Deep Learning from Heterogeneous Sequences of Sparse Medical Data

for Early Prediction of Sepsis

Mahbub Ul Alam

, Aron Henriksson

, John Karlsson Valik

2,3

, Logan Ward

, Pontus Naucler

2,3

and Hercules Dalianis

Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden

Division of Infectious Disease, Department of Medicine, Karolinska Institute, Stockholm, Sweden

Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden

Treat Systems ApS, Aalborg, Denmark

Keywords:

Sepsis, Early Prediction, Machine Learning, Deep Learning, Health Informatics, Healthcare Analytics.

Abstract:

Sepsis is a life-threatening complication to infections, and early treatment is key for survival. Symptoms of

sepsis are difﬁcult to recognize, but prediction models using data from electronic health records (EHRs) can

facilitate early detection and intervention. Recently, deep learning architectures have been proposed for the

early prediction of sepsis. However, most efforts rely on high-resolution data from intensive care units (ICUs).

Prediction of sepsis in the non-ICU setting, where hospitalization periods vary greatly in length and data is

more sparse, is not as well studied. It is also not clear how to learn effectively from longitudinal EHR data,

which can be represented as a sequence of time windows. In this article, we evaluate the use of an LSTM net-

work for early prediction of sepsis according to Sepsis-3 criteria in a general hospital population. An empirical

investigation using six different time window sizes is conducted. The best model uses a two-hour window and

assumes data is missing not at random, clearly outperforming scoring systems commonly used in healthcare

today. It is concluded that the size of the time window has a considerable impact on predictive performance

when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis.

1 INTRODUCTION

Sepsis is a leading cause of hospital morbidity and

mortality (Singer et al., 2016). It is also one of

the most serious forms of healthcare associated in-

fections, many of which are considered avoidable

(Cassini et al., 2016). The survival of sepsis patients

is dependent on initiating appropriate antimicrobial

treatment as early as possible (Ferrer et al., 2014). To

facilitate this, prompt detection of sepsis patients is

crucial. It has been shown that early identiﬁcation of

sepsis signiﬁcantly improves patient outcomes. For

instance, mortality from septic shock has been found

to increase by 7.6% for every hour that antimicrobial

treatment is delayed after the onset of hypotension

(Kumar et al., 2006), while timely administration of

a 3-hour bundle of sepsis care and fast administration

of antibiotics was associated with lower in-hospital

mortality (Seymour et al., 2017). Unfortunately, di-

agnosing sepsis early and accurately is challenging

even for the most experienced clinicians, as there is

no standard diagnostic test (Singer et al., 2016) and

symptoms associated with sepsis may also be caused

by many other clinical conditions (Jones et al., 2010).

Clinical decision support tools have the potential

to facilitate early intervention in sepsis patients. For-

tunately, clinical data that could be used to inform

predictions about patients that are at risk of sepsis is

already being routinely collected in electronic health

records (EHRs). Today, warning scores based on such

data are calculated for early identiﬁcation of clini-

cal deterioration at the bedside. Examples of early

warning scores include NEWS (Williams et al., 2012)

and qSOFA (Singer et al., 2016). These scoring tools

compare a small number of physiological variables to

normal ranges of values and generate a single com-

posite score: once a certain threshold is reached, the

system triggers an alert (Despins, 2017). However,

a serious limitation of early warning scores is that

they are typically broad in scope and were not specif-

ically developed for sepsis, which means that many

other diseases may also yield high scores. This can

cause breakdowns in the training and education pro-

cess and result in low speciﬁcity and high alarm fa-

Alam, M., Henriksson, A., Valik, J., Ward, L., Naucler, P. and Dalianis, H.

Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis.

DOI: 10.5220/0008911400450055

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 45-55

ISBN: 978-989-758-398-8; ISSN: 2184-4305

tigue. Early warning scores, which are used in clin-

ical practice today, are also overly simplistic by as-

signing independent scores to each variable, ignoring

both the complex relationships between different vari-

ables and their evolution in time (Vincent et al., 1998;

Smith et al., 2013).

