De Turck, Filip

Decruyenaere, Johan

Dhaene, Tom

Ongenae, Femke

Peiffer, Thomas

Ruyssinck, Joeri

Ghent University Academic Bibliography

Early detection of positive blood cultures using recurrent neuralnetworks on time series dataThomas Peiffer thomas.peiffer@ugent.beGhent University - iMinds, iGent - Technologiepark-Zwijnaarde 15, 9052, Ghent, BelgiumJoeri Ruyssinck joeri.ruyssinck@intec.ugent.beGhent University - iMinds, iGent - Technologiepark-Zwijnaarde 15, 9052, Ghent, BelgiumJohan Decruyenaere johan.decruyenaere@ugent.beGhent University Hospital, De Pintelaan 185, 9000, Ghent, BelgiumFilip De Turck, Femke Ongenae, Tom Dhaene firstname.lastname@intec.ugent.beGhent University - iMinds, iGent - Technologiepark-Zwijnaarde 15, 9052, Ghent, BelgiumAbstractThe presence of bacteria or fungi in thebloodstream of patients can lead to life-threatening conditions. A computationalmodel is developed to assist doctors in theintensive care unit (ICU) to predict whetherblood cultures of patients will return posi-tive. Our model is based on a Bi-directionalLong Short-Term Memory (BiLSTM) artifi-cial neural network. As input it uses ninemonitored clinical parameters, presented astime series data, collected from 2000 ICU ad-missions at the Ghent University Hospital.1. IntroductionA positive blood culture is defined as a blood samplewhere bacteria or fungi are present. This growth of or-ganisms in the blood stream can lead to inflammationthroughout the body or even organ failure or death(Morrell et al., 2005). When doctors suspect a patientto have a positive blood culture they can decide toadvance to a blood culture test. Symptoms indicativeof a likely positive culture are complex and not fullyunderstood. Nevertheless, it is suspected a link existsbetween a patients physiological data and the outcomeof such a test. This work presents research about howcomputational models can assist in detecting culturepositive patients or even suggest new relationships.Appearing in Proceedings of Benelearn 2016. Copyright2016 by the author(s)/owner(s).2. Data collection and processingA database was constructed with physiological infor-mation from 2177 patients admitted at the ICU of theGent University Hospital whereof 229 admissions hada positive blood culture test. For all other patients,a blood test was performed which returned negative.The dataset contains more than 14 million values asso-ciated with 29 parameters. Those variables can be sub-divided and reduced to 9 parameter groups includingblood pressure, temperature, resperatory rate, organhealth assessments and blood count measurements.The data was further processed to exclude transcrip-tional errors and transformed into time series with aconstant interval. This was done by by taking the last,mean, minimum or maximum parameter value in a settime window. A time series ends at the point when afirst positive blood culture test is taken or in case ofa negative culture patient, where there was no posi-tive test, at the time when the physiological data flowstops.3. MethodsA Recurrent Neural Network (RNN) is a computa-tional model designed to work with temporal features.It is similar to a feed forward neural network by exten-sion that cycles are present in the network. Throughthose cycles the model can obtain a memory effect thatallows to learn using input from several time steps inthe past.A commonly recognized problem in training recur-rent neural networks is the vanishing gradient prob-lem. The influence of inputs from several time stepsEarly detection of positive blood cultures using RNNTable 1. Prediction results of different networks trained onthe last values.Network ROC auc Pr aucRNN 0.797 0.492BiRNN 0.818 0.529LSTM 0.837 0.453BiLSTM 0.841 0.499BiLSTM adv. 0.891 0.629away fades exponentially. This makes it impossible forthose networks to learn longterm dependencies. LongShort-Term Memory (LSTM) networks (Hochreiter &Schmidhuber, 1997) mitigate this problem by intro-ducing the the principle of gating. A gate makes itpossible for the network to block inputs or outputsand that way contain the hidden state for longer timeperiods.In our work, we have used five types of networks:A unidirectional RNN, a bidirectional RNN, a unidi-rectional LSTM network, a bidirectional LSTM (BiL-STM) network and a more complex BiLSTM network(BiLSTM adv.) with an extra hidden layer. In themore complex BiLSTM network, hidden states fromall time steps have direct influence to the output node.In contrast, in the BiLSTM network, the hidden statevalues need to ripple through the whole LSTM chain.Figure 1 depicts an advanced BiLSTM network. Allnetworks consist of 1000 hidden nodes. A weightedMSE cost function is used with more weight for pos-itive admissions to compensate the imbalance in thedataset. There are about nine times more negativeadmissions than positive ones.Figure 1. Topology of an unfolded BiLSTM network with2 input features and 2 LSTM cells and an extra hiddenfeedforward layer.4. EvaluationThis section handles the evaluation of the mentionednetworks. Models are compared based on their abil-ity to predict up to the point where an actual bloodsample is taken and on their early prediction capabil-ities. Finally the temporal models are compared to anontemporal one.Table 1 shows the area under the Receiver OperatorCharacteristic and Precision Recall curve (ROC aucand PR auc). We use a dataset where the last value ina sample window is used. It is clear that the advancedBiLSTM network (BiLSTM adv.) achieved the bestperformance in this setting.Diagnosing a culture positive patient earlier increasesthe chances of survival. Therefore the model was alsotested on sequences that end several hours before theactual blood test was performed. The network is firsttrained on sequences that span 24 hours. Next, thenetwork is tested on sequences that stopped x hoursearlier. We conclude that up to 10 hours upfront themodel still reaches a ROC auc of 0.8. At 24 hoursupfront the ROC auc is around 0.76. The area underthe precision recall curve shows a similar evolution.It is interesting to see whether using temporal infor-mation actually brings an added value in predicting ablood culture test outcome. For this we compare to afeed forward neural network (FNN). As input we usean extremum dataset except that only the most recentsample is taken instead of the whole sequence. TheBiLSTM network uses the dataset with last samplesfrom each sample window. The FNN network keeps upwith the BiLSTM at times close to the blood culturetest but at 24 hours upfront the difference is alreadyremarkable. This indicates that indeed the temporalmodels bring an advantage to the table.5. ConclusionThis research explored models that are able to capturetemporal effects and predict blood culture test out-comes. We conclude that temporal models are clearlybetter in predicting the outcome of a blood culture testthan non-temporal models. Temporal models have aslight advantage in predicting the outcome close to thetime the blood sample test was taken but are notice-ably better than other models several hours upfront.AcknowledgmentsWe would like to thank Bram Gadeyne, ReinHouthooft, Cedric De Boom and Kirsten Colpaert fortheir invaluable support.Early detection of positive blood cultures using RNNReferencesHochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9, 1735–1780.Morrell, M., Fraser, V. J., & Kollef, M. H. (2005). De-laying the empiric treatment of candida bloodstreaminfection until positive blood culture results are ob-tained: a potential risk factor for hospital mortality.Antimicrobial agents and chemotherapy, 49, 3640–3645.

Early detection of positive blood cultures using recurrent neural networks on time series data

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Early detection of positive blood cultures using recurrent neural networks on time series data

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