Original Paper

Asthma Exacerbation Prediction and Interpretation based on Time-sensitive Attentive Neural Network: A Retrospective Cohort Study

1YangXiang,Ph.D.,Yang.Xiang@uth.tmc.edu

1,2HangyuJi,M.D.,jihangyuecho@163.com

1YujiaZhou,M.B.B.S.,Yujia.Zhou@uth.tmc.edu

1FangLi,Ph.D.,Fang.Li@uth.tmc.edu

1JingchengDu,Ph.D.,Jingcheng.Du@uth.tmc.edu

1LailaRasmy,M.S.,Laila.Rasmy.GindyBekhet@uth.tmc.edu

1StephenWu,Ph.D.,wu.stephen.t@gmail.com

1Wenjin.JimZheng,Ph.D.,Wenjin.J.Zheng@uth.tmc.edu

1HuaXu,Ph.D.,Hua.Xu@uth.tmc.edu

1DeguiZhi,Ph.D.,Degui.Zhi@uth.tmc.edu

1YaoyunZhang,Ph.D.,Yaoyun.Zhang@uth.tmc.edu

1CuiTao*,Ph.D.,Cui.Tao@uth.tmc.edu

1SchoolofBiomedicalInformatics,TheUniversityofTexasHealthScienceCenterat

Houston,Houston,TX,U.S.

2DivisionofGastroenterology,Guang'anmenHospital,ChinaAcademyofChinese

MedicalSciences,Beijing,China

*correspondingauthor

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NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Abstract Background:Asthmaexacerbationisanacuteorsub-acuteepisodeofprogressiveworseningofasthmasymptomsandcanhavesignificantimpactsonpatients’dailylife.In2016,12.4millioncurrentasthmatics(46.9%)intheU.S.hadatleastoneasthmaexacerbationinthepreviousyear.Objective:Theobjectivesofthisstudyweretopredicttheriskofasthmaexacerbationsandtoexplorepotentialriskfactorsinvolvedinprogressiveasthma.Methods:Weproposedatime-sensitiveattentiveneuralnetworktopredictasthmaexacerbationusingclinicalvariablesfromelectronichealthrecords(EHRs).TheclinicalvariableswerecollectedfromtheCernerHealthFacts®databasebetween1992and2015including31,433asthmaticadultpatients.Interpretationsonboththepatientlevelandthecohortlevelwereinvestigatedbasedonthemodelparameters.Results:TheproposedmodelobtainsanAUCvalueof0.7003through5-foldcross-validation,whichoutperformsthebaselinemethods.Theresultsalsodemonstratethattheadditionofelapsedtimeembeddingsconsiderablyimprovestheperformanceonthisdataset.Throughfurtheranalysis,itwaswitnessedthatriskfactorsbehaveddistinctlyalongthetimelineandacrosspatients.Wealsofoundsupportingevidencefrompeer-reviewedliteratureforsomepossiblecohort-levelriskfactorssuchasrespiratorysyndromesandesophagealreflux.Conclusions:Theproposedtime-sensitiveattentiveneuralnetworkissuperiortotraditionalmachinelearningmethodsandperformsbetterthanstate-of-the-artdeeplearningmethodsinrealizingeffectivepredictivemodelsforthepredictionofasthmaexacerbation.Webelievethattheinterpretationandvisualizationofriskfactorscanhelptheclinicalcommunitytobetterunderstandtheunderlyingmechanismsofthediseaseprogression.Keywords:asthmaexacerbation;predictivemodel;time-sensitive;elapsedtimeembedding;deeplearning;attentionmechanism

Introduction Asthmaisacommonandserioushealthproblemwhichaffects235millionpeopleworldwide[1]andanestimatedof26.5millionpeople(8.3%oftheU.S.population)intheU.S.[2].Asthmatakesasignificanttollonthepopulationwhichimposesanunacceptableburdenonhealthcaresystemswithatotalannualcostof$81.9billionin2013intheU.S.[3,4].Asthmamaydevelopintoexacerbationifitisnotwellcontrolledorstimulatedbyspecificriskfactors[3].In2016,12.4millioncurrentasthmatics(46.9%)intheU.S.hadatleastoneasthmaexacerbationinthepreviousyear[2].Exacerbationsofasthmacanbesevereandrequireimmediatemedicalinterventions,eitherasanemergencydepartmentvisitoranadmissiontohospital[5].Seriousasthmaexacerbationsmayevenresultindeath[6].Therefore,itisofpracticalsignificancetomakeearlypredictionssothatinterventionscanbecarriedoutinadvancetoreducetheprobabilityofexacerbation.