Machine learning provides the possibility of over-

coming the limitations of heuristics-based warning

scores by accounting for dependencies between a

large number of (temporal) input variables from

EHRs to predict an outcome of interest, such as sep-

sis. Using machine learning for early prediction of

sepsis has been greatly facilitated by the possibil-

ity of exploiting agreed upon deﬁnitions of clinical

sepsis criteria for identifying cases (Delahanty et al.,

2019). This allows for the large-scale creation of la-

beled datasets needed for supervised machine learn-

ing. Therefore, the primary focus of machine learning

approaches should be on early detection, i.e. to detect

sepsis as early as possible prior to sepsis onset.

In this article, the Sepsis-3 deﬁnition (Singer et al.,

2016) is used for labeling three years of EHR data

from a general university hospital population. In con-

trast, most previous efforts have focused on using data

from intensive care units (ICUs) (Mani et al., 2014;

Desautels et al., 2016; Taylor et al., 2016; Moor et al.,

2019). Data from ICUs tends to be less sparse and

hospitalization periods shorter. In this study, a re-

current neural network, in the form of a long short-

term memory (LSTM) network, is used to learn from

heterogeneous sequences of EHR data for early pre-

diction of sepsis in the non-ICU setting. In particu-

lar, different temporal representations that divide the

longitudinal EHR data into time windows of various

sizes are investigated. In previous work, a one-hour

time window was used without justiﬁcation (Moor

et al., 2019; Futoma et al., 2017a,b), whereas we

wanted to investigate empirically the impact of us-

ing different window sizes. The window size has a

direct impact on the length of the sequences and the

amount of missing data: with a smaller window size,

the sequences become longer in terms of the number

of windows, and the missing rate higher; a larger win-

dow size will have the opposite effect, while also in-

creasing the likelihood of needing to summarize – or

represent in some other way – multiple values for a

given variable. We therefore investigate these inter-

connected factors: the window size vs. the length of

sequences, as well as basic approaches to handling

missingness. It is important to note that this paper

does not propose a novel deep learning architecture

for early prediction of sepsis, but rather investigates

some fundamental questions that are important to ad-

dress, the results of which are intended to inform the

design of new architectures. In summary, the main

contributions of this study are as follows:

• A deep learning LSTM model is used for early

prediction of sepsis in order to investigate differ-

ent representations of longitudinal EHR data. It is

shown that the size of the time window has a con-

siderable impact on predictive performance, mak-

ing it a particularly important design decision in

the face of sparse non-ICU data.

• Two basic approaches to missingness was inves-

tigated for handling sparse non-ICU data: assum-

ing data is missing at random and assuming data

is missing not at random. It is shown that the

latter assumption yields somewhat better perfor-

mance and indicates that missingness is some-

times a valuable indicator that should not always

be imputed.

• Predictive performance is evaluated for healthcare

episodes of various lengths, revealing consider-

ably higher performance for shorter vs. longer

sequences. The model is also evaluated in terms

of earliness, showing that more true positive pre-

dictions are made in the time windows closer to

sepsis onset.

2 METHODS AND MATERIALS

The EHR data used in this study comes from

the research infrastructure Health Bank (Dalianis

et al., 2015), that contains EHR data collected

from Karolinska University Hospital. This research

has been approved by the Regional Ethical Review

Board in Stockholm, Sweden under permission no.

2016/2309-32.

2.1 Data Selection

Patients older than 18 years admitted to the hospi-

tal between July 2010 and June 2013 were included,

and followed until ﬁrst sepsis onset, discharge or

death. Patients were excluded if admitted to an ob-

stetric ward and censored during ICU care. The

dataset encompasses 124,054 patients and 198,638

care episodes from a general university hospital popu-

lation over a three-year period. The incidence of sep-

sis in the dataset is 8.9%, yielding a very uneven class

distribution.

An instance in the dataset represents a care

episode, which constitutes the period between general

admission and discharge (or death) for a particular pa-

tient. If a patient was admitted via the emergency unit,

this arrival time was considered as admission time for

HEALTHINF 2020 - 13th International Conference on Health Informatics

that particular episode. Additionally, if the time fol-

lowing discharge and next admission for the same pa-

tient is less than 24 hours, the two are considered to be

part of the same care episode. Care episodes may in-

volve stays in several different wards and vary greatly

in length, with a median length of around three days.

2.2 Feature Selection

Data for each care episode is collected solely from the

EHR and comprises data on microbiological cultures

and antimicrobial treatment, as well as demographic

and physiological data. The included variables were

selected by domain experts.

