HYBRID MORTALITY PREDICTION USING MULTIPLE SOURCE SYSTEMS

DOI: 10.5121/ijci.2019.8101 1
HYBRID MORTALITY PREDICTION USING
MULTIPLE SOURCE SYSTEMS
Isaac Mativo1
, Yelena Yesha 1
, Michael Grasso2
, Tim Oates 1
, Qian Zhu 3
1
Department of Computer Science, University of Maryland Baltimore County (UMBC)
2
School of Medicine, University of Maryland Baltimore (UMB)
3
Department of Information Systems, University of Maryland Baltimore County
(UMBC)
ABSTRACT
The use of artificial intelligence in clinical care to improve decision support systems is increasing. This is
not surprising since by its very nature, the practice of medicine consists of making decisions based on
observations from different systems both inside and outside the human body. In this paper, we combine
three general systems (ICU, diabetes, and comorbidities) and use them to make patient clinical predictions.
We use an artificial intelligence approach to show that we can improve mortality prediction of hospitalized
diabetic patients. We do this by utilizing a machine learning approach to select clinical input features that
are more likely to predict mortality. We then use these features to create a hybrid mortality prediction
model and compare our results to non artificial intelligence models. For simplicity, we limit our input
features to patient comorbidities and features derived from a well-known mortality measure, the Sequential
Organ Failure Assessment (SOFA).
KEYWORDS
Decision Support Systems; Artificial Intelligence; Hybrid Systems.
I. INTRODUCTION
Clinical decision support systems are driven by large amounts of data sourced from various
disparate systems. These may include data from lab instruments, wearable devices, prior hospital
records, patient reported data, etc. Often, the clinician is exposed to a lot of this data and is
expected to make decisions to improve clinical outcomes. Although decision support systems are
effective in logical tasks such as alerting the user when some parameters are outside defined
ranges, they are not designed to learn from the data they have. As such, they are limited to what
they have been programmed to do. This limitation becomes an opportunity to experiment with
artificial intelligence within the clinical setting [1]. Specifically, a hybrid system that combines
attributes from both traditional clinical decision support systems and attributes derived from
machine learning tools to create a prediction model becomes attractive.Hybrid systems have been
used in many fields to improve performance in areas such as managing humanitarian relief chains
[2] and multiple-criteria decision-making [3] . In healthcare, hybrid decision support systems
have been used to help clinicians make more informed decisions [4] [5]. For mortality prediction,
several hybrid approaches have been proposed such as the use of hybrid image features on neural
network for cancer patients [3][6].In this paper, we take a systems theory view of a clinical
setting whereby many different systems come together to inform the clinician on the best
decisions to make. In the next section, we will describe what we consider to be the three systems
that we consider and apply machine learning tools on.

International Journal on Cybernetics & Informatics (IJCI) Vol. 8, No.1, February 2019
2
2. THE THREE SYSTEMS
A. Diabetes
For this paper, we consider the chronic disease diabetes as one of our three systems. This is
because diabetes, being a disorder of metabolism, affects multiple systems within the human
body [7]. When blood sugar is not well controlled and therefore outside the normal range for
prolonged periods of time, the proper functioning of various organ systems is compromised.
Consequently, this manifests in various body conditions such as kidney disease, neuropathy, liver
disease, etc. [8] .From a cybernetics perspective, diabetes is interesting because it involves the
delicate control of blood sugar by both systems inside and outside of the body. Diabetic patients
often rely on external interventions such as medication ingestion, insulin injection, dietary
controls, and lifestyle changes such as exercise. When the sugar level in blood is found to be
outside the normal range based on information gathered, various actions are taken to bring it
under control.
B. Intensive Care Unit
We consider the Intensive Care Unit (ICU) in a hospital to be our next system. This is because the
ICU is a specialized facility with many controlled and interconnected systems that work together
to intervene in critically ill patients. In the ICU, information is availed through various channels
such as electronic medical records, clinical notes, instrument measurements, etc. [8] [9] All this
information is then used to provide a level of clarity in making clinical decisions. Various aspects
of this information such as temporal occurrence (e.g. did the blood pressure fall after or before
takings a specific medication?) dependency (e.g. eat solid foods before taking a certain
medication), are important. As such, in an ICU facility, the proper control of information from
various systems is critical [10] . In this paper therefore, we view the ICU facility as one
large system with many subsystems within it.
C. Comorbidities
Comorbidities are co-occurring disease conditions within a person. Often, diabetic patients have
other disease conditions as well. Some of these conditions are more commonly associated with
diabetes than others [11]. In this paper, we think of comorbidities as a system because they are
well-defined conditions within the human body that individually and collectively influence the
wellbeing of the entire body. Since comorbidities are an important piece of information
considered in patient treatment, we considered comorbidities to be relevant in informing our
hybrid prediction model.
With these three systems, we build a hybrid mortality prediction model and evaluated its
performance. It is a hybrid model because it combined both machine learning techniques and
non-machine learning techniques to select it features and perform its predictions. In the next
section, we discuss the materials we used to do our experiment.
3. MATERIALS
A. Medical Information Mart for Intensive Care (MIMIC) III