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Investigationsonpredictionandriskfactorrecognitionforasthmaexacerbationhavebeenrespectable,inwhichthemainstreamadoptstraditionalstatisticalmethods,suchaslogisticregression[7,8],proportional-hazardsregression[9],andgeneralizedlinearmixedmodels[10].However,mostofthemhaveonlyexploredasmallgroupofcandidateriskfactors,areusuallyhardtoextendtootherdatasetsandhardtomakepersonalizedpredictions.Withtheexplosionofhealthcaredatainrecentyears,machinelearningmethodshavegrowninprominenceforthisdomain,duetotheirsuperiorityoverstatisticmethodsinprocessinglargernumbersofvariablesandcapacityinminingmorepossiblecorrelationsbetweenthem[11].TypicalmodelsincludeNaïveBayes[13],Bayesiannetworks[12–14],artificialneuralnetworks[12],GaussianProcess[12],andSupportVectorMachines[12,13].Althoughdifferentattemptshavebeenmade,therearestillseveraldeficienciesinapplyingthesetraditionalmachinelearningmethods.Forexample,ignoringtemporaldependenciesbetweenvariablesmightnotprovideameaningfulriskestimationoffutureexacerbationsforindividualpatients[15].Furthermore,mostapproachesonlyconcentrateontheperformancebutlackfurtherattentiontopersonalizedriskfactors.Recentpredictivemodeling-relatedstudiesfocusedmoreondeeplearning,whichhasanupperhandonhealthcarepredictionsbecauseofitsflexibilityindealingwithlongitudinaldata[16],powerfullearningcapabilities[17],andabilitytotackletheproblemofdatairregularity[18].Oneofthemostpopulararchitecturesofdeeplearning-basedpredictivemodelistherecurrentneuralnetworks(RNNs),whichtakeapatient’svisitsequenceastheinputandmakepredictionsaccordingtotheencodedrepresentations.Multiplesuccesseshavebeenachievedinapplyingdeeplearningondiseaseprediction[19],mostlyusingvariantsofRNNswithdistinctnetworkcomponents,suchasattentionmechanismforevaluatingweightsofeachvariable[20–22],andspecialconfigurationsintacklingtheproblemoftimedecays[20,21,23,24].Typicalpredictiontasksincludethepredictionofdiabetesmellitus[18],Parkinson[18,25], chronicheartfailure[21],sepsis[26],mortalityandreadmission[20].Inspiredbypreviousstudies,weappliedLongShort-termMemory(LSTM),apopularRNNvariantusedbydozensofpreviousstudies[19,20,23,27],forasthmaexacerbationprediction.WeproposedtheTime-SensitiveAttentiveNeuralNetwork(TSANN),whichemploysaself-attentionmechanism[28]tohelpmodelthecontextofbothvisit-levelandcode-levelvariables.Meanwhile,toincorporatetheimpactofelapsedtime,weprojectedthevisittimeofeachclinicalvariableintoalow-dimensionalspaceandassignedanumericvectortoeachtime.MakinguseoftheattentionweightsofTSANN,dataanalysiswasthenconductedtoinvestigatepersonalizedandcohort-levelriskfactors.

Asfarasweknow,thisisthefirstdata-drivenstudytopredictasthmaexacerbationusingdeeplearningandEHRdata,thefirstefforttointroduceelapsedtimeembeddingsintoclinicalpredictivemodeling,andthefirstattempttovisualizeriskfactorsofasthmaexacerbationonboththeindividuallevelandthecohortlevel,whichhavebeeninsufficientlyexploredinmostpreviousstudies.Webelievethat

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theoutcomesofthisstudycanhelptheclinicalcommunitytobetterunderstandtheunderlyingmechanismsofthediseaseprogressionandtoassistindecision-making.Althoughfocusingonasthmaexacerbationforthisspecificproject,theproposedapproachcanalsobeadoptedinriskpredictionforotherchronicdiseases.

Methods

Database ThisstudyusedCernerHealthFacts®,aHIPAA-compliantdatabasecollectedfrommultipleenrolledclinicalfacilities,containingmostlyin-patientdata.DatainHealthFactswereextracteddirectlyfromtheEHRsfromhospitalswithwhichCernerhasadatauseagreement.Encountersmayincludethepharmacy,clinicalandmicrobiologylaboratory,admission,andbillinginformationfromaffiliatedpatientcarelocations.Allpersonalidentifyinginformationofthepatientswasanonymized.Inourstudy,weprimarilyfocusedontheimpactofclinicalfactorsonasthmaexacerbation,soweextracteddiagnosis,medications,anddemographiccharacteristicssuchasgender,race,andagefromthedatabase.TheUniversityofTexasHealthScienceCenter(UTHealth)hadagreementswithCernertousethisdataforresearchpurposes.TheinstitutionalreviewboardatUTHealthapprovedthestudyprotocol.

Study Design Weconductedaretrospectivestudytopredicttheriskofasthmaexacerbation.Weextractedpatients’recordsbetween1992and2015fromtheCernerdatabase.Forclarity,wedefineseveraltermsinadvance(Table1).Table 1. Defined terms for asthma exacerbation prediction. Term Definition index date the date of the first diagnosis of asthma in a patient’s EHR exacerbation date the date of the first diagnosis of asthma exacerbation after the

index date case group patients with asthma and later asthma exacerbations within 365

days and satisfy the inclusion and exclusion criteria (see Multimedia Appendix 1 for more details)

control group patients with asthma but without exacerbations within 365 days and satisfy the inclusion and exclusion criteria (see Multimedia Appendix 1 for more details)