In the study, data deﬁning collection of micro-

bial cultures and tests from all types of body ﬂu-

ids is used. Data on newly administrated antimicro-

bial treatment is collected based on ATC (Anatomi-

cal Therapeutic Chemical) codes (Nahler, 2009) J01

and J04. Demographic and physiological data is col-

lected for the following 19 parameters: age, body

temperature, heart rate, respiratory rate, systolic and

diastolic blood pressure, oxygen saturation, supple-

mentary oxygen ﬂow, mental status, leucocyte count,

neutrophil count, platelet count, C-reactive protein,

lactate, creatinine, albumin, and bilirubin. Most of

the variables are numeric, and generally extremely

sparse, with a missing rate of more than 90% in some

cases.

In addition to the aforementioned variables, early

warning scores are also used as aggregated features

in the predictive model. The output of the follow-

ing scoring tools were used: NEWS2 (Williams et al.,

2012), qSOFA (Singer et al., 2016) and SOFA (Vin-

cent et al., 1996). This is a common practice in ma-

chine learning (Raghu et al., 2017a,b). The model

was only allowed to access data that would be readily

available in the EHR – or could be computed from it

– at the time of prediction.

2.3 Care Episode Representation

To account for the temporality of the data, the care

episodes are transformed into sequences based on a

given window (bin) size. Experiments are conducted

using six different window sizes: 1, 2, 3, 4, 6 and 8

hours.

A variable in a time window can either be missing

or have multiple values associated with it. For exam-

ple, body temperature may not have been measured

in a given time window or may have been measured

multiple times. When multiple values are present in

a time window, the ”worst” value is chosen. This is

deﬁned as the most pathological value for a particular

variable and is determined apriori by clinical experts.

For certain variables, the most pathological value can

be either high or low. For example, in the case of

body temperature, a value less than 36 is considered

the worst, but if no such value exists, the highest value

is chosen instead.

Missing data can be handled in various ways and

a fundamental decision concerns whether data for a

given variable is assumed to be missing at random or

not (Steele et al., 2018). In this study, both assump-

tions are taken into account. When data is assumed

to be missing not at random, imputation is not car-

ried out; instead, missing values are simply assigned

an integer value which is not present in the data, and

the idea is that the model may learn to treat missing-

ness as a distinct feature. When data is assumed to

be missing at random, the following simple imputa-

tion strategy was carried out. If a value exists for a

given feature in the care episode, it is carried forward

to subsequent windows until another observed value

is encountered, which is then in turn carried forward

and so on. When there is no value for a given feature

in a care episode, it is imputed globally: for categori-

cal features, the most frequent value is chosen, while

mean imputation is carried out for numeric features.

For SOFA, qSOFA, and NEWS2, missing values are

not mean-imputed; instead, the score is assumed to

be 0 – if missing – at the start of an episode and then

carried forward as described above.

Figure 1 shows the distribution of care episode

lengths with different time window sizes and the

episode-wise missing rate, i.e. the percentage of miss-

ing values in a care episode. Smaller time windows

yield longer sequences and a higher rate of miss-

ingness, while larger time windows yield shorter se-

quences but a somewhat smaller amount of missing

data. There is high variance with respect to both se-

quence length and missingness.

2.4 Sepsis Deﬁnition

The operational Sepsis-3 clinical criteria are used to

deﬁne sepsis (Singer et al., 2016; Seymour et al.,

2016). To fulﬁll the criteria, patients are required to

suffer from a suspected infection in combination with

organ dysfunction (Singer et al., 2016; Seymour et al.,

2016). Suspected infection is deﬁned as having any

culture taken and at least two doses of antimicrobial

treatment newly administered within a certain time

period. If antimicrobial treatment was initiated ﬁrst,

cultures had to be collected within 24 hours. If cul-

tures were collected ﬁrst, antimicrobial treatment had

to be started within 72 hours after the cultures. Or-

gan dysfunction is measured by an increase in sequen-

Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis

Figure 1: Distribution of sequence length (left) and episode-wise missing rate with different time window sizes (right).

tial organ failure assessment (SOFA) score by greater

than or equal to 2 points compared to the baseline.

Organ dysfunction is measured 48 hours before to 24

hours after the onset of suspected infection. The base-

line SOFA score is deﬁned as the latest value mea-

sured before the 72-hour time window and is assumed

to be 0 in patients not known to have a pre-existing

organ dysfunction.

