ICU Patient Deterioration Prediction : A Data-Mining Approach

Jan Zizka et al. (Eds) : ICAITA, SAI, CDKP, Signal, NCO - 2015
pp. 209–225, 2015. © CS & IT-CSCP 2015 DOI : 10.5121/csit.2015.51517
ICU PATIENT DETERIORATION
PREDICTION: A DATA-MINING
APPROACH
Noura AlNuaimi, Mohammad M Masud and Farhan Mohammed
College of Information Technology,
United Arab Emirates University, Al-Ain, UAE
{noura.alnuaimi, m.masud, 200835338}@uaeu.ac.ae
ABSTRACT
A huge amount of medical data is generated every day, which presents a challenge in analysing
these data. The obvious solution to this challenge is to reduce the amount of data without
information loss. Dimension reduction is considered the most popular approach for reducing
data size and also to reduce noise and redundancies in data. In this paper, we investigate the
effect of feature selection in improving the prediction of patient deterioration in ICUs. We
consider lab tests as features. Thus, choosing a subset of features would mean choosing the
most important lab tests to perform. If the number of tests can be reduced by identifying the
most important tests, then we could also identify the redundant tests. By omitting the redundant
tests, observation time could be reduced and early treatment could be provided to avoid the risk.
Additionally, unnecessary monetary cost would be avoided. Our approach uses state-of-the-art
feature selection for predicting ICU patient deterioration using the medical lab results. We
apply our technique on the publicly available MIMIC-II database and show the effectiveness of
the feature selection. We also provide a detailed analysis of the best features identified by our
approach.
KEYWORDS
Big data analytics; data mining; ICU; lab test; feature selection; learning algorithm
1. INTRODUCTION
Healthcare is changing from traditional medical practice to modern evidence-based healthcare.
Evidence is based on patient data, which are collected from different resources like electronic
health record (EHR) systems, monitoring devices and sensors [1]. One specific example of these
technological advances is the observation and monitoring technologies for intensive care unit
(ICU) patients. Currently, the data generated in the process of medical care ICUs are huge,
complex and unstructured. Such data can be called big data due to their complexity, large size
and difficulty to process in real-time [2]. However, these data could be used with the help of
intelligent systems, such as big data analytics and decision support systems, to determine which
patients are at an increased risk of death. This could support making the right decision to enhance
the efficiency, accuracy and timeliness of clinical decision making in the ICU.

210 Computer Science & Information Technology (CS & IT)
Reducing the amount of data without losing information is a great challenge. Dimension
reduction would be the first solution to eliminate duplicate, useless and irrelevant features. In this
paper, our goal is to propose an efficient mining technique to reduce the observation time in ICUs
by predicting patient deterioration in its early stages through big data analytics. Our proposed
technique has several contributions. First, we use the lab test results to predict patient
deterioration. To the best of our knowledge, this is the first work that primarily uses medical lab
tests to predict patient deterioration. Lab test results have a crucial role in medical decision
making. Second, we identify most important medical lab tests using state-of-the-art feature-
selection techniques without using any informed domain knowledge. Finally, our approach helps
reduce redundant medical lab tests. Thus, healthcare professionals could focus on the most
important lab tests to assist them, which would save not only costs but also valuable time in
recovering the patient from a critical condition.
The paper is organised as follows. Section 2 presents the related work of predicting ICU death,
Section 3 gives background on data mining and big data analytics, Section 4 illustrates our
proposed approach, Section 5 summarises the MIMIC II dataset, Section 6 illustrates the
experiment’s work, Section 7 discusses the findings, and finally, the conclusion of this research is
presented in Section 8.
2. LITERATURE REVIEW
This section reviews related works for predicting ICU death or the deterioration of ICU patients.
We highlight some similarities and differences between some of the related works and the
proposed work.
In [3], the authors developed an integrated data-mining approach to give early deterioration
warnings for patients under real-time monitoring in the ICU and real-time data sensing (RDS).
They synthesised a large feature set that included first- and second-order time-series features,
detrended fluctuation analysis (DFA), spectral analysis, approximative entropy and cross-signal
features. Then, they systematically applied and evaluated a series of established data-mining
methods, including forward feature selection, linear and nonlinear classification algorithms, and
exploratory under sampling for class imbalance. In our work, we are using the same dataset.
However, we are using only the medical lab tests. Also, in our approach, we depend on feature
selection to reduce the size of the dataset.
A health-data search engine was developed in [4] that supported predictions based on the
summarised clusters patient types which claimed that it was better than predictions based on the
non-summarised original data. In our work, we use only the medical lab tests, and we attempt to
highlight the most important medical labs.
Liu et al. [4] investigated the critical feature size dimension. In their work, an ad hoc heuristic
method based on feature-ranking algorithms was used to perform the experiment on six datasets.
They found that the heuristic method is useful in finding the critical feature dimension for large
datasets. In our work, we also use the ranking to rank the most useful features. However, we
attempt to investigate the percentage of selected features that would be enough to have moderate
model accuracy.

