No
BOOLEAN
Yes
BOOLEAN
As this corpus of annotated patient notes comprises original healthcare data which contains protected health information (PHI) per The Health Information Portability and Accountability Act of 1996 (HIPAA) BIBREF16 and can be joined to the MIMIC-III database, individuals who wish to access to the data must satisfy all requirements to access the data contained within MIMIC-III. To satisfy these conditions, an individual who wishes to access the database must take a “Data or Specimens Management” course, as well as sign a user agreement, as outlined on the MIMIC-III database webpage, where the latest version of this database will be hosted as “Annotated Clinical Texts from MIMIC” BIBREF17. This corpus can also be accessed on GitHub after completing all of the above requirements.
Phenotype definitions, dataset distribution, patient note statistics, inter-operator error, and the results of baseline models were reported to demonstrate that the dataset is well-suited for the development of both rule-based and statistical models for patient phenotyping. We hope that the release of this dataset will accelerate the development of algorithms for patient phenotyping, which in turn would significantly help medical research progress faster.
2003.03044
Introduction Ġand ĠRelated ĠWork </s> With Ġthe Ġwidespread Ġadoption Ġof Ġelectronic Ġhealth Ġrecords Ġ( E HR s ), Ġmedical Ġdata Ġare Ġbeing Ġgenerated Ġand Ġstored Ġdigitally Ġin Ġvast Ġquantities ĠB IB REF 0 . ĠWhile Ġmuch ĠE HR Ġdata Ġare Ġstructured Ġand Ġam enable Ġto Ġanalysis , Ġthere Ġappears Ġto Ġbe Ġlimited Ġhom ogeneity Ġin Ġdata Ġcomple teness Ġand Ġquality ĠB IB REF 1 , Ġand Ġit Ġis Ġestimated Ġthat Ġthe Ġmajority Ġof Ġhealthcare Ġdata Ġare Ġbeing Ġgenerated Ġin Ġun struct ured , Ġtext - based Ġformat ĠB IB REF 2 . ĠThe Ġgeneration Ġand Ġstorage Ġof Ġthese Ġun struct ured Ġdata Ġcome Ġconcurrently Ġwith Ġpolicy Ġinitiatives Ġthat Ġseek Ġto Ġutilize Ġprevent ative Ġmeasures Ġto Ġreduce Ġhospital Ġadmission Ġand Ġread mission ĠB IB REF 3 . </s> Ch ronic Ġillnesses , Ġbehavioral Ġfactors , Ġand Ġsocial Ġdetermin ants Ġof Ġhealth Ġare Ġknown Ġto Ġbe Ġassociated Ġwith Ġhigher Ġrisks Ġof Ġhospital Ġread mission , ĠB IB REF 4 Ġand Ġthough Ġbehavioral Ġfactors Ġand Ġsocial Ġdetermin ants Ġof Ġhealth Ġare Ġoften Ġdetermined Ġat Ġthe Ġpoint Ġof Ġcare , Ġtheir Ġidentification Ġmay Ġnot Ġalways Ġbe Ġcurated Ġin Ġstructured Ġformat Ġwithin Ġthe ĠE HR Ġin Ġthe Ġsame Ġmanner Ġthat Ġother Ġfactors Ġassociated Ġwith Ġroutine Ġpatient Ġhistory Ġtaking Ġand Ġphysical Ġexamination Ġare ĠB IB REF 5 . ĠIdent ifying Ġthese Ġpatient Ġattributes Ġwithin ĠE HR s Ġin Ġa Ġreliable Ġmanner Ġhas Ġthe Ġpotential Ġto Ġreveal Ġaction able Ġassociations Ġwhich Ġotherwise Ġmay Ġremain Ġpoorly Ġdefined . </s> As ĠE HR s Ġact Ġto Ġstream line Ġthe Ġhealthcare Ġadministration Ġprocess , Ġmuch Ġof Ġthe Ġdata Ġcollected Ġand Ġstored Ġin Ġstructured Ġformat Ġmay Ġbe Ġthose Ġdata Ġmost Ġrelevant Ġto Ġreimbursement Ġand Ġbilling , Ġand Ġmay Ġnot Ġnecessarily Ġbe Ġthose Ġdata Ġwhich Ġwere Ġmost Ġrelevant Ġduring Ġthe Ġclinical Ġencounter . ĠFor Ġexample , Ġa Ġdiabetic Ġpatient Ġwho Ġdoes Ġnot Ġadhere Ġto Ġan Ġinsulin Ġtreatment Ġregimen Ġand Ġwho Ġthereafter Ġpresents Ġto Ġthe Ġhospital Ġwith Ġsymptoms Ġindicating Ġdiabetic Ġket o acid osis Ġ( D KA ) Ġwill Ġbe Ġtreated Ġand Ġconsidered Ġadministr atively Ġas Ġan Ġindividual Ġpresenting Ġwith ĠD KA , Ġthough Ġthat Ġmedical Ġemergency Ġmay Ġhave Ġbeen Ġsecondary Ġto Ġnon - ad herence Ġto Ġthe Ġinitial Ġtreatment Ġregimen Ġin Ġthe Ġsetting Ġof Ġdiabetes . ĠIn Ġthis Ġinstance , Ġany Ġretrospective Ġstudy Ġanalyzing Ġonly Ġthe Ġstructured Ġdata Ġfrom Ġmany Ġsimilarly Ġselected Ġclinical Ġencounters Ġwill Ġnecessarily Ġthen Ġunderestimate Ġthe Ġeffect Ġof Ġtreatment Ġnon - ad herence Ġwith Ġrespect Ġto Ġhospital Ġadmissions . </s> While Ġthis Ġform Ġof Ġhigh Ġcontext Ġinformation Ġmay Ġnot Ġbe Ġfound Ġin Ġthe Ġstructured ĠE HR Ġdata , Ġit Ġmay Ġbe Ġaccessible Ġin Ġpatient Ġnotes , Ġincluding Ġnursing Ġprogress Ġnotes Ġand Ġdischarge Ġsumm aries , Ġparticularly Ġthrough Ġthe Ġutilization Ġof Ġnatural Ġlanguage Ġprocessing Ġ( N LP ) Ġtechnologies . ĠB IB REF 6 , ĠB IB REF 7 ĠGiven Ġprogress Ġin ĠN LP Ġmethods , Ġwe Ġsought Ġto Ġaddress Ġthe Ġissue Ġof Ġun struct ured Ġclinical Ġtext Ġby Ġdefining Ġand Ġannot ating Ġclinical Ġphen otypes Ġin Ġtext Ġwhich Ġmay Ġotherwise Ġbe Ġprohib itive ly Ġdifficult Ġto Ġdiscern Ġin Ġthe Ġstructured Ġdata Ġassociated Ġwith Ġthe Ġtext Ġentry . ĠFor Ġthis Ġtask , Ġwe Ġchose Ġthe Ġnotes Ġpresent Ġin Ġthe Ġpublicly Ġavailable ĠM IM IC Ġdatabase ĠB IB REF 8 . </s> Given Ġthe ĠM IM IC Ġdatabase Ġas Ġsubstrate Ġand Ġthe Ġaforementioned Ġpolicy Ġinitiatives Ġto Ġreduce Ġunnecessary Ġhospital Ġread missions , Ġas Ġwell Ġas Ġthe