Sepsis onset time is regarded as the ﬁrst time-

window when both organ dysfunction and suspected

infection criteria are met. As a time window is used

as a ﬁxed length of time interval to represent the tem-

porality, the particular time window in which sepsis

onset occurs is regarded as time zero.

2.5 LSTM Model

A deep learning model learns from the input data

features, by using hierarchical representations of in-

put features, from lower level compositions to higher

level compositions. This type of abstraction at multi-

ple levels allows learning features automatically using

the complex formation of functions, which map the

input to the output straightforwardly without any need

for human feature engineering. (Goodfellow et al.,

2016).

In this study, we use a Long Short-Term Mem-

ory based Recurrent Neural Network (LSTM) model

(Hochreiter and Schmidhuber, 1997) for deep learn-

ing. The choice of model is motivated by the longi-

tudinal nature of the EHR data and the task to predict

sepsis as early as possible on the basis of current and

past information in a given care episode. LSTM mod-

els are particularly suited to this type of sequence la-

beling task, as it can retain information from previous

inputs in their internal memory. It also can learn from

a very distant past if the information is relevant by

using different gated cells (input, forget, and output)

where these cells determine what information to store

and what information to erase. From a clinical per-

spective, this is also relevant since typically clinical

measurements and observations closer to the outcome

is of higher importance in the care episode.

The PyTorch package (Paszke et al., 2017) was

used to implement the model on a Dell R730 server

with one Intel Xeon E5-2623 processor with 32 MB

cache memory extended with a GeForce GTX 1080

GPU. The server ran the operating system Linux De-

bian 9.1. One training cycle took approximately 60

minutes.

2.6 Baselines

The LSTM model is compared to two baselines in

the form of early warning scores that are commonly

used in clinical practice today: NEWS2 and qSOFA

(Singer et al., 2016). NEWS2 is an updated version of

the National Early Warning Score (NEWS) (Williams

et al., 2012) and constitutes an aggregate scoring sys-

tem for each time window in the episode based on

physiological measurements of respiration rate, oxy-

gen saturation, systolic blood pressure, pulse rate,

level of consciousness or new confusion, and tem-

perature. qSOFA (Quick SOFA) is another aggregate

scoring tool that takes into account altered mental sta-

tus, respiratory rate, and systolic blood pressure.

A score is originally only present in a time win-

dow if the data required for calculating the score is

also present. If no score is available at the beginning

of a care episode, a score of 0 is inserted in the corre-

sponding time window. Existing values are then car-

ried forward to subsequent time windows until a new

value is encountered. In order to convert the score to a

binary classiﬁcation decision, a threshold of 5 is used

HEALTHINF 2020 - 13th International Conference on Health Informatics

for NEWS2 and 2 for qSOFA. These are standard de-

cision thresholds for NEWS2 and qSOFA (Williams

et al., 2012; Singer et al., 2016).

2.7 Experimental Setup

The dataset is split into 80% for training, 10% for tun-

ing and 10% for testing and evaluating the tuned mod-

els. The data is stratiﬁed in each split using an equal

probabilistic distribution with respect to both class

and sequence length. Care episodes of a sequence

length with ﬁve or fewer instances are discarded. Pos-

itive cases are care episodes in which Sepsis-3 crite-

ria were fulﬁlled and comprise data from admission

to sepsis onset.

Twelve different versions of the dataset are created

based on six different time window sizes (1h, 2h, 3h,

4h, 6h, and 8h) and two approaches to handling miss-

ing values: with imputation and without imputation.

In each time window, the model outputs a probability

score concerning the presence or absence of sepsis in

the patient based on current and previous information

in the care episode.

The models are evaluated using a number of

predictive performance metrics. In order to as-

sess the models globally, without a speciﬁc decision

threshold, we used Area Under the Receiver Operat-

ing Characteristic curve (AUROC) and Area Under

the Precision-Recall Curve (AUPRC). AUROC is an

overall measure of discrimination and can be inter-

preted as the probability that the model ranks a true

positive higher than a false positive. As the data is

highly imbalanced, AUPRC is considered the primary

metric for model selection.

The hyper-parameters of the LSTM model are pre-

sented in Table 1. To tune these parameters, ﬁfty

points were chosen at random in order to more effec-

tively search the space instead of doing a grid search

in some restricted hyperparameter space. Oversam-

pling method was used to make the distribution even

(50% positive, and 50% negative) in each minibatch.

