIC: Deploying Risk Models
\n\"\" Case Study: State of Maine hospitals <\/em><\/strong><\/td>\n<\/tr>\n
\"\"
\nPredictive analytics for Asset Managers<\/span><\/a> <\/strong>
\nSteve Krawciw, Able Markets<\/td>\n		\"\"
\nDeploying Predictive Patient Risk Models through a Health Information Exchange (HIE)<\/span><\/a> <\/strong>
\nDevore Culver, HealthInfoNet<\/td>\n<\/tr>\n
\"\" TOPIC:
\nAnalytics in Consumer Banking<\/strong><\/td>\n		 TOPIC: Disease Modeling
\n\"\" Case Study: Southern Nazerene University <\/em><\/strong><\/td>\n<\/tr>\n
\"\"
\nEmbedding Predictive Analytics Within the Corporate Culture-What are the Challenges in the Big Data World?<\/span><\/a><\/strong>
\nRichard Boire, Boire Filler Group<\/td>\n		\"\"
\nDeveloping a Mortality Prediction Model for Disseminated Intravascular Coagulation (DIC)<\/span><\/a><\/strong>
\nLinda Miner, PhD, Southern Nazerene University<\/td>\n<\/tr>\n
TOPIC: Analytics Strategy
\n\"\" Case Studies: CFPB, Capital One, Citibank & Bank of America <\/em><\/strong><\/td>\n		 TOPIC: Healthcare Informatics
\n\"\" Case Study: University of California, Irvine <\/em><\/strong><\/td>\n<\/tr>\n
\"\"
\nSpotting the Wisdom in the Noise: Using Data Science to Identify and Eradicate Consumer Concerns<\/span><\/a><\/strong>
\nBrandon Purcell, Beyond the Arc<\/td>\n		\"\"
\nHealthcare 2020: How Emerging Technologies Will Advance Clinical Practice and Research<\/span><\/a> <\/strong>
\nCharles Boicey, Stony Brook Medicine<\/td>\n<\/tr>\n
\"\" TOPIC:
\nAnalytics Tools<\/strong><\/td>\n		 TOPIC: Personalized What-if Analysis
\n\"\" Case Study: WA Univ. St. Louis School of Medicine & PotentiaMED <\/em><\/strong><\/td>\n<\/tr>\n
\"\"
\nPython for Data Science<\/span><\/a><\/strong>
\nField Cady, Think Big Analytics<\/td>\n		\"\"
\nUsing Predictive Analytics to Empower Cancer Patients through “MyCancerJourney”<\/span><\/a><\/strong>
\nRobert Palmer, PotentiaMED<\/td>\n<\/tr>\n
TOPIC: Big Data
\n\"\" Case Study: Sears Holdings Corporation<\/em><\/strong><\/td>\n		\"\"
\nJay Piccirillo, Washington University in St. Louis School of Medicine<\/td>\n<\/tr>\n
\"\"
\nHadoop Use Cases: Speeding Up Data Workloads<\/span><\/a><\/strong>
\nAndy McNalis, Sears Holding Corporation<\/td>\n<\/tr>\n
TOPIC: Churn Modeling
\n\"\" Case Study: Paychex <\/em><\/strong><\/td>\n		 TOPIC: Predicting Disease and Infection
\n\"\" Case Study: Baptist Health <\/em><\/strong><\/td>\n<\/tr>\n
\"\"
\nCombat Client Churn with Predictive Analytics<\/span><\/a><\/strong>
\nPhilip O’Brien, Paychex<\/td>\n		\"\"
\nPredicting the Invisible Patient: Using Predictive Analytics to Reduce Suffering, Save Lives, and Optimize Cost of Care<\/span><\/a> <\/strong>
\nKatrina Belt, Baptist Health<\/td>\n<\/tr>\n
TOPIC: Churn Modeling
\n\"\" Case Study: nTelos Wireless<\/em><\/strong><\/td>\n		 TOPIC: Predicting Insurance Costs
\n\"\" Case Study: Pensylvania Department of Public Welfare <\/em><\/strong><\/td>\n<\/tr>\n
\"\"
\nImproving Customer Retention & Profitability<\/span><\/a><\/strong>