Ġgoal Ġof Ġproviding Ġstructure Ġto Ġtext , Ġwe Ġelected Ġto Ġfocus Ġon Ġpatients Ġwho Ġwere Ġfrequently Ġread mitted Ġto Ġthe ĠIC U ĠB IB REF 9 . ĠIn Ġparticular , Ġa Ġpatient Ġwho Ġis Ġadmitted Ġto Ġthe ĠIC U Ġmore Ġthan Ġthree Ġtimes Ġin Ġa Ġsingle Ġyear . ĠBy Ġdefining Ġour Ġcohort Ġin Ġthis Ġway Ġwe Ġsought Ġto Ġensure Ġwe Ġwere Ġable Ġto Ġcapture Ġthose Ġcharacteristics Ġunique Ġto Ġthe Ġcohort Ġin Ġa Ġmanner Ġwhich Ġmay Ġyield Ġaction able Ġintelligence Ġon Ġinterventions Ġto Ġassist Ġthis Ġpatient Ġpopulation . </s> Data ĠCharacter istics </s> We Ġhave Ġcreated Ġa Ġdataset Ġof Ġdischarge Ġsumm aries Ġand Ġnursing Ġnotes , Ġall Ġin Ġthe ĠEnglish Ġlanguage , Ġwith Ġa Ġfocus Ġon Ġfrequently Ġread mitted Ġpatients , Ġlabeled Ġwith Ġ15 Ġclinical Ġpatient Ġphen otypes Ġbelieved Ġto Ġbe Ġassociated Ġwith Ġrisk Ġof Ġrecurrent ĠInt ensive ĠCare ĠUnit Ġ( IC U ) Ġread mission Ġper Ġour Ġdomain Ġexperts Ġ( co - authors ĠL AC , ĠPAT , ĠD AG ) Ġas Ġwell Ġas Ġthe Ġliterature . ĠB IB REF 10 ĠB IB REF 11 ĠB IB REF 12 </s> Each Ġentry Ġin Ġthis Ġdatabase Ġof Ġconsists Ġof Ġa ĠSubject ĠIdent ifier Ġ( integer ), Ġa ĠHospital ĠAdmission ĠIdent ifier Ġ( integer ), ĠCategory Ġ( string ), ĠText Ġ( string ), Ġ15 ĠPhen otypes Ġ( binary ) Ġincluding ĠâĢ ľ None âĢ Ŀ Ġand ĠâĢ ľ Un sure âĢ Ŀ , ĠB atch ĠDate Ġ( string ), Ġand ĠOper ators Ġ( string ). ĠThese Ġvariables Ġare Ġsufficient Ġto Ġuse Ġthe Ġdata Ġset Ġalone , Ġor Ġto Ġjoin Ġit Ġto Ġthe ĠM IM IC - III Ġdatabase Ġby ĠSubject ĠIdent ifier Ġor ĠHospital ĠAdmission ĠIdent ifier Ġfor Ġadditional Ġpatient - level Ġor Ġadmission - level Ġdata , Ġrespectively . ĠThe ĠM IM IC Ġdatabase ĠB IB REF 8 Ġwas Ġutilized Ġto Ġextract ĠSubject ĠIdent ifiers , ĠHospital ĠAdmission ĠIdent ifiers , Ġand ĠNote ĠText . </s> An not ated Ġdischarge Ġsumm aries Ġhad Ġa Ġmedian Ġtoken Ġcount Ġof Ġ14 17 . 50 Ġ( Q 1 - Q 3 : Ġ10 46 . 75 Ġ- Ġ1926 . 00 ) Ġwith Ġa Ġvocabulary Ġof Ġ26 454 Ġunique Ġtokens , Ġwhile Ġnursing Ġnotes Ġhad Ġa Ġmedian Ġcount Ġof Ġ208 Ġ( Q 1 - Q 3 : Ġ120 Ġ- Ġ312 ) Ġwith Ġa Ġvocabulary Ġof Ġ128 65 Ġunique Ġtokens . </s> Table Ġdefines Ġeach Ġof Ġthe Ġconsidered Ġclinical Ġpatient Ġphen otypes . ĠTable Ġcounts Ġthe Ġoccurrences Ġof Ġthese Ġphen otypes Ġacross Ġpatient Ġnotes Ġand ĠFigure Ġcontains Ġthe Ġcorresponding Ġcorrelation Ġmatrix . ĠLastly , ĠTable Ġpresents Ġan Ġoverview Ġof Ġsome Ġdescriptive Ġstatistics Ġon Ġthe Ġpatient Ġnotes ' Ġlengths . </s> Methods </s> Cl inical Ġresearchers Ġteamed Ġwith Ġjunior Ġmedical Ġresidents Ġin Ġcollaboration Ġwith Ġmore Ġsenior Ġintensive Ġcare Ġphysicians Ġto Ġcarry Ġout Ġtext Ġannotation Ġover Ġthe Ġperiod Ġof Ġone Ġyear ĠB IB REF 13 . ĠOper ators Ġwere Ġgrouped Ġto Ġfacilitate Ġthe Ġannotation Ġof Ġnotes Ġin Ġduplicate , Ġallowing Ġfor Ġcases Ġof Ġdisagreement Ġbetween Ġoperators . ĠThe Ġoperators Ġwithin Ġeach Ġteam Ġwere Ġinstructed Ġto Ġwork Ġindependently Ġon Ġnote Ġannotation . ĠClinical Ġtexts Ġwere Ġannot ated Ġin Ġbatches Ġwhich Ġwere Ġtime - st amped Ġon Ġtheir Ġday Ġof Ġcreation , Ġwhen Ġboth Ġoperators Ġin Ġa Ġteam Ġcompleted Ġannotation Ġof Ġa Ġbatch , Ġa Ġnew Ġbatch Ġwas Ġcreated Ġand Ġtransferred Ġto Ġthem . </s> Two Ġgroups Ġ( group Ġ1 : Ġco - authors ĠET M Ġ& ĠJ TW ; Ġgroup Ġ2 : Ġco - authors ĠJ W Ġ& ĠJ F ) Ġof Ġtwo Ġoperator Ġpairs Ġof Ġone Ġclinical Ġresearcher Ġand Ġone Ġresident Ġphysician Ġ( who Ġhad Ġpreviously Ġtaken Ġthe ĠMC AT Â® ) Ġfirst Ġannot ated Ġnursing Ġnotes Ġand Ġthen Ġdischarge Ġsumm aries . ĠEveryone Ġwas Ġfirst Ġtrained Ġon Ġthe Ġhigh - context Ġphen otypes Ġto Ġlook Ġfor Ġas Ġwell Ġas Ġtheir Ġdefinitions Ġby Ġgoing Ġthrough Ġa Ġnumber Ġof Ġnotes Ġin Ġa Ġgroup . ĠA Ġtotal Ġof Ġ13 Ġphen otypes Ġwere Ġconsidered Ġfor Ġannotation , Ġand Ġthe Ġlabel ĠâĢ ľ uns ure âĢ Ŀ Ġwas Ġused Ġto Ġindicate Ġthat Ġan Ġoperator Ġwould Ġlike Ġto Ġseek Ġassistance Ġdetermining Ġthe Ġpresence Ġof Ġan Ġphenotype Ġfrom Ġa Ġmore Ġsenior Ġphysician . ĠAnnotations Ġfor Ġphen otypes Ġrequired Ġexplicit Ġtext Ġin Ġthe Ġnote Ġindicating Ġthe Ġphenotype , Ġbut Ġas Ġa Ġresult Ġof Ġthe Ġcomplexity Ġof Ġcertain Ġphen otypes Ġthere Ġwas Ġno Ġspecific Ġdictionary Ġof Ġterms , Ġor Ġorder Ġin Ġwhich Ġthe Ġterms Ġappeared , Ġrequired Ġfor Ġa Ġphenotype Ġto Ġbe Ġconsidered Ġpresent . </s> Lim itations </s> There Ġexist Ġa Ġfew Ġlimitations Ġto Ġthis Ġdatabase . ĠThese Ġdata Ġare Ġunique Ġto ĠBeth ĠIsrael ĠDe acon ess ĠMedical ĠCenter Ġ( B ID MC ), Ġand Ġmodels Ġresulting Ġfrom Ġthese Ġdata Ġmay Ġnot Ġgeneral ize Ġto Ġnotes Ġgenerated Ġat Ġother Ġhospitals . ĠAd missions Ġto Ġhospitals Ġnot Ġassociated Ġwith ĠB ID MC Ġwill Ġnot Ġhave Ġbeen Ġcaptured , Ġand Ġgeneral iz ability Ġis Ġlimited Ġdue Ġto Ġthe Ġlimited Ġgeographic Ġdistribution Ġof Ġpatients Ġwhich Ġpresent Ġto Ġthe Ġhospital . </s> We Ġwelcome Ġopportunities Ġto Ġcontinue Ġto Ġexpand Ġthis Ġdataset Ġwith Ġadditional Ġphen otypes Ġsought Ġin Ġthe Ġun struct ured Ġtext , Ġpatient Ġsubs ets , Ġand Ġtext Ġoriginating Ġfrom Ġdifferent Ġsources , Ġwith Ġthe Ġgoal Ġof Ġexpanding Ġthe Ġutility Ġof ĠN LP Ġmethods Ġto Ġfurther Ġstructure Ġpatient Ġnote Ġtext Ġfor Ġretrospective Ġanalyses . </s> Technical ĠVal idation </s> All Ġstatistics Ġand Ġtab ulations Ġwere Ġgenerated Ġand Ġperformed Ġwith ĠR ĠStatistical ĠSoftware Ġversion Ġ3 . 