The model with the best AUPRC on the tuning set was

selected and evaluated on the test set.

Earliness is evaluated in one of two ways: (i) cal-

culating AUROC and AUPRC at different time points

before sepsis onset, and (ii) looking at the (median)

prediction times relative to sepsis onset (in hours) for

true positives. In order to evaluate earliness in terms

of (ii), class label predictions need to be made using

a decision threshold. We use a standard threshold of

> 0.5 and only allowed the model to make a single

positive prediction per care episode, retaining the ﬁrst

one and ignoring predictions in subsequent windows.

Since earliness calculated in this way depends solely

on true positives, it should not be analyzed in isola-

tion, but in conjunction with more conventional per-

formance metrics like sensitivity and positive predic-

tive value. We therefore report F

-score, which is the

harmonic mean between precision (positive predictive

value) and recall (sensitivity), along with the median

earliness in hours. Three evaluation settings are used

to calculate earliness according to (ii) depending on

the time period in which the evaluation is carried out:

(a) <24 hours prior to sepsis onset, (b) <48 hours

prior to sepsis onset, or (c) at any time in the care

episode. The motivation for the more conservative

evaluation settings is to reduce the effect of extremely

early predictions, which perhaps should not be con-

sidered realistic from a clinical point of view. We

also report the temporal distribution of true positive

predictions.

Table 1: Neural Network Parameters.

Name Values / Range

alpha 0, 10

−4

beta one 0, 1 −10

−1

beta two 0, 1 −10

−3

hidden layers 2, 3, 4

neurons 64, 128, 256

drop out 0, 10,20, 30, 40, 50, 60, 70

epochs 1,2

mini-batch 100

classiﬁcation function log-softmax

optimizer Adam optimizer

Note that the evaluation includes an assessment of

the models both in terms of their general predictive

performance and in terms of one possible way of em-

ploying the model in a clinical setting.

2.8 Experiments

A series of experiments were carried out with the use

of an LSTM model for early prediction of sepsis in

a general hospital population and compared to ex-

isting early warning scores in the form of NEWS2

and qSOFA. The experiments are centered around the

use of different time window sizes for representing

the temporally evolving EHR data. The size of the

time window affects (i) sequence length and (ii) miss-

ingness. The impact of these factors on the predic-

tive performance of the LSTM model was investi-

gated and two approaches are compared to dealing

with missing values. The predictive performance of

the LSTM models and the baselines is evaluated us-

ing a number of different metrics and the evaluation is

carried out in various conﬁgurations and time points

prior to sepsis onset. Naturally, particular attention is

Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis

Figure 2: Left: AUPRC for each model, using test dataset, as a function of the number of hours prior to sepsis onset/discharge.

Right: AUROC for each model, using test dataset, as a function of the number of hours prior to sepsis onset/discharge. The

models are colored according to the legend in the left plot. Dotted line represents without imputation, solid line represents

with imputation.

paid to the capacity of the models to predict the out-

come as early as possible.

The following ﬁve experiments are conducted.

The ﬁrst three experiments look at the predictive per-

formance of all twelve models in general, without us-

ing a speciﬁc decision threshold. The last two experi-

ments use a standard threshold of > 0.5 and only take

into account the ﬁrst positive prediction, as described

earlier. Post-analyses are also conducted using the

best overall model.

Experiment 1: Different Time Window Sizes.

The impact of using different window sizes is eval-

uated on the predictive performance of the resulting

model. The choice of window size has a substan-

tial impact on the sequence length distribution and

the amount of missing values in each time window,

as seen in Figure 1.

Experiment 2: Handling Missing Values. Two

very different approaches are investigated to handling

the large amount of missing values in EHR data on a

care episode level. In one approach, data is assumed

to be missing not at random and therefore no impu-

tation is carried out; instead, the model is allowed

to learn to treat missingness as a distinct feature. In

the other approach, data is assumed to be missing at

random (i.e. for a particular set of features) and is

evaluated using a simple imputation strategy based on

carrying forward existing values and globally mean-

imputing values that are absent at the beginning of a

care episode. These two approaches to handling miss-

ing values are evaluated for each of the six time win-

dow sizes, i.e. with different degrees of missingness.