\nJohn Ainsworth, Elder Research, Inc.<\/td>\n		\"\"Superutilizers Made Simple. Identifying High-Cost Recipients Using a Model Any Medicaid Agency Could Implement<\/span><\/a><\/strong>
\nAran Canes, Open Minds<\/td>\n<\/tr>\n
TOPIC: Cloud Analytics
\n\"\" Case Study: Verizon<\/em><\/strong><\/td>\n		 TOPIC: Readmission Risk
\n\"\" Case Study: UPMC Health Plan <\/em><\/strong><\/td>\n<\/tr>\n
\"\"
\nThird Generation Contextual Learning as a Service and Consumer Data-Haven Practice<\/span><\/a> <\/strong>
\nMadhusudan Raman, Verizon<\/td>\n		\"\"
\nUsing Association Rule Mining to Identify Risks for Readmissions<\/span><\/a><\/strong>
\nScott Zasadil, PhD, UPMC Health Plan<\/td>\n<\/tr>\n
TOPIC: Credit Scoring
\n\"\" Case Study: Kabbage<\/em><\/strong><\/td>\n		 \"\" TOPIC:
\nResource Optimization<\/strong><\/td>\n<\/tr>\n
\"\"
\nData Science Approach to Small and Medium Business Lending<\/span><\/a><\/strong>
\nPinar Donmez, Kabbage<\/td>\n		\"\"
\nUsing Predictive Analytics to Forecast Hospital Patient Volume for Hospital Resource Allocation and Staffing<\/span><\/a><\/strong>
\nNephi Walton, MD, Washington University<\/td>\n<\/tr>\n
TOPIC: Customer Satisfaction & Retention
\n\"\" Case Study: Citrix<\/em><\/strong><\/td>\n		 TOPIC: Targeted Care Management
\n\"\" Case Study: ActiveHealth Management<\/em><\/strong><\/td>\n<\/tr>\n
\"\"
\nPredicting Customer Experience Risk in B2B World<\/span><\/a><\/strong>
\nMike Stringer, Madhav Chinta, & Jim Regetz, Citrix Data Science<\/td>\n		\"\"
\nSignificant Improvements in Population Health Management<\/span><\/a><\/strong>
\nKen Yale, JD, DDS, ActiveHealth Management<\/td>\n<\/tr>\n
\nThe remaining videos below are from Predictive Analytics World for Business<\/a> in Boston<\/strong><\/td>\n<\/tr>\n
\"\"<\/td>\n\"\"<\/td>\n<\/tr>\n
\"\" TOPIC: Data Cleansing<\/strong><\/td>\n \"\" TOPIC: Data Privacy<\/strong><\/td>\n<\/tr>\n
\"\"
\nData Preparation from the Trenches: 4 Approaches to Deriving Attributes<\/span><\/a><\/strong>
\nDean Abbott, Abbott Analytics<\/td>\n		\"\"
\nPredictive Analytics and Privacy by Design<\/a><\/span><\/strong>
\nJeff Kosseff, Covington & Burling, LLP<\/td>\n<\/tr>\n
\"\" TOPIC:
\nEnterprise-wide Deployment<\/strong><\/td>\n		 TOPIC: Fraud Detection; Analytics in Gaming
\n\"\" Case Study: Activision<\/em> <\/strong><\/td>\n<\/tr>\n
\"\"
\nOracle’s Internal Use of Data Mining and Predictive Analytics<\/span><\/a><\/strong>
\nCharles Berger, Oracle<\/td>\n		\"\"
\nCheating Detection in Call of Duty<\/span><\/a><\/strong>
\nJosh Hemann, Activision<\/td>\n<\/tr>\n
TOPIC: Infrastructure Planning
\n\"\" Case Study: Facebook<\/em><\/strong><\/td>\n		 TOPIC: Large-Scale Continuous Learning
\n\"\" Case Study: eBay<\/em> <\/strong><\/td>\n<\/tr>\n
\"\"
\nManaging Large-Scale Infrastructure with Predictive Analytics<\/span><\/a><\/strong>
\nClinton Brownley, Facebook<\/td>\n		\"\"
\nImportance of Speed and Relevance to eBay and Our Big Data Strategies<\/span><\/a><\/strong>
\nGayatri Patel, eBay<\/td>\n<\/tr>\n
TOPIC: Marketing Attribution
\n\"\" Case Study: LinkedIn<\/em><\/strong><\/td>\n		 \"\" TOPIC:
\nPersuasion Modeling (aka Uplift Modeling) <\/strong><\/td>\n<\/tr>\n
\"\"
\n Increasing B2B Marketing Contribution through Optimal Marketing Attribution Analysis Techniques<\/span><\/a> <\/strong>
\nMay Xu, Neethi Mary Thomas, Rajat Mishra, Mu Sigma<\/td>\n		\"\"
\nPinpointing the Persuadables: Convincing the Right Customers and the Right Voters<\/span><\/a><\/strong>
\nDaniel Porter, BlueLabs<\/td>\n<\/tr>\n
TOPIC: Project Risk Assessment
\n\"\" Case Study: State Street Corporation <\/em> <\/strong><\/td>\n		 TOPIC: Risk Detection; Government Applications
\n\"\" Case Study: State Auditor’s Office<\/em><\/strong><\/td>\n<\/tr>\n
\"\"How Can Predictive Analytics Help Avoid $1.2 Million in IT Project Development Costs?<\/span><\/a><\/strong>
\nScott Lancaster, State Street Corp.<\/td>\n		\"\"
\nRisk Analytics Engine at State Auditor’s Office<\/span><\/a><\/strong>
\nKleber Gallardo, Alivia Technology<\/td>\n<\/tr>\n
TOPIC: Targeting Email
\n\"\" Case Study: Ameublements Tanguay<\/em> <\/strong><\/td>\n		 TOPIC: Uplift Modeling
\n\"\" Case Study: Fidelity <\/em> <\/strong><\/td>\n<\/tr>\n
\"\"
\nPredictive Analytics to the Rescue of Email Marketing<\/span><\/a><\/strong>
\nRoger Plourde, Intema Solutions<\/td>\n		\"\"
\nUplift Modeling: Introduction, Applications, Comparisons, and Latest Developments<\/span><\/a><\/strong>
\nVictor Lo, Fidelity Investments & Bentley University<\/td>\n<\/tr>\n
TOPIC: Workforce Analytics – Retention
\n\"\" Case Study: A Major Financial Services Call Center<\/em> <\/strong><\/td>\n		 TOPIC: Workforce Analytics – Workload Management
\n\"\" Case Study: IBM<\/em> <\/strong><\/td>\n<\/tr>\n
\"\"
\nData Science Approach to Reduce Call Center Employee Attrition<\/span><\/a><\/strong>
\nPasha Roberts, Talent Analytics<\/td>\n		\"\"
\nData-Driven Transformation in End-to-End Sales Transaction Support<\/span><\/a><\/strong>
\nPitipong Lin, IBM<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

View the 2013 PAW Conference Videos from Boston<\/span><\/a><\/h2>\n","protected":false},"excerpt":{"rendered":"

Videos from Predictive Analytics World for Business and Healthcare Boston \u2013 October 5-9, 2014 sessions are at the All Levels sessions are at the Expert\/Practitioner Level Keynote: Blackjack Analytics: A Surprising Teacher from Which All Businesses Can LearnSameer Chopra, Orbitz Worldwide *Presentation not available for viewing. Keynote: Real-Time Modeling of Surgical Site Infections John Cromwell, […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"template-page-full.php","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-4481","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/pages\/4481","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=4481"}],"version-history":[{"count":58,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/pages\/4481\/revisions"}],"predecessor-version":[{"id":6128,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/pages\/4481\/revisions\/6128"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=4481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}