5 . 2 . ĠB IB REF 14 ĠCohen 's ĠKappa ĠB IB REF 15 Ġwas Ġcalculated Ġfor Ġeach Ġphenotype Ġand Ġpair Ġof Ġannot ators Ġfor Ġwhich Ġprecisely Ġtwo Ġnote Ġannotations Ġwere Ġrecorded . ĠTable Ġsummarizes Ġthe Ġcalculated ĠCohen 's ĠKappa Ġcoefficients . </s> Usage ĠNotes </s> As Ġthis Ġcorpus Ġof Ġannot ated Ġpatient Ġnotes Ġcomprises Ġoriginal Ġhealthcare Ġdata Ġwhich Ġcontains Ġprotected Ġhealth Ġinformation Ġ( PH I ) Ġper ĠThe ĠHealth ĠInformation ĠPort ability Ġand ĠAccountability ĠAct Ġof Ġ1996 Ġ( H IP AA ) ĠB IB REF 16 Ġand Ġcan Ġbe Ġjoined Ġto Ġthe ĠM IM IC - III Ġdatabase , Ġindividuals Ġwho Ġwish Ġto Ġaccess Ġto Ġthe Ġdata Ġmust Ġsatisfy Ġall Ġrequirements Ġto Ġaccess Ġthe Ġdata Ġcontained Ġwithin ĠM IM IC - III . ĠTo Ġsatisfy Ġthese Ġconditions , Ġan Ġindividual Ġwho Ġwishes Ġto Ġaccess Ġthe Ġdatabase Ġmust Ġtake Ġa ĠâĢ ľ Data Ġor ĠSpec imens ĠManagement âĢ Ŀ Ġcourse , Ġas Ġwell Ġas Ġsign Ġa Ġuser Ġagreement , Ġas Ġoutlined Ġon Ġthe ĠM IM IC - III Ġdatabase Ġwebpage , Ġwhere Ġthe Ġlatest Ġversion Ġof Ġthis Ġdatabase Ġwill Ġbe Ġhosted Ġas ĠâĢ ľ An not ated ĠClinical ĠText s Ġfrom ĠM IM IC âĢ Ŀ ĠB IB REF 17 . ĠThis Ġcorpus Ġcan Ġalso Ġbe Ġaccessed Ġon ĠGitHub Ġafter Ġcompleting Ġall Ġof Ġthe Ġabove Ġrequirements . </s> Bas elines </s> In Ġthe Ġsection , Ġwe Ġpresent Ġthe Ġperformance Ġof Ġtwo Ġwell - established Ġbaseline Ġmodels Ġto Ġautomatically Ġinfer Ġthe Ġphenotype Ġbased Ġon Ġthe Ġpatient Ġnote , Ġwhich Ġwe Ġapproach Ġas Ġa Ġmulti - label , Ġmulti - class Ġtext Ġclassification Ġtask ĠB IB REF 18 . ĠEach Ġof Ġthe Ġbaseline Ġmodel Ġis Ġa Ġbinary Ġclass ifier Ġindicating Ġwhether Ġa Ġgiven Ġphenotype Ġis Ġpresent Ġin Ġthe Ġinput Ġpatient Ġnote . ĠAs Ġa Ġresult , Ġwe Ġtrain Ġa Ġseparate Ġmodel Ġfor Ġeach Ġphenotype . </s> Bas elines Ġ: :: ĠBag Ġof ĠWords Ġ+ ĠLog istic ĠReg ression </s> We Ġconvert Ġeach Ġpatient Ġnote Ġinto Ġa Ġbag Ġof Ġwords , Ġand Ġgive Ġas Ġinput Ġto Ġa Ġlog istic Ġregression . </s> Bas elines Ġ: :: ĠConv olution al ĠNeural ĠNetwork Ġ( CNN ) </s> We Ġfollow Ġthe ĠCNN Ġarchitecture Ġproposed Ġby Ġcoll ober t 2011 natural Ġand Ġk im 2014 conv olution al . ĠWe Ġuse Ġthe Ġconv olution Ġwidth s Ġfrom Ġ1 Ġto Ġ4 , Ġand Ġfor Ġeach Ġconv olution Ġwidth Ġwe Ġset Ġthe Ġnumber Ġof Ġfilters Ġto Ġ100 . ĠWe Ġuse Ġdrop out Ġwith Ġa Ġprobability Ġof Ġ$ 0 . 5 $ Ġto Ġreduce Ġover fitting ĠB IB REF 19 . ĠThe Ġtrain able Ġparameters Ġwere Ġinitialized Ġusing Ġa Ġuniform Ġdistribution Ġfrom Ġ$ - 0 . 05 $ Ġto Ġ$ 0 . 05 $. ĠThe Ġmodel Ġwas Ġoptimized Ġwith Ġad ad elta ĠB IB REF 20 . ĠWe Ġuse Ġword 2 vec ĠB IB REF 21 Ġas Ġthe Ġword Ġembed d ings , Ġwhich Ġwe Ġpret rain Ġon Ġall Ġthe Ġnotes Ġof ĠM IM IC ĠIII Ġv 3 . </s> Table Ġpresents Ġthe Ġperformance Ġof Ġthe Ġtwo Ġbaseline Ġmodels Ġ( F 1 - score ). </s> Conclusion </s> In Ġthis Ġpaper Ġwe Ġhave Ġpresented Ġa Ġnew Ġdataset Ġcontaining Ġdischarge Ġsumm aries Ġand Ġnursing Ġprogress Ġnotes , Ġfocusing Ġon Ġfrequently Ġread mitted Ġpatients Ġand Ġhigh - context Ġsocial Ġdetermin ants Ġof Ġhealth , Ġand Ġoriginating Ġfrom Ġa Ġlarge Ġtert iary Ġcare Ġhospital . ĠEach Ġpatient Ġnote Ġwas Ġannot ated Ġby Ġat Ġleast Ġone Ġclinical Ġresearcher Ġand Ġone Ġresident Ġphysician Ġfor Ġ13 Ġhigh - context Ġpatient Ġphen otypes . </s> P hen otype Ġdefinitions , Ġdataset Ġdistribution , Ġpatient Ġnote Ġstatistics , Ġinter - operator Ġerror , Ġand Ġthe Ġresults Ġof Ġbaseline Ġmodels Ġwere Ġreported Ġto Ġdemonstrate Ġthat Ġthe Ġdataset Ġis Ġwell - su ited Ġfor Ġthe Ġdevelopment Ġof Ġboth Ġrule - based Ġand Ġstatistical Ġmodels Ġfor Ġpatient Ġphen otyp ing . ĠWe Ġhope Ġthat Ġthe Ġrelease Ġof Ġthis Ġdataset Ġwill Ġaccelerate Ġthe Ġdevelopment Ġof Ġalgorithms Ġfor Ġpatient Ġphen otyp ing , Ġwhich Ġin Ġturn Ġwould Ġsignificantly Ġhelp Ġmedical Ġresearch Ġprogress Ġfaster . </s> Acknowled gements </s> The Ġauthors Ġwould Ġlike Ġto Ġacknowledge ĠKai - ou ĠTang Ġand ĠWilliam ĠLab ad ie - M ose ley Ġfor Ġassistance Ġin Ġthe Ġdevelopment Ġof Ġa Ġgraphical Ġuser Ġinterface Ġfor Ġtext Ġannotation . ĠWe Ġwould Ġalso Ġlike Ġto Ġthank ĠPhilips ĠHealthcare , ĠThe ĠLaboratory Ġof ĠComput ational ĠPhys iology Ġat ĠThe ĠMassachusetts ĠInstitute Ġof ĠTechnology , Ġand Ġstaff Ġat Ġthe ĠBeth ĠIsrael ĠDe acon ess ĠMedical ĠCenter , ĠBoston , Ġfor Ġsupporting Ġthe ĠM IM IC - III Ġdatabase , Ġfrom Ġwhich Ġthese Ġdata Ġwere Ġderived .