prediction date training set: for the case group, the visit date before the exacerbation date; for the control group, the penultimate visit date within 365 days. testing set A: the 5th visit starting from the index date testing set B: defined analogous to the training set (following [21])

observed time window

the time window between the index date and the prediction date

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Intuitively,atestingsampleshouldbedefinedinasimilarwayasthatforthetrainingsamples(astestingsetB).However,sincewecannotforeseewhentheexacerbationwouldhappeninreal-worlddeployment,wecanonlymakefuturepredictionsateachvisit.Inourstudy,weselectedthe5thvisitfromasthmaindexasthepredictiondate(testingsetA),consideringbothleveragingmorevisitsandkeepingmorepatientsforexperiments(seeMultimediaAppendix1formoredetails).WealsodefinedtestingsetB:thepenultimatevisitasthepredictiondate,behavingastheupperboundoftheclassifierperformance,sinceitenablesmorecompletevisitinformation.TheTSANNmodelwastrainedtopredicttheonsetofasthmaexacerbationgiventheobservedtimewindow.Themainoutcomesofthemethodare:(1)ascorethatmeasurestheriskofasthmaexacerbationforeachpatient;(2)visualizationoftheresultsincludingapersonalizedheatmapidentifyingtheimportanceofeachclinicalvariableintheobservedtimewindow,cohort-levelriskfactorsandtheirtemporaldistributionsamongpatients.Basedontheoutcomes,furtherdataminingorclinicaltrialscanbecarriedoutforvalidation.TheworkflowofthisresearchisshowninFigure1.

Figure1.Theworkflowofriskpredictionofasthmaexacerbation.

Selection of Study Subjects Thesubjectsinthestudywerepatientswithadiagnosisofasthma.Inclusionandexclusioncriteriaweredecidedbasedonpreviouswork[29,30]includingthediagnosisofasthmaandasthmaexacerbation.Thecurrentstudyonlyfocusedonadultpatientswithagebetween18and80.Intheend,31,433individualsremained,including2,262casesand29,171controls(≈1:13).ThecohortselectionprocessisshowninFigure2.MoredetailsforthecohortselectionareshownintheMultimediaAppendix1.

Time-sensitive Attention Neural Network

Model Overview TSANNacceptsthewholesequenceofclinicalvariablesintheobservedtimewindowasinputs,andoutputstheprobabilityofasthmaexacerbation(seeFigure

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3).ThearchitectureofTSANNisbasedonLSTMwiththeadditionofhierarchicalattentionandelapsedtimeembeddings.

Figure2.Thecohortselectionprocessforthestudyofasthmaexacerbation.

Foreachvisit,multipleclinicalvariablesareencodedintheinputlayerandaveragedthroughthecode-levelattentionmechanism.Theelapsedtimeembeddingisconcatenatedasthecomplementaryinformationtoindicatetherelativetimeintervalbetweeneachvisitdateandthepredictiondate.LSTMacceptsthesequenceofencodedvisitsasinputsandoutputsfurtherencodingsforeachvisit.Thevisit-levelattentionlayeristhenappliedontheoutputsofLSTMtosummarizeallthevisitsforeachpatient.Finally,byfeedingtheoutputofvisit-levelattentionintothesoftmaxfunction,aprobabilityindicatingtheriskofdiseaseonsetisgenerated.

Figure3.TheoverviewoftheTSANNmodelforasthmaexacerbationprediction.

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Input Theinputsofthemodelconsistoftwotypesoffeatures.OnetypeisclinicalvariablesincludingICDcodes,medications,anddemographicfeatures.TheICD-10codesareallconvertedintoICD-9basedonpredefinedmappings[31].Allthemedicationsarenormalizedtotheirgenericnames.Thedemographicfeaturesincludeage,gender,andrace,whichareonlytakenasinputsonthevisitofthepredictiondate.Usingaprojectionmatrix𝑊"#$% ∈ ℝ()×+) ,wemappedeachclinicalvariableintoaconceptembeddingvector:

𝐶-. = 𝑊"#$% ∙ 𝑥-. (1)

whereCijisthegeneratedconceptembeddingvectorand𝑥-. ∈ ℝ+) istheone-hotvectordenotingtheexistenceofeachvariable.Theotherfeaturetypeistimefeatures,whichindicatetheoccurrencetimeforeachclinicalvariable.Intuitively,variableswithdifferenttimestampswouldbehavedifferentlyinprediction.Forinstance,inmanycases,aclinicaleventthathappensseveraldaysagowouldplayamoreimportantrolethanonethathappenedseveralmonthsago.Meanwhile,duetothenatureofdatairregularityanddeficiencyofEHRs,successivevisitsalwayshavediversetimeintervals[23],whichmakesitindispensabletoconsiderthetimeelapsewhendoingpredictivemodeling.Inspiredbytheideaofpositionembeddingsinnaturallanguageprocessingwhichwereintroducedtomodelthepositionalinformationforeachwordine.g.relationclassification[32]andthetransformerstructureinneurallanguagemodeling[28,33,34],weintroducedelapsedtimeembeddingstorepresenttherelativetimegapforeachclinicalvariable.Specifically,takingthetimeofthepredictiondateT0asapivot,thetimefeatureofeachvariableistheabsolutedifferencebetweenitsoccurrencetimeTiandT0,i.e.T0-Ti.Sincetheobservedtimewindowhasanupperboundof365days,thevocabularysizeofthetimeembeddingswassettobe365.Weappliedamatrix𝑊2#$% ∈ ℝ(3×+3 toprojecteachtimevaluetoanm-dimensionvector.Unlikethecodeembeddings,elapsedtimeembeddingsarefedintothemodelafterthecode-levelattentionandassignedtoeachvisit.Theequationtogettheelapsedtimeembeddingforeachvisitisanalogoustothatforconceptembeddingswhere𝑡-. ∈ ℝ+3:

𝑇-. = 𝑊2#$% ∙ 𝑡-. (2)

Foreasierdescription,wedenoteeachvisitas andeachclinicalvariableineachvisitas ,whereTisthemaximumnumberofvisitsandMisthemaximumnumberofeventsineachvisit.

Code-level Attention Attentionisamechanismspecificallydesignedfordeepneuralnetworksthatactsasaninformationfilter,meanwhilehasthecapacityofalleviatinginformationlosswhendealingwithlongsequences.Itselectsimportantsequencespansbyassigningweightstodifferentelementsinasequence[35,36].Throughattention,eachvariableisassignedaweightsothatimportantvariableswouldhavelargerweightsthanthe

, {1,2,..., }iv i TÎ, {1,2,..., }ijv j MÎ

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others.Weadoptedtheattentionmechanismfrom[37]inwhichtheweightofeachvariableisgeneratedaccordingtothesequenceandacontextvector.Concretely,giventhesetofcodes intheithvisit,theencodedrepresentationofvicanbegeneratedby:

(3)

(4)

(5)

whereWvandbvaretheweightandbiasformatrixtransformation,uijistheattentionvectorforeachcodejinvi,uvisthecontextvectorforviandisupdatedduringtraining,andbijistheattentionweightfortheeventvijbasedonwhichwecangeneratethefinalweightforthisvariable.

Visit-level Attentive LSTM Layer Takingtheencodedrepresentationofeachvisitasinput,LSTMmodelsthesequentialinformationintheobservedtimewindowandgetsthesummarizationatthefinalstep(thepredictiondate).TheadvantageofLSTMsoverbasicRNNsisthattheycanalleviatethevanishinggradientproblem,andarethusabletoretain“memories”ofpriortimestamps[38,39].LSTMsareimplementedbyseveralmatrixmultiplicationsandnonlineartransformationsthataimtomimicthememorymechanismofhumanbrains,inwhichtheseoperationsarecalledgates,signifyingthatthenetworkcanselecteffectiveinformationandabandonuselessinformation.TheequationsofLSTMsarelistedasfollows:

(6)

(7)

(8)

(9)

(10)(11)

whereWsandbsareweightsandbiasesfordifferentgatesorcells(ft:forgetgate,it:inputgate,Ct:memorycell,ot:outputgate,ht:hiddencell),andσistheactivationfunctionsuchastanhorsigmoid.

Weappliedself-attentionagainonthevisit-leveltomeasuretheriskscoresforeachclinicalvariableineachvisit.ByassigningattentionweightstotheoutputsofLSTMfromeachstep,wecanweighteachvisitintheobservedtimewindow.

(12)

{ }, {1,2,..., }ijv j MÎ

tanh( )ij v ij vu W v b= +

exp( )exp( )

Tij v

ij Tik v

k

u uu u

b =å

i ij ijj

v vb= ×å

1( [ , ] )t f t t ff W h v bs -= × +

1( [ , ] )t t t t ti W h v bs -= × +~

1tanh( [ , ] )t c t t cC W h v b-= × +

1* *t t t t tC f C i C-= + !

1( [ , ] )t o t t oo W h v bs -= × +*tanh( )t t th o C=

tanh( )i p i pu W v b= +

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(13)

(14)

whereWpandbparetheweightandbiasformatrixtransformation,uiistheattentionvectorforeachvisitigivenvi,upisthecontextvector,andajistheattentionweightforvj.Thisprocesscanbeseenasasimulationtowardsthediagnosisprocedureofaclinicvisit,duringwhichaphysicianwouldlookbackintoapatient’sEHR,measuretheimpactsofeachhistoricalclinicaleventandmakeafinaldecision.

Output Thevisit-levelattentionlayercompressesalltheinformationintheobservedtimewindowintoafixedsizevector.Theoutputofattentionfurthergoesthroughafullyconnectedlayerwithanonlinearactivation.Asoftmaxfunctionisfinallyappliedtogeneratethepredictedprobabilityp

𝑝 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥(𝑊" ⋅ 𝑟? + 𝑏")(15)whererpstandsfortheoutputofvisit-levelattention-LSTM.Thevaluepisusedasthescorefortheriskofdevelopinganasthmaexacerbation.