Experiment 3: Performance at Different Time

Points. In this experiment, the predictive perfor-

mance is evaluated, in terms of AUROC and AUR-

PRC, at different time points relative to sepsis on-

set, starting from 24 hours prior to onset. The LSTM

models – with different window sizes and approaches

to missingness – are compared to commonly used

early warning scores in NEWS2 and qSOFA.

Experiment 4: Evaluation of Earliness. In this

experiment, a closer look is taken at the distribution of

earliness, i.e. at which time points true positive pre-

dictions are made relative to sepsis onset, for the best

LSTM model. This is shown with a combination of

overall F

-score for the particular time window size.

This post-analysis is carried out with the results ob-

tained from the previous experiments.

Experiment 5: Performance with Different Se-

quences Lengths. In this experiment, the predictive

performance is analyzed, in terms of F

-score, on care

episodes of different sequence lengths. The purpose

of this analysis is to learn how the best model copes

with heterogeneous care episodes in terms of length

of hospital stay.

HEALTHINF 2020 - 13th International Conference on Health Informatics

Table 2: Earliness performance in median hours before sepsis onset combined with F

-score, using test dataset, with and

without imputation of missing values, using different time window sizes.

Window

Size

Without Imputation With Imputation

<24h <48h All <24h <48h All

Med. F

1 6.00 0.65 7.37 0.53 11.79 0.28 5.90 0.61 7.77 0.48 10.78 0.25

2 7.24 0.73 8.00 0.57 12.68 0.33 7.02 0.74 8.00 0.57 13.43 0.32

3 12.45 0.77 12.57 0.65 14.34 0.38 7.78 0.75 8.33 0.65 13.00 0.42

4 7.13 0.73 7.92 0.63 14.22 0.41 11.22 0.79 10.00 0.67 13.36 0.41

6 10.09 0.76 11.00 0.68 14.00 0.46 8.43 0.74 8.48 0.65 14.00 0.44

8 8.73 0.74 9.82 0.67 14.75 0.46 6.58 0.70 8.00 0.64 24.28 0.44

3 RESULTS

In this section, the results of the above experiments

are presented in terms of (i) predictive performance at

different time points, (ii) earliness of true positive pre-

dictions, and (iii) predictive performance for episodes

of different sequence lengths.

3.1 Temporal Analysis

The overall predictive performance without a speciﬁc

decision threshold, in terms of AUPRC and AUROC,

of the different models is presented at different time

points relative to sepsis onset – from as early as 24

hours to sepsis onset time. The results, also depicting

the performance of the two baselines, are shown in

Figure 2.

As can be seen, the performance of the LSTM

models – with and without imputation – is vastly su-

perior to NEWS2 and qSOFA. The performance natu-

rally drops further from sepsis onset and does so quite

rapidly. The LSTM models without imputation gen-

erally performs better with respect to both AUPRC

and AUROC. The best results come from using a two-

hour time window, without imputation: at sepsis on-

set, the AUROC is 0.93 and AUPRC 0.73. However,

four hours prior to onset, the AUROC has dropped to

0.88 and AUPRC to 0.54. Finally, eight hours prior to

onset, the AUROC and AUPRC has dropped further

to 0.85 and 0.44, respectively.

It is clear that the size of the window has a signif-

icant impact on the predictive performance of the re-

sulting models. For instance, the difference between a

two-hour window and an eight-hour window is more

than 20 percentage points w.r.t. AUPRC and 5 per-

centage points w.r.t. AUROC at the time of sepsis

onset.

In general, assuming that data is missing not at

random leads to better predictive performance com-

pared to assuming that data is missing at random.

However, the difference is is generally smaller com-

pared to the size of the time window.

3.2 Earliness

The median in the distribution of earliness (in hours)

for true positive predictions of the models – with dif-

ferent time window sizes and with/without imputa-

tion – are shown in Table 2. Earliness results are re-

ported for the three evaluation settings (<24h, <48h

and All). Irrespective of evaluation setting, all models

are able to predict sepsis more than ﬁve hours before

onset in half of the cases, with the best median result

as early as 24.28 hours prior. We also present the F

scores of the models. F

-score is calculated by allow-

ing only one positive prediction in the care episodes

where the decision threshold was >0.5. With this par-

ticular way of using the model for early prediction,

the best result is obtained in the <24h setting with

a four-hour time window and imputation of missing

values: the F

-score is 0.79 and the median earliness

is 11.22 hours prior to sepsis onset.