Introduction and Related Work With the widespread adoption of electronic health records (EHRs), medical data are being generated and stored digitally in vast quantities BIBREF0. While much EHR data are structured and amenable to analysis, there appears to be limited homogeneity in data completeness and quality BIBREF1, and it is estimated that the majority of healthcare data are being generated in unstructured, text-based format BIBREF2. The generation and storage of these unstructured data come concurrently with policy initiatives that seek to utilize preventative measures to reduce hospital admission and readmission BIBREF3. Chronic illnesses, behavioral factors, and social determinants of health are known to be associated with higher risks of hospital readmission, BIBREF4 and though behavioral factors and social determinants of health are often determined at the point of care, their identification may not always be curated in structured format within the EHR in the same manner that other factors associated with routine patient history taking and physical examination are BIBREF5. Identifying these patient attributes within EHRs in a reliable manner has the potential to reveal actionable associations which otherwise may remain poorly defined. As EHRs act to streamline the healthcare administration process, much of the data collected and stored in structured format may be those data most relevant to reimbursement and billing, and may not necessarily be those data which were most relevant during the clinical encounter. For example, a diabetic patient who does not adhere to an insulin treatment regimen and who thereafter presents to the hospital with symptoms indicating diabetic ketoacidosis (DKA) will be treated and considered administratively as an individual presenting with DKA, though that medical emergency may have been secondary to non-adherence to the initial treatment regimen in the setting of diabetes. In this instance, any retrospective study analyzing only the structured data from many similarly selected clinical encounters will necessarily then underestimate the effect of treatment non-adherence with respect to hospital admissions. While this form of high context information may not be found in the structured EHR data, it may be accessible in patient notes, including nursing progress notes and discharge summaries, particularly through the utilization of natural language processing (NLP) technologies. BIBREF6, BIBREF7 Given progress in NLP methods, we sought to address the issue of unstructured clinical text by defining and annotating clinical phenotypes in text which may otherwise be prohibitively difficult to discern in the structured data associated with the text entry. For this task, we chose the notes present in the publicly available MIMIC database BIBREF8. Given the MIMIC database as substrate and the aforementioned policy initiatives to reduce unnecessary hospital readmissions, as well as the goal of providing structure to text, we elected to focus on patients who were frequently readmitted to the ICU BIBREF9. In particular, a patient who is admitted to the ICU more than three times in a single year. By defining our cohort in this way we sought to ensure we were able to capture those characteristics unique to the cohort in a manner which may yield actionable intelligence on interventions to assist this patient population. Data Characteristics We have created a dataset of discharge summaries and nursing notes, all in the English language, with a focus on frequently readmitted patients, labeled with 15 clinical patient phenotypes believed to be associated with risk of recurrent Intensive Care Unit (ICU) readmission per our domain experts (co-authors LAC, PAT, DAG) as well as the literature. BIBREF10 BIBREF11 BIBREF12 Each entry in this database of consists of a Subject Identifier (integer), a Hospital Admission Identifier (integer), Category (string), Text (string), 15 Phenotypes (binary) including “None” and “Unsure”, Batch Date (string), and Operators (string). These variables are sufficient to use the data set alone, or to join it to the MIMIC-III database by Subject Identifier or Hospital Admission Identifier for additional patient-level or admission-level data, respectively. The MIMIC database BIBREF8 was utilized to extract Subject Identifiers, Hospital Admission Identifiers, and Note Text. Annotated discharge summaries had a median token count of 1417.50 (Q1-Q3: 1046.75 - 1926.00) with a vocabulary of 26454 unique tokens, while nursing notes had a median count of 208 (Q1-Q3: 120 - 312) with a vocabulary of 12865 unique tokens. Table defines each of the considered clinical patient phenotypes. Table counts the occurrences of these phenotypes across patient notes and Figure contains the corresponding correlation matrix. Lastly, Table presents an overview of some