Evaluation AUROC/AUC(AreaUndertheReceiverOperatingCurve)iswidelyusedasanevaluationmetricforpredictivemodelswhichreflectsabalancebetweensensitivityandspecificity[40].Accordingtothepredictedprobabilityp(between0and1)foreachinstance,theAUCvalueisgeneratedbysettingdifferentcut-offs.ThemethodslistedinTable2werecomparedinourexperiments.Table 2. The methods used for comparisons. Method Note LR-sparse A popular conventional machine learning algorithm[41], usually

behaves as a strong baseline in predictive modeling[42]. The input of LR-sparse for each sample is a fixed length feature vector, the length of which is the number of distinct variables (the vocabulary size) and the value of each dimension is the occurrence times of each variable. LR suffered from the data imbalance problem on this dataset, so we employed SMOTE[43] to do over-sampling and help reduce its impact.

LR-dense A simplified version of Multi-Layer Perceptron[44] with only one input layer and one softmax layer. In LR-dense, the representations of all the codes were averaged after being projected to the embedding space. The differences between LR-dense and LR-sparse are two-fold: a) the inputs of LR-dense is the average of code embeddings while the input of LR is a fixed length vector denoting the occurrence of each clinical variable; b) the input embeddings of LR-dense can be fine-tuned during training but the input of LR-sparse cannot.

exp( )exp( )

Ti p

i Tk p

l

u uu u

a =åp j j

jr va= ×å

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LSTM The basic LSTM algorithm, taking the sequence of the clinical variables as input ordered by time; the variables in each visit are averaged.

ALSTM Attention LSTM with one layer of LSTM and one layer of attention. TLSTM[23] The time-aware LSTM model, which is one of the state-of-the-art

predictive models. In TLSTM, the time gap is used to compute the information decay in the LSTM unit.

RETAIN[21] A two-layer attention model, which is another state-of-the-art model for disease onset prediction. In RETAIN, the time features are not embedded as vectors, but real values denoting the gaps from the first visit.

TSANN-I The proposed TSANN model but with the second attention layer removed, the prediction is based on the final state of LSTM.

TSANN-I-step Use the time encoding method from [34] on TSANN-I, in which although time was also encoded using a vector, it only showed the order of each visit, but not the actual elapsed time, which is insufficient in modeling the irregularity in EHRs.

TSANN-II A complete version of the proposed TSANN model. Forevaluation,wefirstlysplitthedataintoatrainingsetandaheld-outtestingsetwitharatioof8:2.Further,5-foldcross-validationwasperformedonthetrainingdatasetforparametertuning.Duringcross-validation,gridsearchwasappliedtotunethehyperparameters.Finally,thehyperparametersforourbestmodelTSANN-Iwerebatchsize=32,codeembeddingsdimension=100,timeembeddingsdimension=20,learningrate=0.001,l2penalty=0.0001foralllayers,Leaky_ReLU[45]astheactivationfunctionforLSTM,addingbatchnormalizationbeforesoftmax,andAdam[46]astheoptimizer.AmoredetailedparametertuningprocessisshownintheMultimediaAppendix1.CodesforRETAINandTLSTMwereprovidedbytherespectiveauthors,andallotherdeeplearningmodelswereimplementedwithTensorFlow[47]andtestedonNvidiaTeslaV100,QuadroP6000andTitanXPGPUs.

Results

AUC Values SincethetimeinformationisofcriticalimportanceinmodelingEHRdata,weconductedexperimentsonsituationsbothwithandwithoutit.WedidnotimplementLR-sparsewithtimeembeddingsassociatedwithdayasthetimeunitsinceitwouldhaveintroducedagreaternumberofvariables(i.e.12,390*365),whichwouldhavebeentoosparseanddifficultforcomputation.Instead,wereducedthevocabularyofthetimevariablebysettingmonthasthetimeunitandfinally148,680distinctclinicalvariablesweregenerated.ForTLSTM,weonlyconsideredawith-timeversionsinceitisdefinedasatime-awarevariantofLSTM.ForLR-dense,LSTM,ALSTM,TSANN-IandTSANN-II,weusedtheelapsedtimeembeddingsintroducedinthisstudy.TheAUCvaluesonthetestingsetforallthemethodsareshowinTable3.

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Table 3. AUC values by the proposed models compared with baselines. (+/- stands for the improvement of adding time info).

Method Without time With time +/-(%) LR-sparse 0.5685 0.5825 +1.4

LR-dense 0.6545 0.6753 +2.08

LSTM 0.6045 0.6567 +5.22

ALSTM 0.6346 0.6714 +3.68

TLSTM - 0.6548 -

RETAIN 0.6455 0.6882 +4.27

TSANN-I 0.6692 0.7003 +3.11

TSANN-I-step 0.6463 - -

TSANN-II 0.6827 0.6855 +0.28

*the optimal value for each column is marked in bold. Comparedwithdifferentrows,wenoticethatTSANN-IwithtimeinformationachievestheoptimalAUCvalue,improvingthestrongestbaseline(RETAIN)by1.21%.TSANN-IIgetscomparableperformancewithRETAIN.AlltheresultsshowthattheconventionalmachinelearningmethodLR-sparsebehavesworsethanthedeeplearningmethods.ItisnoticedthatLR-denseperformsbetteronbothwithandwithouttimethanLSTMandALSTM.TSANN-I-step,whichonlyusedtimeembeddingstodenotetherelativepositionofeachvisit,doesnotgetgoodresult.