3
The data source we used to get our patient population is MIMIC-III [12] . MIMIC-III is a large
and single-center database comprising de identified health related information containing 46,520
patients of whom 20,399 are female and 26,121 are male. These patients stayed in critical care
units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes
information such as demographics, vital sign measurements, laboratory test results, procedures,
medications, caregiver notes, imaging reports, and mortality. In this study, we accessed MIMIC-
III to extract diabetes patients for mortality prediction.
B. Elixhauser comorbidity measure.
In order to get the comorbidity information for our diabetic patients, we used The Elixhauser
comorbidity index [13] . This index consists of 30 comorbidity measures that have been shown to
have an impact on patient hospital length of stay, cost, and mortality [14] . Classification in to this
index is based on conditions described by the ICD-9-CM (International Classification of
Diseases, Ninth Edition, Clinical Modifications) discharge records. The Elixhauser index assigns
a value of 1 for each of 30 predefined conditions, and computes the sum. Therefore, a score of 10
indicates a patient has 10 conditions present.
C. Mortality Measure
To compare our hybrid model prediction accuracy with a needed a tool that measured mortality.
For this, we chose the Sequential Organ Failure Assessment (SOFA) [15]. SOFA is developed
from a large sample of ICU patients from around the world and gives a score based on the
severity of on a patient. The SOFA score is computed based on six variables, with each variable
corresponding to an organ system. The organ systems included in SOFA are respiratory,
cardiovascular, hepatic, coagulation, renal and neurological systems. Each organ system is
assigned a value between 0 (good) and 4 (severe). The SOFA score is the sum of all the
individual organ system values.
4. METHODS
In this section, we describe the methods we used to retrieve the patient cohort, computer the
mortality and comorbidity measure, and to predict mortality. For this study, ethical approval was
not needed because the data used is de-identified t o conform with the Health Insurance
Portability and Accountability Act (HIPAA). Additionally, the data is publicly available to
researchers.
A. Patient Retrieval
From our MIMIC III PostgreSQL database, we retrieved the diabetes patient cohort from MIMIC
III database based on ICD-9 codes for diabetes. To avoid restricting diabetic patients with
comorbidities for further mortality prediction, we extracted the entire diabetes diagnoses for each
of the diabetic patients.
B. Mortality Measure Computation
We used the SOFA score as a mortality measure. To calculate the SOFA score for each patient,

4
we extracted the patient in the PostgreSQL database for calculating the the SOFA score
corresponding to the organ areas as shown in table 1. We extracted this data using query scripts in
PostgreSQL from the MIMIC III diabetic patients data based on the first day of each patient's’
ICU stay. For each patient, we used an SQL script to calculate the actual points for each organ
system, then added up the points for each organ system to obtain the SOFA score. For missing
values, we assigned zero points to that organ system. We did this for all collected diabetic
patients.
Table 1: Sofa Organ Areas and Points Thresholds
Organ/Points 1 2 3 4
Respiratory
PaO 2 /FiO 2
(mmHg)
<400 <300 <200 and
respiratory
support
<100 and
respiratory
support
Cardiological
Mean arterial
pressure
MAP < 70
mm/Hg
dop <=
5 or
dob
dop > 5
OR epi <=
0.1 OR nor
<= 0.1
dop > 15 OR epi
> 0.1 OR nor >
0.1
Hepatic
Bilirubin (mg/dl)
[μmol/L]
1.2–1.9
[> 20-32]
2.0–5.9
[33-101]
6.0–11.9
[102-204]
> 12.0
[> 204]
Neurological
Glasgow coma
scale
13 - 14 10 - 12 6 - 9 <6
Coagulation
Platelets×103/μl
<150 <100 <50 <20
C. Comorbidity Measure and Weight Computation
We used the Elixhauser index to calculate the comorbidity measure for each diabetic patient in
MIMIC-III. For each diabetic patient, we extracted 29 different diagnosis excluding diabetes and
quantified them by using the Elixhauser index for comorbidities represented in the diabetic
population. In order to improve the efficiency of our classifier by reducing the number of
features, we identified the top five comorbidities that are most predictive of mortality across the
identified diabetic patients. To do this, we experimented using 5 different feature selection
algorithms, which are Gain Ratio [16] , Correlation [17] , Symmetrical Uncertainty [18] ,
Information Gain [19] , and Correlation Feature Selection (CFS) Subset Evaluator [20] . For each
algorithm run, we ranked the comorbidities based on the resulted predictive mortality. We then
averaged these results over the 5 algorithms to get our final top 5 most predictive comorbidities.
The final list of the comorbidities was cardiac arrhythmias, coagulopathy, metastatic cancer,
congestive heart failure, and fluid electrolyte.
D. Mortality Prediction
For mortality prediction, we created a hybrid mortality prediction model that took as input the 6
SOFA scores representing the 6 organ systems and the top 6 ranked comorbidities, for a total of
11 features. We then experimented with a Naive Bayes classifier and a Random Forest classifier