descriptive statistics on the patient notes' lengths. Methods Clinical researchers teamed with junior medical residents in collaboration with more senior intensive care physicians to carry out text annotation over the period of one year BIBREF13. Operators were grouped to facilitate the annotation of notes in duplicate, allowing for cases of disagreement between operators. The operators within each team were instructed to work independently on note annotation. Clinical texts were annotated in batches which were time-stamped on their day of creation, when both operators in a team completed annotation of a batch, a new batch was created and transferred to them. Two groups (group 1: co-authors ETM & JTW; group 2: co-authors JW & JF) of two operator pairs of one clinical researcher and one resident physician (who had previously taken the MCAT®) first annotated nursing notes and then discharge summaries. Everyone was first trained on the high-context phenotypes to look for as well as their definitions by going through a number of notes in a group. A total of 13 phenotypes were considered for annotation, and the label “unsure” was used to indicate that an operator would like to seek assistance determining the presence of an phenotype from a more senior physician. Annotations for phenotypes required explicit text in the note indicating the phenotype, but as a result of the complexity of certain phenotypes there was no specific dictionary of terms, or order in which the terms appeared, required for a phenotype to be considered present. Limitations There exist a few limitations to this database. These data are unique to Beth Israel Deaconess Medical Center (BIDMC), and models resulting from these data may not generalize to notes generated at other hospitals. Admissions to hospitals not associated with BIDMC will not have been captured, and generalizability is limited due to the limited geographic distribution of patients which present to the hospital. We welcome opportunities to continue to expand this dataset with additional phenotypes sought in the unstructured text, patient subsets, and text originating from different sources, with the goal of expanding the utility of NLP methods to further structure patient note text for retrospective analyses. Technical Validation All statistics and tabulations were generated and performed with R Statistical Software version 3.5.2. BIBREF14 Cohen's Kappa BIBREF15 was calculated for each phenotype and pair of annotators for which precisely two note annotations were recorded. Table summarizes the calculated Cohen's Kappa coefficients. Usage Notes As this corpus of annotated patient notes comprises original healthcare data which contains protected health information (PHI) per The Health Information Portability and Accountability Act of 1996 (HIPAA) BIBREF16 and can be joined to the MIMIC-III database, individuals who wish to access to the data must satisfy all requirements to access the data contained within MIMIC-III. To satisfy these conditions, an individual who wishes to access the database must take a “Data or Specimens Management” course, as well as sign a user agreement, as outlined on the MIMIC-III database webpage, where the latest version of this database will be hosted as “Annotated Clinical Texts from MIMIC” BIBREF17. This corpus can also be accessed on GitHub after completing all of the above requirements. Baselines In the section, we present the performance of two well-established baseline models to automatically infer the phenotype based on the patient note, which we approach as a multi-label, multi-class text classification task BIBREF18. Each of the baseline model is a binary classifier indicating whether a given phenotype is present in the input patient note. As a result, we train a separate model for each phenotype. Baselines ::: Bag of Words + Logistic Regression We convert each patient note into a bag of words, and give as input to a logistic regression. Baselines ::: Convolutional Neural Network (CNN) We follow the CNN architecture proposed by collobert2011natural and kim2014convolutional. We use the convolution widths from 1 to 4, and for each convolution width we set the number of filters to 100. We use dropout with a probability of $0.5$ to reduce overfitting BIBREF19. The trainable parameters were initialized using a uniform distribution from $-0.05$ to $0.05$. The model was optimized with adadelta BIBREF20. We use word2vec BIBREF21 as the word embeddings, which we pretrain on all the notes of MIMIC III v3. Table presents the performance of the two baseline models (F1-score). Conclusion In this paper we have presented a new dataset containing discharge summaries and nursing progress notes, focusing on frequently readmitted patients and high-context social determinants of