In addition to the median values reported in Ta-

ble 2, the distribution of true positive predictions

with respect to earliness in hour is shown in Figure

3. The distribution is obtained from the overall best

model: a two-hour time window and without impu-

tation of missing values. When evaluating the en-

tire care episode (All), the median earliness in iden-

tifying sepsis is 12.68 hours before sepsis onset. In

the more conservative evaluation settings (<24h and

<48h), the median is 7.24 and 8.0 hours before onset,

respectively.

The distribution of earliness is also shown in Fig-

ure 4. Here, each bin represents the successive two

hours of prior predictions up to 24 hours. As can be

seen, there are relatively more true positive predic-

tions closer to sepsis onset. However, there are also

cases of earlier predictions, up to 24 hours prior to

sepsis onset. The bin representing earliness >24h in-

Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis

Figure 3: The distribution of earliness, using test dataset,

measured in terms of hours prior to sepsis onset, for true

positive cases in three different evaluation settings: allow-

ing true positives only <24h or <48h before sepsis onset,

as well as any time (All) prior to sepsis onset. Results are

shown for the best model: 2h time window, no imputation.

cludes some very early true positive predictions, for

example, predictions >96h prior to sepsis, which can

be regarded as outliers.

Figure 4: The distribution of earliness, using test dataset,

measured in terms of hours prior to sepsis onset from the

best LSTM model (2h time window, no imputation), evalu-

ation setting ’All’.

3.3 Episode Sequence Length

As using different time window sizes has a signiﬁ-

cant impact on the length of sequences that consti-

tute the care episodes, the predictive performance of

the model is also evaluated with respect to sequence

length. In Figure 5, the F

-score calculated as a lo-

cal scope of evaluation (described in section 2.8) at

the sepsis onset time (time zero) of our best model is

shown. The care episodes in the test set are binned

in such a way that each bin contains at least 1000 test

instances, ensuring that there is sufﬁcient statistical

evidence for estimating performance. As a result, the

bins may vary in size: there are, for instance, many

episodes of length 1 (n>1000), i.e. comprising only

a single time window (here, 2h), which make up one

bin. As seen in Figure 1, the episode sequence length

distribution is skewed towards shorter sequences; as a

result, the bins encompass more sequence lengths the

longer the sequences get.

In general, the model performs better on shorter

care episodes. The results are high for sequences

comprising fewer than around eleven time windows.

The performance degrades quickly with care episodes

of length greater than eleven.

4 DISCUSSION

In this study, our aim was not to propose a novel deep

learning architecture for early prediction of sepsis.

We rather wanted to take a step back and investigate

two basic and interconnected assumptions, without

empirical or theoretical justiﬁcation, made by current

state-of-the-art models (Futoma et al., 2017a,b; Moor

et al., 2019): (i) dividing the temporal EHR data into

hourly time windows and prediction times, and (ii)

treating missing values in the care episodes as miss-

ing at random. We carried out an extensive empirical

investigation into these matters and moreover focused

on early detection of sepsis in the non-ICU setting,

where data is considerably more sparse and hetero-

geneous. As the choice of window size affects the

number of windows in each care episode, we also in-

vestigated how the model performs on care episodes

of different (sequence) lengths.

The investigation demonstrates that the size of the

time window used for dividing up a patient’s longi-

tudinal EHR data has a clear impact on the predic-

tive performance of the model. The size of the time

window is hence an important consideration and a de-

sign choice that needs to justiﬁed, either empirically

or from a clinical point of view. In our non-ICU set-

ting, with heterogeneous data representative of a gen-

eral hospital population, we found that using a 2-hour

time window led to the best predictive performance

overall. In an ICU setting, it may very well be that

a smaller window leads to better performance since

the data is of higher resolution and less sparse. As

described earlier, the size of the time window has a

signiﬁcant impact on sparsity per care episode and

how often clinical measurements are taken will differ

signiﬁcantly in ICU vs. non-ICU settings. However,

as clinical measurements are not taken as frequently

HEALTHINF 2020 - 13th International Conference on Health Informatics

Figure 5: Prediction performance of the best LSTM model

(2h time window, no imputation, evaluation setting ’All’)

for episodes with different sequence lengths using test

dataset. Each bin is created such that it contains at least

1000 test instances. The analysis is carried out using F

score.

in the non-ICU setting, only considering the ”worst”

value – as we have done here – for each feature and

time window may not be adequate since, in this way,

we are discarding potentially valuable information. In

future work, a method is needed that better utilizes all

of the available data.