5
with 10-fold cross validation across our patient population. We compared the prediction accuracy
of our hybrid system with the accuracy of (i) a prediction model with only the SOFA scores and
(ii) a prediction model with only the comorbidity information. Our accuracy comparison was
based on the AUC on both the Naive Bayes and Random Forest classifiers on mortality.
5. RESULTS
A. Patient Retrieval
A total of 10,403 of diabetes patients were retrieved from 46,520 patients contained in MIMIC III
database. Of all the identified diabetic patients, 1,513 died while in ICU. Among the 10,403
diabetes patients, we identified 29 different comorbidities. B. Mortality Measure Computation
Table 2 shows the calculated SOFA scores and the corresponding mortality rates as computed
from our data. Our mortality rates were consistent with the expected SOFA mortality rates.
Table 2: Calculated Mortality Rates
C. Comorbidity Measure and Weights
Figure 1 shows the results of calculated comorbidity for the diabetic patients using the
Elixhauser index. As can be seen, most of the patients had comorbidities.
Figure 1: Comorbidities Across Patients

6
Table 3 shows results generated via the five feature extraction algorithms to determine what
comorbidities to be applied for prediction classifiers. These are the comorbidities that were used
as features in our model.
Table 3: Highly Weighed Comorbidities
D. Mortality Prediction
Table 4 shows the results generated by the Naive Bayes and Random Forest classifiers. The ROC
is lowest when using only the 5 comorbidity features are used, is better when using the 6 SOFA
scores are used, and best when both the 5 comorbidities and 6 SOFA scores are combined. Thus,
the hybrid model outperforms the natively SOFA or comorbidity only models.
Table 4: Mortality Prediction Results
6. DISCUSSION
In this study, we proposed viewing ICU clinical care through the lens of cybernetics. We

7
identified three systems; the ICU, diabetes, and patient comorbidity, as related and defined
systems that are important in supporting clinical decisions. We examined the mortality impact of
comorbidities on diabetic patients patients admitted in an ICU unit using machine learning
tools.We created a hybrid mortality prediction model and compare its results to non-hybrid
models.The results we obtained support our claim that mortality prediction can be improved by
incorporation key features from different systems (comorbidities in our case) and using artificial
intelligence to train our classifiers.
Another key observation from our results is that we can still get improved performance from our
prediction model without using all of the available input features. In our case, we used statistical
techniques to rank all our comorbidities based on their weight in impacting mortality. This
observation is important because within systems, we can optimize performance by carefully
selecting the components that affect our output. This careful selection results in dimension
reduction and consequently reduces computational resources needed to run algorithms while at
the same time producing improved results.
A logical extension of this research is further defining the subsystems within our system, or yet
still identifying other systems that are important in improving clinical decision support
systems.An improvement of our approach would be to take a closer look at the information flow
within and across our systems and evaluate its impact on the overall efficiency of an ICU
setting.Important makers, in this case, could include items such as readmission rates, adverse
events, hospital length of stay, central line infection, etc. We hope that with this study we have
demonstrated that it is possible to view clinical decision support systems from cybernetics
perspective and encouraged other researchers to consider this approach.
DISCLOSURE OF INTERESTS
The authors report no conflict of interest.
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