health, and originating from a large tertiary care hospital. Each patient note was annotated by at least one clinical researcher and one resident physician for 13 high-context patient phenotypes. Phenotype definitions, dataset distribution, patient note statistics, inter-operator error, and the results of baseline models were reported to demonstrate that the dataset is well-suited for the development of both rule-based and statistical models for patient phenotyping. We hope that the release of this dataset will accelerate the development of algorithms for patient phenotyping, which in turn would significantly help medical research progress faster. Acknowledgements The authors would like to acknowledge Kai-ou Tang and William Labadie-Moseley for assistance in the development of a graphical user interface for text annotation. We would also like to thank Philips Healthcare, The Laboratory of Computational Physiology at The Massachusetts Institute of Technology, and staff at the Beth Israel Deaconess Medical Center, Boston, for supporting the MIMIC-III database, from which these data were derived.
Is this dataset publicly available for commercial use?
9658b5ffb5c56e5a48a3fea0342ad8fc99741908
Is Ġthis Ġdataset Ġpublicly Ġavailable Ġfor Ġcommercial Ġuse ?
No
<s> Is Ġthis Ġdataset Ġpublicly Ġavailable Ġfor Ġcommercial Ġuse ? </s> Introduction Ġand ĠRelated ĠWork </s> With Ġthe Ġwidespread Ġadoption Ġof Ġelectronic Ġhealth Ġrecords Ġ( E HR s ), Ġmedical Ġdata Ġare Ġbeing Ġgenerated Ġand Ġstored Ġdigitally Ġin Ġvast Ġquantities ĠB IB REF 0 . ĠWhile Ġmuch ĠE HR Ġdata Ġare Ġstructured Ġand Ġam enable Ġto Ġanalysis , Ġthere Ġappears Ġto Ġbe Ġlimited Ġhom ogeneity Ġin Ġdata Ġcomple teness Ġand Ġquality ĠB IB REF 1 , Ġand Ġit Ġis Ġestimated Ġthat Ġthe Ġmajority Ġof Ġhealthcare Ġdata Ġare Ġbeing Ġgenerated Ġin Ġun struct ured , Ġtext - based Ġformat ĠB IB REF 2 . ĠThe Ġgeneration Ġand Ġstorage Ġof Ġthese Ġun struct ured Ġdata Ġcome Ġconcurrently Ġwith Ġpolicy Ġinitiatives Ġthat Ġseek Ġto Ġutilize Ġprevent ative Ġmeasures Ġto Ġreduce Ġhospital Ġadmission Ġand Ġread mission ĠB IB REF 3 . </s> Ch ronic Ġillnesses , Ġbehavioral Ġfactors , Ġand Ġsocial Ġdetermin ants Ġof Ġhealth Ġare Ġknown Ġto Ġbe Ġassociated Ġwith Ġhigher Ġrisks Ġof Ġhospital Ġread mission , ĠB IB REF 4 Ġand Ġthough Ġbehavioral Ġfactors Ġand Ġsocial Ġdetermin ants Ġof Ġhealth Ġare Ġoften Ġdetermined Ġat Ġthe Ġpoint Ġof Ġcare , Ġtheir Ġidentification Ġmay Ġnot Ġalways Ġbe Ġcurated Ġin Ġstructured Ġformat Ġwithin Ġthe ĠE HR Ġin Ġthe Ġsame Ġmanner Ġthat Ġother Ġfactors Ġassociated Ġwith Ġroutine Ġpatient Ġhistory Ġtaking Ġand Ġphysical Ġexamination Ġare ĠB IB REF 5 . ĠIdent ifying Ġthese Ġpatient Ġattributes Ġwithin ĠE HR s Ġin Ġa Ġreliable Ġmanner Ġhas Ġthe Ġpotential Ġto Ġreveal Ġaction able Ġassociations Ġwhich Ġotherwise Ġmay Ġremain Ġpoorly Ġdefined . </s> As ĠE HR s Ġact Ġto Ġstream line Ġthe Ġhealthcare Ġadministration Ġprocess , Ġmuch Ġof Ġthe Ġdata Ġcollected Ġand Ġstored Ġin Ġstructured Ġformat Ġmay Ġbe Ġthose Ġdata Ġmost Ġrelevant Ġto Ġreimbursement Ġand Ġbilling , Ġand Ġmay Ġnot Ġnecessarily Ġbe Ġthose Ġdata Ġwhich Ġwere Ġmost Ġrelevant Ġduring Ġthe Ġclinical Ġencounter . ĠFor Ġexample , Ġa Ġdiabetic Ġpatient Ġwho Ġdoes Ġnot Ġadhere Ġto Ġan Ġinsulin Ġtreatment Ġregimen Ġand Ġwho Ġthereafter Ġpresents Ġto Ġthe Ġhospital Ġwith Ġsymptoms Ġindicating Ġdiabetic Ġket o acid osis Ġ( D KA ) Ġwill Ġbe Ġtreated Ġand Ġconsidered Ġadministr atively Ġas Ġan Ġindividual Ġpresenting Ġwith ĠD KA , Ġthough Ġthat Ġmedical Ġemergency Ġmay Ġhave Ġbeen Ġsecondary Ġto Ġnon - ad herence Ġto Ġthe Ġinitial Ġtreatment Ġregimen Ġin Ġthe Ġsetting Ġof Ġdiabetes . ĠIn Ġthis Ġinstance , Ġany Ġretrospective Ġstudy Ġanalyzing Ġonly Ġthe Ġstructured Ġdata Ġfrom Ġmany Ġsimilarly Ġselected Ġclinical Ġencounters Ġwill Ġnecessarily Ġthen Ġunderestimate Ġthe Ġeffect Ġof Ġtreatment Ġnon - ad herence Ġwith Ġrespect Ġto Ġhospital Ġadmissions . </s> While Ġthis Ġform Ġof Ġhigh Ġcontext Ġinformation Ġmay Ġnot Ġbe Ġfound Ġin Ġthe Ġstructured ĠE HR Ġdata , Ġit Ġmay Ġbe Ġaccessible Ġin Ġpatient Ġnotes , Ġincluding Ġnursing Ġprogress Ġnotes Ġand Ġdischarge Ġsumm aries , Ġparticularly Ġthrough Ġthe Ġutilization Ġof Ġnatural Ġlanguage Ġprocessing Ġ( N LP ) Ġtechnologies . ĠB IB REF 6 , ĠB IB REF 7 ĠGiven Ġprogress Ġin ĠN LP Ġmethods , Ġwe Ġsought Ġto Ġaddress Ġthe Ġissue Ġof Ġun struct ured Ġclinical Ġtext Ġby Ġdefining Ġand Ġannot ating Ġclinical Ġphen otypes Ġin Ġtext Ġwhich Ġmay Ġotherwise Ġbe Ġprohib itive ly Ġdifficult Ġto Ġdiscern Ġin Ġthe Ġstructured Ġdata Ġassociated Ġwith Ġthe Ġtext Ġentry . ĠFor Ġthis Ġtask , Ġwe Ġchose Ġthe Ġnotes Ġpresent Ġin Ġthe Ġpublicly Ġavailable ĠM IM IC Ġdatabase ĠB IB REF 8 . </s> Given Ġthe ĠM IM IC Ġdatabase Ġas Ġsubstrate Ġand Ġthe Ġaforementioned Ġpolicy Ġinitiatives Ġto Ġreduce