An important challenge lies in handling the ex-

treme sparsity (as shown in Figure 1, right) in non-

ICU EHR data. In this study, we used 22 features,

and the missingness in our data per feature is on aver-

age 72.7%, with a median of 87.6% (with a four-hour

time window). The variance across care episodes is

substantial and could in future be modeled using an

end-to-end hybrid attention-based neural architecture

(Vaswani et al., 2017), where different attention can

be provided to an episode when it is sparse and oth-

erwise. Previous studies in the area consider miss-

ingness as a random phenomenon and model it on an

evenly spaced grid using the Multitask Gaussian Pro-

cess (MGP) adapters (Futoma et al., 2017a,b; Moor

et al., 2019). However, data in the clinical setting is

often missing not at random. On the contrary, miss-

ingness may provide important information about the

condition of the patient or the assessment of the treat-

ing physician. The meaning of a missing value, and

thereby also how it should be handled, may also vary

across medical institutions. For instance, if some

tests are completed routinely at one location, but only

when there is a suspected infection at another, then

there will be a signiﬁcant difference in the predictive

contribution of missingness between the two loca-

tions. It may therefore be counterproductive to impute

missing values in all cases and our preliminary results

support this notion. In this study, we experimented

with very basic techniques for imputation and mod-

eling missingness as a feature, i.e. using a dummy

value. In the future, we plan to represent missing-

ness not at random in a more sophisticated way, for

example using advanced Generative Adversarial Net-

works (GANs) (Li et al., 2019) to model the missing

data distribution as not at random. We also plan to ex-

plore approaches capable of modeling both data that

is missing at random and not at random. Attention-

based architectures, as described above, could also be

utilized here where both types of missingness can be

modeled in an end-to-end manner.

Model evaluation using AUROC and AUPRC

is common practice in machine learning, including

for the early sepsis prediction task (Futoma et al.,

2017a,b; Moor et al., 2019). AUROC and AUPRC

provide a global view of predictive performance, mea-

sured on a continuum of thresholds values for the

classiﬁcation of patients into sepsis and no sepsis. In

order to deploy a machine learning model in a real

clinical setting, however, the model should be tuned

according to the circumstances and prerequisites of

the medical institution in which it will be used. The

decision threshold can be optimized according to one

or more performance metrics and this choice should

be informed by clinical needs – a decision that may

depend on, for instance, the tolerance for false posi-

tives. In this paper, we used a decision threshold of

>0.5 for positive predictions and only allowed one

positive prediction per episode. There are many alter-

native ways of deploying the model, not only using

a different decision threshold, but also, for instance,

allowing the model to make multiple predictions and

silencing the model for some period following a false

positive, i.e. according to the attending physician.

The results also show that the optimal window size

is different when evaluating the model from a general

perspective and when employing the model in a spe-

ciﬁc manner. It indicates that the window size needs

to be determined accordingly.

In the future, we will try to modify the neural

network-based architecture based on the insights we

obtained from this study as described above. We may

also explore using a dynamic window size depend-

ing, for instance, on the level of sparsity. To enrich

our data representation, we will also incorporate free

EHR text data with our current structured data, as pre-

vious work has shown that it can lead to improved

predictive performance for outcome prediction (Hen-

riksson et al., 2015). Multihead attention, in par-

ticular, has proven to be the reason for the success

Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis

of state-of-art pre-trained natural language process-

ing embedding models based on BERT (Huang et al.,

2019; Alsentzer et al., 2019) for downstream tasks,

which can be used in this context. The explainability

of the model is a crucial issue here as this can be uti-

lized by different stakeholders (Lipton, 2017). In the

future, we will also explore this explainability issue.

5 CONCLUSIONS

We investigated missingness and different time win-

dow sizes in extremely sparse EHR data obtained

from a Swedish university hospital for the task of

early prediction of sepsis using a deep learning-based

LSTM model. It was shown that the size of the win-

dow has a signiﬁcant impact on the predictive perfor-

mance of the models. We also observed that treating

missing data as missing not at random can in some

cases lead to better predictive performance compared

to assuming that it is missing at random.

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