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What Makes Healthcare Data Science so Hard & Interesting David Talby SVP Engineering, Atigeo davidtalby antigeo #datapopupseattle
UNSTRUCTURED Data Science POP-UP in Seattle D Produced by Domino Data Lab Domino’s enterprise data science platform is used by leading analytical organizations to increase productivity, enable collaboration, and publish models into production faster. www.dominodatalab.com #datapopupseattle
WHAT&MAKES&HEALTHCARE&DATA&SCIENCE SO&HARD&&&INTERESTING David&Talby& SVP&Engineering,&Atigeo& @davidtalby
“TECHNOLOGY&WILL&REPLACE&80%&OF&WHAT&DOCTORS&DO”& VINOD&KHOSLA,&2012 2 2
“as&many&as&40,500&patients&die&each&year&in&an&ICU&in& the&U.S.&due&to&misdiagnosis”&Winters&et&al.,&2012,&John&Hopkins&& “Combining&estimates&from&3&studies&yielded&a&rate&of& outpatient&diagnostic&errors&of&5.08%,&or&12&million&US& adults&every&year.”&Singhet&al.,&2014,&VA&Medical&Center Root$cause:$being$human Premature(closure Overconfidence Snap(judgment Fatigue Team&dynamics Prejudice 3
THE&PROMISE Real&time&monitoring Always&up&to&date&with&science Large&sample&size Works&24x7& No&trip,&no&waiting Cheaper More&accurate More&objective 4
HUMAN&NUANCES Can&you&distinguish&between&real&smiles&of&happiness&and&fake&smiles& trying&to&mask&frustration? “The&algorithm&was&able& to&identify&the&fake&smiles& 92%&of&the&time.& Humans,&on&the&other& hand,&performed&no&better& than&chance.”& MIT,&2012 6
MENTAL&HEALTH “Algorithms&correctly& predicted&which&atcrisk& youth&would&go&on&to& develop&psychosis&over&a& 2.5cyear&period&with& 100%&accuracy.”& Bedi&et&al.,&Nature'Schizophrenia,'2015 7
SAMPLE&HYBRID&ANALYTICS&PIPELINE Direct&&&ambient&Feedback Imagery,& drugs,&labs,&… Train&&&test&ensembles NLP&Features Freectext& clinical¬es Train&&&test&Classifiers Graph&Features Relationships& &&ontologies& Time&Series& Features Sensors&&& wearables 8
THE&OPEN&PROBLEM:&EXPLAINABILITY @DavidJBianco,&http://www2.mlsecproject.org/blog/oncexplainabilitycincmachineclearning 9
THE&MOMENT&YOU&PUT&A&MODEL&IN&PRODUCTION,& IT&STARTS&DEGRADING Never&Changing Always&Changing Political$&$ Economic$Models Banking$&$ eCommerce$fraud& Cyber$Security Natural$Language,$ Social$Behavior$ Models Google/Amazon& Search$models Physical$models:& Face$recognition& Voice$recognition$ Climate$models Online$Social$ Networking$Models/ Rules$ Automated$trading& RealAtime$ad$bidding [Gunjan&Gupta,&Atigeo,&2014]
SO&PUT&THE&RIGHT&MACHINERY&IN&PLACE 100%&Offcline 100%&Online Automated$ ‘challenger’$online$ evaluation$&$ deployment Traditional Scientific$Method: Test$a$Hypothesis HandAcrafted$ machine$ learned$ models Daily/weekly$ batch$retraining RealAtime$online$ learning$via$ passive$feedback Hard$Crafted$Rules Active$learning$via& Active$feedback Automated$ensemble,$ boosting$&$feature$ selection$techniques
STATE&OF&THE&PRACTICE&IN&HEALTHCARE 100%&Offcline 100%&Online Automated$ ‘challenger’$online$ evaluation$&$ deployment Traditional Scientific$Method: Test$a$Hypothesis HandAcrafted$ machine$ learned$ models Daily/weekly$ batch$retraining RealAtime$online$ learning$via$ passive$feedback Hard$Crafted$Rules Active$learning$via& Active$feedback Automated$ensemble,$ boosting$&$feature$ selection$techniques
THE&OPEN&PROBLEM:&MODEL&EVALUATION Evaluate&models&that&are:& • Personalized& • Localized& • Evolve&over&time& • Regulatory&acceptable& ?,'? 14
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@Atigeo& @davidtalby 16 ‹ #›
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