Ġunnecessary Ġhospital Ġread missions , Ġas Ġwell Ġas Ġthe Ġgoal Ġof Ġproviding Ġstructure Ġto Ġtext , Ġwe Ġelected Ġto Ġfocus Ġon Ġpatients Ġwho Ġwere Ġfrequently Ġread mitted Ġto Ġthe ĠIC U ĠB IB REF 9 . ĠIn Ġparticular , Ġa Ġpatient Ġwho Ġis Ġadmitted Ġto Ġthe ĠIC U Ġmore Ġthan Ġthree Ġtimes Ġin Ġa Ġsingle Ġyear . ĠBy Ġdefining Ġour Ġcohort Ġin Ġthis Ġway Ġwe Ġsought Ġto Ġensure Ġwe Ġwere Ġable Ġto Ġcapture Ġthose Ġcharacteristics Ġunique Ġto Ġthe Ġcohort Ġin Ġa Ġmanner Ġwhich Ġmay Ġyield Ġaction able Ġintelligence Ġon Ġinterventions Ġto Ġassist Ġthis Ġpatient Ġpopulation . </s> Data ĠCharacter istics </s> We Ġhave Ġcreated Ġa Ġdataset Ġof Ġdischarge Ġsumm aries Ġand Ġnursing Ġnotes , Ġall Ġin Ġthe ĠEnglish Ġlanguage , Ġwith Ġa Ġfocus Ġon Ġfrequently Ġread mitted Ġpatients , Ġlabeled Ġwith Ġ15 Ġclinical Ġpatient Ġphen otypes Ġbelieved Ġto Ġbe Ġassociated Ġwith Ġrisk Ġof Ġrecurrent ĠInt ensive ĠCare ĠUnit Ġ( IC U ) Ġread mission Ġper Ġour Ġdomain Ġexperts Ġ( co - authors ĠL AC , ĠPAT , ĠD AG ) Ġas Ġwell Ġas Ġthe Ġliterature . ĠB IB REF 10 ĠB IB REF 11 ĠB IB REF 12 </s> Each Ġentry Ġin Ġthis Ġdatabase Ġof Ġconsists Ġof Ġa ĠSubject ĠIdent ifier Ġ( integer ), Ġa ĠHospital ĠAdmission ĠIdent ifier Ġ( integer ), ĠCategory Ġ( string ), ĠText Ġ( string ), Ġ15 ĠPhen otypes Ġ( binary ) Ġincluding ĠâĢ ľ None âĢ Ŀ Ġand ĠâĢ ľ Un sure âĢ Ŀ , ĠB atch ĠDate Ġ( string ), Ġand ĠOper ators Ġ( string ). ĠThese Ġvariables Ġare Ġsufficient Ġto Ġuse Ġthe Ġdata Ġset Ġalone , Ġor Ġto Ġjoin Ġit Ġto Ġthe ĠM IM IC - III Ġdatabase Ġby ĠSubject ĠIdent ifier Ġor ĠHospital ĠAdmission ĠIdent ifier Ġfor Ġadditional Ġpatient - level Ġor Ġadmission - level Ġdata , Ġrespectively . ĠThe ĠM IM IC Ġdatabase ĠB IB REF 8 Ġwas Ġutilized Ġto Ġextract ĠSubject ĠIdent ifiers , ĠHospital ĠAdmission ĠIdent ifiers , Ġand ĠNote ĠText . </s> An not ated Ġdischarge Ġsumm aries Ġhad Ġa Ġmedian Ġtoken Ġcount Ġof Ġ14 17 . 50 Ġ( Q 1 - Q 3 : Ġ10 46 . 75 Ġ- Ġ1926 . 00 ) Ġwith Ġa Ġvocabulary Ġof Ġ26 454 Ġunique Ġtokens , Ġwhile Ġnursing Ġnotes Ġhad Ġa Ġmedian Ġcount Ġof Ġ208 Ġ( Q 1 - Q 3 : Ġ120 Ġ- Ġ312 ) Ġwith Ġa Ġvocabulary Ġof Ġ128 65 Ġunique Ġtokens . </s> Table Ġdefines Ġeach Ġof Ġthe Ġconsidered Ġclinical Ġpatient Ġphen otypes . ĠTable Ġcounts Ġthe Ġoccurrences Ġof Ġthese Ġphen otypes Ġacross Ġpatient Ġnotes Ġand ĠFigure Ġcontains Ġthe Ġcorresponding Ġcorrelation Ġmatrix . ĠLastly , ĠTable Ġpresents Ġan Ġoverview Ġof Ġsome Ġdescriptive Ġstatistics Ġon Ġthe Ġpatient Ġnotes ' Ġlengths . </s> Methods </s> Cl inical Ġresearchers Ġteamed Ġwith Ġjunior Ġmedical Ġresidents Ġin Ġcollaboration Ġwith Ġmore Ġsenior Ġintensive Ġcare Ġphysicians Ġto Ġcarry Ġout Ġtext Ġannotation Ġover Ġthe Ġperiod Ġof Ġone Ġyear ĠB IB REF 13 . ĠOper ators Ġwere Ġgrouped Ġto Ġfacilitate Ġthe Ġannotation Ġof Ġnotes Ġin Ġduplicate , Ġallowing Ġfor Ġcases Ġof Ġdisagreement Ġbetween Ġoperators . ĠThe Ġoperators Ġwithin Ġeach Ġteam Ġwere Ġinstructed Ġto Ġwork Ġindependently Ġon Ġnote Ġannotation . ĠClinical Ġtexts Ġwere Ġannot ated Ġin Ġbatches Ġwhich Ġwere Ġtime - st amped Ġon Ġtheir Ġday Ġof Ġcreation , Ġwhen Ġboth Ġoperators Ġin Ġa Ġteam Ġcompleted Ġannotation Ġof Ġa Ġbatch , Ġa Ġnew Ġbatch Ġwas Ġcreated Ġand Ġtransferred Ġto Ġthem . </s> Two Ġgroups Ġ( group Ġ1 : Ġco - authors ĠET M Ġ& ĠJ TW ; Ġgroup Ġ2 : Ġco - authors ĠJ W Ġ& ĠJ F ) Ġof Ġtwo Ġoperator Ġpairs Ġof Ġone Ġclinical Ġresearcher Ġand Ġone Ġresident Ġphysician Ġ( who Ġhad Ġpreviously Ġtaken Ġthe ĠMC AT Â® ) Ġfirst Ġannot ated Ġnursing Ġnotes Ġand Ġthen Ġdischarge Ġsumm aries . ĠEveryone Ġwas Ġfirst Ġtrained Ġon Ġthe Ġhigh - context Ġphen otypes Ġto Ġlook Ġfor Ġas Ġwell Ġas Ġtheir Ġdefinitions Ġby Ġgoing Ġthrough Ġa Ġnumber Ġof Ġnotes Ġin Ġa Ġgroup . ĠA Ġtotal Ġof Ġ13 Ġphen otypes Ġwere Ġconsidered Ġfor Ġannotation , Ġand Ġthe Ġlabel ĠâĢ ľ uns ure âĢ Ŀ Ġwas Ġused Ġto Ġindicate Ġthat Ġan Ġoperator Ġwould Ġlike Ġto Ġseek Ġassistance Ġdetermining Ġthe Ġpresence Ġof Ġan Ġphenotype Ġfrom Ġa Ġmore Ġsenior Ġphysician . ĠAnnotations Ġfor Ġphen otypes Ġrequired Ġexplicit Ġtext Ġin Ġthe Ġnote Ġindicating Ġthe Ġphenotype , Ġbut Ġas Ġa Ġresult Ġof Ġthe Ġcomplexity Ġof Ġcertain Ġphen otypes Ġthere Ġwas Ġno Ġspecific Ġdictionary Ġof Ġterms , Ġor Ġorder Ġin Ġwhich Ġthe Ġterms Ġappeared , Ġrequired Ġfor Ġa Ġphenotype Ġto Ġbe Ġconsidered Ġpresent . </s> Lim itations </s> There Ġexist Ġa Ġfew Ġlimitations Ġto Ġthis Ġdatabase . ĠThese Ġdata Ġare Ġunique Ġto ĠBeth ĠIsrael ĠDe acon ess ĠMedical ĠCenter Ġ( B ID MC ), Ġand Ġmodels Ġresulting Ġfrom Ġthese Ġdata Ġmay Ġnot Ġgeneral ize Ġto Ġnotes Ġgenerated Ġat Ġother Ġhospitals . ĠAd missions Ġto Ġhospitals Ġnot Ġassociated Ġwith ĠB ID MC Ġwill Ġnot Ġhave Ġbeen Ġcaptured , Ġand Ġgeneral iz ability Ġis Ġlimited Ġdue Ġto Ġthe Ġlimited Ġgeographic Ġdistribution Ġof Ġpatients Ġwhich Ġpresent Ġto Ġthe Ġhospital . </s> We Ġwelcome Ġopportunities Ġto Ġcontinue Ġto Ġexpand Ġthis Ġdataset Ġwith Ġadditional Ġphen otypes Ġsought Ġin Ġthe Ġun struct ured Ġtext , Ġpatient Ġsubs ets , Ġand Ġtext Ġoriginating Ġfrom Ġdifferent Ġsources , Ġwith Ġthe Ġgoal Ġof Ġexpanding Ġthe Ġutility Ġof ĠN LP Ġmethods Ġto Ġfurther Ġstructure Ġpatient Ġnote Ġtext Ġfor Ġretrospective Ġanalyses . </s> Technical ĠVal idation </s> All Ġstatistics Ġand Ġtab ulations Ġwere Ġgenerated Ġand Ġperformed Ġwith ĠR ĠStatistical ĠSoftware Ġversion Ġ3 . 