Whencomparingtheresultswithandwithouttimeinformation,considerableimprovementswereobservedafteraddingtimeinformationonmostmethods,signifyingthattheproblemofdatairregularityisobviousinthestudiedproblemandthetimeinformationplaysanimportantroleinmodelingvisitsforpredictingasthmaexacerbation.Forexample,TSANN-Iintegratedthetimeintervalintodecayfunctionsandobtaineda3.11%improvement.SimilarcasescanalsobeobservedinothermodelsincludingRETAIN.Surprisingly,TSANN-II,whenintegratingtimeembeddings,didnotgetmuchimprovement.Inaddition,ifwithouttimeinformation,ourproposedmethodsalsoperformmuchbetterthanothers,showingthattheyhavestrengthsevenincasessuchasthelossoftemporalinformation.ConsiderableimprovementscanalsobeobservedontestingsetB(RETAIN:0.7761,TSANN-I:0.8202)aswellasthecontributionofaddingthetimeinformation.Andasexpected,thegeneralresultsweremuchbetterontestingsetB(seeMultimediaAppendix1).

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Personalized Heatmap Inourstudy,aheatmapconveystheinterpretationsandbehavesasavisualizationtoolinidentifyingthepersonalizedriskfactors.Aheatmapistoillustratehoweachcandidateriskfactorbehavesineachvisitintheprogressionofasthma.Eachgridintheheatmapiscoloredbasedontheattentionweightsderivedfromthemodel.Thedarkeranareais,themoreimportancetheclinicalvariablesignifies,andthemorepossibleitbehavesasariskfactor.Forexample,Figure4showsacasewherethesymptomsofhypoxemia,shortnessofbreathandwheezing(799.02,786.05and786.07inICD9),etc.arerecognizedaspossibleriskfactors.Apossibleexplanationmightbethepatient’sstatusofhypoxemiaworsenedtheconditionofasthma,followingsymptomsinbreath,andasthmaexacerbationwasthendiagnosed.

Figure4.Anexampleofheatmapwiththemostpossibleriskfactordenotedbytheclinicalvariables:hypoxemia(D_799.02),shortnessofbreath(D_786.05)andwheezing(D_786.07),etc.

Itishardtogetaclearoverviewofthediseaseprogressionsincewecanonlydependonstructureddatabutwithoutanyclinicalnotes.Asaresult,wecanhardlyconfirmthediscoveredfactorsarerealriskfactorsbutonlyknowthattheymightbeeitherpossiblefactorstriggeringexacerbationsorofhighassociationswiththeevent.However,ourmethodmaybehaveasanimportantcomplementindiseasepredictionandclinicaldecision.

Cohort-level Risk Factors Wealsoproposedamethodtodiscovercommonriskfactorsonthepopulationlevelsothatclinicianscanhaveabetterunderstandingtowardsthediseaseprogressionwhilepatientscanpaymoreattentiontoriskfactorsindailylives.Therecognizedtop-rankedriskfactorsareshowninTable4.ThedetailsofthemethodandclinicaldiscussiontothefactorsaredescribedinMultimediaAppendix1.

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Table 4. Clinical variables with the top-ranked weights (/N stands for the variable occurred in N months prior to the prediction date). We regard both * and ▲ containing valuable information.

ICD-9 (Diagnosis) Medication 1 493.9x/0-5 (asthma)* (meaning diagnosed with

asthma multiple times before exacerbation) methylprednisolone/0,1**

2 786.07/0-2 (wheezing)△ prednisone/0,1,2** 3 496.0/0,1 (chronic airway obstruction not

elsewhere classified)▲ ipratropium/0,1,2**

4 530.81/0 (esophageal reflux)* midazolam/0,1,2△ 5 V46.2/0 (dependence on supplemental

oxygen)△ hydromorphone/0-2▲

6 787.02/0 (nausea alone)△ heparin/0,1△ 7 786.50/0 (unspecified chest pain)△ acetaminophen-oxycodone/0* 8 V08/042/0 (HIV related)▲ fentanyl/0▲ 9 786.59/0 (other chest pain)△ methylprednisolone/2-4** 10 786.05/0 (shortness of breath)△ glycopyrrolate/0* 11 V58.69/0 (long-term (current) use of other

medications)▲ lidocaine/0△

12 784.0/0 (headache)▲ dexamethasone/0△ 13 346.90/0 (migraine, unspecified, without

mention of intractable migraine without mention of status migrainosus)▲

promethazine/0△

14 V58.66/0 (long-term (current) use of aspirin)* atorvastatin/0△ 15 491.21/0 (obstructive chronic bronchitis with

(acute) exacerbation)▲ furosemide/0**

*Possible risk factors of asthma exacerbations △These factors were comorbidities or combined medications. We believe they were not risk factors of asthma exacerbations. ▲It could hardly be determined whether these factors caused asthma exacerbations but they were with high associations. ** These medications can be used to treat asthma or control asthma symptoms. In the study, it could hardly be determined whether these medications are risk factors since we were unable to investigate the dosage of these medication in the current study. Inappropriate medications use Short-Acting Beta Agonists (SABA)/Inhaled Corticosteroids (ICS) could also lead to asthma exacerbations though.

Discussion

Principle Results OurproposedmethodobtainstheoptimalAUCvalueonthepredictiontask,withhierarchicalattentionandelapsedtimeembeddingsasitsbooster.Thevisualizationpartalsoprovidesusefultracksforbetterunderstandingthediseaseprogression.

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RegardingtheAUCvalues,sinceweonlyselected5visitsastheobservedtimewindow,LSTMandALSTMmaynotbeaspowerfulastheywereinmodelinglongersequences,whichcanbereflectedbytheunsatisfyingresultscomparedwithLR-dense.However,forTSANN,wheremorecomplexattentivestructureswereadded,theresultsgetcomparablewithorbetterthanLR-dense.TLSTM,althoughhasalsointegratedthetimedecayinformation,doesnotgetsatisfyingresults,perhapsduetotheimproperheuristicdecayingfunctionforthecurrentdataset.TSANN-Iand-IIobtainbetterresultsasRETAIN,signifyingtheeffectivenessofthemodelstructure.Besides,thehierarchicalattentionarchitecturemakesiteasierforfurtherinterpretations,e.g.thepersonalizedheatmap.LR-sparse,althoughhasbeentunedthoroughlyinourexperiment,stillbehavesworsethantheothers,whichmaypartlyduetoitsinsufficiencyinmodelingcomplexsequentialpatterns.AtypicalcharacteristicoftheEHRdataisirregularity,whichmeansthatclinicvisitsmayberandomlyandsparselydistributedalongthetimeline,andsometimesareevenmissing.Thus,thepredictivemodelisresponsibleofserializingthevisitsforeachpatientwiththeconsiderationoftimeelapsesbetweencontinuousvisits.ThecomparisonsbetweenresultswithandwithouttimeinformationinTable3demonstratetheeffectivenessofconsideringtimeelapsesonthisstudycohort.Itcanbeconcludedthatthepredictionofasthmaexacerbationisquitetime-sensitive,andmostofthecriticalriskfactorsshouldhavebeentimestamped.Forinstance,evenforavisitjustinfrontofthepredictiondate,iftheoccurrencetimeofthisvisitislongbefore,itsimpactwouldstillbereduced.Similarcasecanalsobefoundin[23]withanimprovementof6%fromLSTMtoTLSTM.ForTSANN-I-step,althoughtimeembeddingswerealsoused,theywereonlyusedtodenotetherelativepositionofeachvisitinthesequencebutlacktheabilitytorepresenttimedecay,whichcanhardlygetgoodresultshere.AddingtimetoTSANN-IIdidnotgetmuchimprovementasthoseinothermethods,whichwethinkmightbeattributedtothattheadditionofthevisit-levelattentionweakensthecontributionofthetimeembeddings.

Apartfromthepersonalizedheatmapsandcohort-levelriskfactors,makinguseoftheweightsgeneratedbyEq.2andEq.3inMultimediaAppendix1,wecanalsovisualizehoweachclinicalvariablecontributesacrosstime,e.g.avariablemaybehavedistinctlyamongindividuals,withdifferentactiontimeordifferentincidences.Figure5-6showtwoexamplesinwhichthetimedistributionsfortheclinicalvariablesaredisplayedthroughscatters.Inthesescatters,eachcirclerepresentsapatientanditssizeandcolordepthdenotetheimportanceofthecorrespondingvariableforthepatient.Inthefigures,thex-axisstandsforthetimegapbetweentheoccurrencedateofthevariabletothepredictiondate,whilethey-axiswasemployedmerelyforcosmesis.Werandomlyselectamaximumof2,000patientstoplotthisfigure.Figure5-6arederivedfromanICDcode(esophagealreflux:530.81inICD9)andamedication(fentanyl)respectively.Weobservedifferenteffectivetimerangesforthesetwofactors,wherethefirstfactortendstodistributemorebetweentheprevious250to50dayswhilethesecondbetweentheprevious150days.Wehope

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thesevisualizationscanhelpfigureoutthedistributionsofmorepossibleriskfactorstoaidtheasthmacontrol.

Fig5.ThetimedistributionoftheclinicalvariableICD-9:530.81(gastro-esophagealreflux

disease)asariskfactor.

Fig6.Thetimedistributionoftheclinicalvariablemedication:fentanylasariskfactor.

Comparison with Prior Work Asfarasweknow,thisisthefirststudyindeeplearning-basedpredictiononasthmaexacerbation,andfromTable3,weobservedthatourproposedmethodoutperformsbothconventionalmachinelearninganddeeplearningmethods.Theperformanceboostsmainlycomefromthearchitectureofhierarchicalattentions,whichcanefficientlycaptureinformationfromdistinctmedicalelements,andthewayofencodingtimeusingelapsedtimeembeddings,whichenableslearningtemporalpatternsfromdifferentperspectives.Generally,deeplearning-based

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clinicalpredictivemodelingappliedRNNstylestructures,withminormodificationsinstructures.Forexample,[20]usedaone-layerRNNand[21]usedatwo-layerRNNwiththeirattentionweightsbutoneattentionisforevaluatingeachembeddingdimension.Incomparison,weappliedattentionweightsonboththecodeandvisitlevel,whichisalsoeasyfortheinterpretationofresults.

StrategiesofencodingtimeinpreviousstudiescanbegenerallycategorizedintolearningasubspacedecompositionofthecellmemoryinRNNstoenabletimedecay[20,23]ortakingthetimevaluesasfeatures[21,48].Thesemethods,sinceonlyusedtimeasasinglevalue,limitedtherepresentationabilityoftimeifmultiplepossiblepatternsexistinthetimeline,e.g.aclinicaleventchappenedintimetmightbemodeledjointlyincausal-likepatternssuchasat1->ctandct->bt2,inwhichtmaybehavedifferently.

Limitations and Future Work Byusingdeeplearning,weofferedanovelwayofidentifyingpossibleriskfactorsandpredictingtheriskofasthmaexacerbation.However,thecurrentworkstillhassomelimitations.First,forthemodelinterpretationpart,howmultipleclinicalvariablesinteractwitheachotherneedsfurtherexploration,simplyconsideringeachvariableindependentlymaylossthedependencypatternsbetweenthem,e.g.theprescriptionofadrugmightbecloselyassociatedwithadiseaseorsymptom.Secondly,EHRshavetheirowndrawbackssuchasdatairregularity,sparsity,andnoise.SomepotentialriskfactorsofasthmaexacerbationsmightnotberecordedinEHRs.Asaresult,theinformationintegritycannotbewellguaranteed.Wemayneedtofindwaystomakethedatamorecompletesuchasincludinginformationfromtextualreportsorpatientsurveys.Finally,theperformanceofthemodelstillhasroomtoimprove.Itmightbeboostedfurtherbydesigningmorepowerfulstructuresorincludingbackgroundknowledge.

Conclusion Inthispaper,weproposedanattentivedeeplearningnetworkforasthmaexacerbationpredictionandemployedelapsedtimeembeddingstomodelthetimedecays.Byleveragingtheweightsofthemodel,wenotonlygeneratedpersonalizedheatmapsandspecificriskscoresattheindividual-level,butalsoidentifiedpossibleriskfactorsofasthmaexacerbationatthecohort-level.Comparedwithpreviousstudies,ourmodeliseffectiveinmodelingtimeinformationandobtainsbetteroverallAUCs.Sincethemodeliscompletelydata-drivenandrelieslittleonfeatureengineering,itcaneasilybegeneralizedtootherpredictiontasks.Toourbestknowledge,thisisthefirststudytopredictasthmaexacerbationrisksusingadeeplearningmodelandincludeselaspsedtimeembeddings.Someofthetop-rankedriskfactorsidentifiedhavegainedsupportingevidencefrompreviousmedicalresearches,whichprovedourmethodhasgoodreliabilityandaccuracy.

Acknowledgements CTconceivedtheresearchproject.YX,HJandCTdesignedthepipelineandmethod.YXimplementedthedeeplearningmodelofthestudyandpreparedthemanuscript.HJcompletedtheclinicalpartofthemanuscript.WJZandHXprovidedvaluable

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suggestionsonthecohortselectionandexperimentdesign.YZhouandYZhangextracted,cleanedthedataanddidstatistics.LRhelpedreorganizethedataanddidnormalizationsfortherevisedversion.FL,JD,SW,DZandCTproofreadthepaperandprovidedvaluablesuggestions.Alltheauthorshavereadandapprovedthefinalmanuscript.

WethankDr.IrmgardWillcocksonforproofreading.Also,thankstoCernerforprovidingthevaluableHealthFactsEMRdata.WegratefullyacknowledgethesupportofNVIDIACorporationwiththedonationoftheQuadroP6000andTITANXPGPUsusedforthisresearch.ThisresearchwaspartiallysupportedbytheNationalLibraryofMedicineoftheNationalInstitutesofHealthunderawardnumberR01LM011829,theNationalInstituteofAllergyandInfectiousDiseasesoftheNationalInstitutesofHealthunderawardnumber1R01AI130460,NationalCenterforAdvancingTranslationalSciencesoftheNationalInstitutesofHealthunderawardnumberU01TR02062,andtheCancerPreventionResearchInstituteofTexas(CPRIT)TrainingGrant#RP160015.

Conflicts of Interest Nonedeclared.

Abbreviations ALSTM:AttentionLongShort-TermMemoryAUROC/AUC:AreaUndertheReceiverOperatingCurveEHRs:ElectronicHealthRecordsICS:InhaledCorticosteroidsLR:LogisticRegressionLSTM:LongShort-TermMemoryRETAIN:REverseTimeAttentIoNmodelRNNs:RecurrentNeuralNetworksSABA:Short-ActingBetaAgonistsSMOTE:SyntheticMinorityOver-samplingTechniqueTLSTM:Time-awareLongShort-TermMemoryTSANN:Time-SensitiveAttentiveNeuralNetwork

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