5 . 2 . ĠB IB REF 14 ĠCohen 's ĠKappa ĠB IB REF 15 Ġwas Ġcalculated Ġfor Ġeach Ġphenotype Ġand Ġpair Ġof Ġannot ators Ġfor Ġwhich Ġprecisely Ġtwo Ġnote Ġannotations Ġwere Ġrecorded . ĠTable Ġsummarizes Ġthe Ġcalculated ĠCohen 's ĠKappa Ġcoefficients . </s> Usage ĠNotes </s> As Ġthis Ġcorpus Ġof Ġannot ated Ġpatient Ġnotes Ġcomprises Ġoriginal Ġhealthcare Ġdata Ġwhich Ġcontains Ġprotected Ġhealth Ġinformation Ġ( PH I ) Ġper ĠThe ĠHealth ĠInformation ĠPort ability Ġand ĠAccountability ĠAct Ġof Ġ1996 Ġ( H IP AA ) ĠB IB REF 16 Ġand Ġcan Ġbe Ġjoined Ġto Ġthe ĠM IM IC - III Ġdatabase , Ġindividuals Ġwho Ġwish Ġto Ġaccess Ġto Ġthe Ġdata Ġmust Ġsatisfy Ġall Ġrequirements Ġto Ġaccess Ġthe Ġdata Ġcontained Ġwithin ĠM IM IC - III . ĠTo Ġsatisfy Ġthese Ġconditions , Ġan Ġindividual Ġwho Ġwishes Ġto Ġaccess Ġthe Ġdatabase Ġmust Ġtake Ġa ĠâĢ ľ Data Ġor ĠSpec imens ĠManagement âĢ Ŀ Ġcourse , Ġas Ġwell Ġas Ġsign Ġa Ġuser Ġagreement , Ġas Ġoutlined Ġon Ġthe ĠM IM IC - III Ġdatabase Ġwebpage , Ġwhere Ġthe Ġlatest Ġversion Ġof Ġthis Ġdatabase Ġwill Ġbe Ġhosted Ġas ĠâĢ ľ An not ated ĠClinical ĠText s Ġfrom ĠM IM IC âĢ Ŀ ĠB IB REF 17 . ĠThis Ġcorpus Ġcan Ġalso Ġbe Ġaccessed Ġon ĠGitHub Ġafter Ġcompleting Ġall Ġof Ġthe Ġabove Ġrequirements . </s> Bas elines </s> In Ġthe Ġsection , Ġwe Ġpresent Ġthe Ġperformance Ġof Ġtwo Ġwell - established Ġbaseline Ġmodels Ġto Ġautomatically Ġinfer Ġthe Ġphenotype Ġbased Ġon Ġthe Ġpatient Ġnote , Ġwhich Ġwe Ġapproach Ġas Ġa Ġmulti - label , Ġmulti - class Ġtext Ġclassification Ġtask ĠB IB REF 18 . ĠEach Ġof Ġthe Ġbaseline Ġmodel Ġis Ġa Ġbinary Ġclass ifier Ġindicating Ġwhether Ġa Ġgiven Ġphenotype Ġis Ġpresent Ġin Ġthe Ġinput Ġpatient Ġnote . ĠAs Ġa Ġresult , Ġwe Ġtrain Ġa Ġseparate Ġmodel Ġfor Ġeach Ġphenotype . </s> Bas elines Ġ: :: ĠBag Ġof ĠWords Ġ+ ĠLog istic ĠReg ression </s> We Ġconvert Ġeach Ġpatient Ġnote Ġinto Ġa Ġbag Ġof Ġwords , Ġand Ġgive Ġas Ġinput Ġto Ġa Ġlog istic Ġregression . </s> Bas elines Ġ: :: ĠConv olution al ĠNeural ĠNetwork Ġ( CNN ) </s> We Ġfollow Ġthe ĠCNN Ġarchitecture Ġproposed Ġby Ġcoll ober t 2011 natural Ġand Ġk im 2014 conv olution al . ĠWe Ġuse Ġthe Ġconv olution Ġwidth s Ġfrom Ġ1 Ġto Ġ4 , Ġand Ġfor Ġeach Ġconv olution Ġwidth Ġwe Ġset Ġthe Ġnumber Ġof Ġfilters Ġto Ġ100 . ĠWe Ġuse Ġdrop out Ġwith Ġa Ġprobability Ġof Ġ$ 0 . 5 $ Ġto Ġreduce Ġover fitting ĠB IB REF 19 . ĠThe Ġtrain able Ġparameters Ġwere Ġinitialized Ġusing Ġa Ġuniform Ġdistribution Ġfrom Ġ$ - 0 . 05 $ Ġto Ġ$ 0 . 05 $. ĠThe Ġmodel Ġwas Ġoptimized Ġwith Ġad ad elta ĠB IB REF 20 . ĠWe Ġuse Ġword 2 vec ĠB IB REF 21 Ġas Ġthe Ġword Ġembed d ings , Ġwhich Ġwe Ġpret rain Ġon Ġall Ġthe Ġnotes Ġof ĠM IM IC ĠIII Ġv 3 . </s> Table Ġpresents Ġthe Ġperformance Ġof Ġthe Ġtwo Ġbaseline Ġmodels Ġ( F 1 - score ). </s> Conclusion </s> In Ġthis Ġpaper Ġwe Ġhave Ġpresented Ġa Ġnew Ġdataset Ġcontaining Ġdischarge Ġsumm aries Ġand Ġnursing Ġprogress Ġnotes , Ġfocusing Ġon Ġfrequently Ġread mitted Ġpatients Ġand Ġhigh - context Ġsocial Ġdetermin ants Ġof Ġhealth , Ġand Ġoriginating Ġfrom Ġa Ġlarge Ġtert iary Ġcare Ġhospital . ĠEach Ġpatient Ġnote Ġwas Ġannot ated Ġby Ġat Ġleast Ġone Ġclinical Ġresearcher Ġand Ġone Ġresident Ġphysician Ġfor Ġ13 Ġhigh - context Ġpatient Ġphen otypes . </s> P hen otype Ġdefinitions , Ġdataset Ġdistribution , Ġpatient Ġnote Ġstatistics , Ġinter - operator Ġerror , Ġand Ġthe Ġresults Ġof Ġbaseline Ġmodels Ġwere Ġreported Ġto Ġdemonstrate Ġthat Ġthe Ġdataset Ġis Ġwell - su ited Ġfor Ġthe Ġdevelopment Ġof Ġboth Ġrule - based Ġand Ġstatistical Ġmodels Ġfor Ġpatient Ġphen otyp ing . ĠWe Ġhope Ġthat Ġthe Ġrelease Ġof Ġthis Ġdataset Ġwill Ġaccelerate Ġthe Ġdevelopment Ġof Ġalgorithms Ġfor Ġpatient Ġphen otyp ing , Ġwhich Ġin Ġturn Ġwould Ġsignificantly Ġhelp Ġmedical Ġresearch Ġprogress Ġfaster . </s> Acknowled gements </s> The Ġauthors Ġwould Ġlike Ġto Ġacknowledge ĠKai - ou ĠTang Ġand ĠWilliam ĠLab ad ie - M ose ley Ġfor Ġassistance Ġin Ġthe Ġdevelopment Ġof Ġa Ġgraphical Ġuser Ġinterface Ġfor Ġtext Ġannotation . ĠWe Ġwould Ġalso Ġlike Ġto Ġthank ĠPhilips ĠHealthcare , ĠThe ĠLaboratory Ġof ĠComput ational ĠPhys iology Ġat ĠThe ĠMassachusetts ĠInstitute Ġof ĠTechnology , Ġand Ġstaff Ġat Ġthe ĠBeth ĠIsrael ĠDe acon ess ĠMedical ĠCenter , ĠBoston , Ġfor Ġsupporting Ġthe ĠM IM IC - III Ġdatabase , Ġfrom Ġwhich Ġthese Ġdata Ġwere Ġderived .