Computer Science & Information Technology (CS & IT) 211
A survey of feature selection is presented in [6]. The authors presented a basic taxonomy of
feature-selection techniques and discussed their use, variety and potential in a number of
common and upcoming bioinformatics applications.
Cismondi et al. [5] proposed reducing unnecessary lab testing in the ICU. They applied artificial
intelligence to study the predictability of future lab test results for gastrointestinal bleeding. This
work is the closest work to our research; they have the same objective of reducing unnecessary
lab tests. However, they only focus on gastrointestinal bleeding. In our work, we are targeting all
cases in the ICUs.
3. BACKGROUND ON DATA MINING AND BIG DATA ANALYTICS
Healthcare, like other sectors, is facing the need for analysing large amounts of information,
otherwise known as big data, which has become a major driver of innovation and success. Big
data has potential to support a wide range of medical and healthcare functions, including clinical
decision support [2].
Data mining is the analysis step of knowledge discovery. It is about the ‘extraction of interesting
(non-trivial, implicit, previously unknown, and potentially useful) patterns or knowledge from
huge amount of data [10]’. When mining massive datasets, two of the most common, important
and immediate problems are sampling and feature selection. Appropriate sampling and feature
selection contribute to reducing the size of the dataset while obtaining satisfactory results in
model building [4].
3.1. Feature Selection
In machine learning, feature selection or attribute selection is the process of selecting a subset of
relevant features (variables, predictors) for use in model construction. Feature selection
techniques are used (a) to avoid overfitting and improve model performance, i.e. predict
performance in the case of supervised classification and better cluster detection in the case of
clustering, (b) to provide faster and more cost-effective models and (c) to gain deeper insight into
the underlying processes that generated the data. In the context of classification, feature selection
techniques can be organized into three categories, depending on how they perform the feature
selection search to build the classification model: filter methods, wrapper methods and embedded
methods, presented in table 1 [6] [7]:
1) Filter Methods are based on applying a statistical measure to assign a scoring to each
feature. Then, features are ranked by score and either selected or removed from the
dataset. The methods are often univariate and consider the feature independently or with
regard to the dependent variable.
2) Wrapper Methods are based on the selection of a set of features as a search problem,
where different combinations are prepared, evaluated and compared to other
combinations. A predictive model is used to evaluate a combination of features and
assign a score based on model accuracy.
3) Embedded Methods are based on learning which features most contribute to the accuracy
of the model while the model is being created.

Table 1: Feature selection categories.
Model Search Advantages Disadvantages
Filter Fast
Scalable
Independent of the classifier
Ignores feature dependencies
Ignores interaction with the classifier
Wrapper Simple
Interacts with the classifier
Models feature decencies
Less computational
Risk for overfitting
More prone than randomized algorithms
Classifier-dependent selection
Embedded Interacts with the classifier
More computational
Models feature dependencies
Classifier-dependent selection
3.2. Data Classification Techniques
Classification is a pattern-recognition task that has applications in a broad range of fields. It
requires the construction of a model that approximates the relationship between input features
and output categories [8]. Some of the most popular techniques are discussed here in brief, all of
which are used in our work.
1) The Naïve Bayes classifier is based on applying Bayes’ theorem with strong
independence assumptions between the features. As one of its main features, the Naïve
Bayes classifier is easy to implement because it requires a small amount of training data
in order to estimate the parameters, and good results can be found in most cases.
However, it has class conditional independence, meaning it causes losses of accuracy and
dependency [9].
2) Sequential minimal optimization (SMO) is an algorithm for efficiently solving the
optimization problem which arises during the training of support vector machines [10].
The amount of memory required for SMO is linear in the training set size, which allows
SMO to handle very large training sets [11].
3) The ZeroR classifier simply predicts the majority category, which relies on the target and
ignores all predictors. Although there is no predictability power in ZeroR, it is useful for
determining a baseline performance as a benchmark for other classification methods [10].
4) A decision tree (J48) is a fast algorithm to train and generally gives good results. Its
output is human readable, therefore one can see if it makes sense. It has tree visualizers to
aid understanding. It is among the most used data mining algorithms. The decision tree
partitions the input space of a data set into mutually exclusive regions, each of which is
assigned a label, a value or an action to characterize its data points [10].
5) A RandomForest is a combination of tree predictors such that each tree depends on the
values of a random vector sampled independently and with the same distribution for all
trees in the forest [12].
4. PROPOSED APPROACH
In this section we introduce our approach for the Big Data mining technique for predicting ICU
patient deterioration. Figure 1 shows the architecture of the proposed technique.

Figure 1: Architecture of the proposed approach
The data are collected from the database of ICU patients (step 1). Then the data are integrated,
cleaned and relevant features are extracted (step 2). After that, feature selection or dimensionality
reduction techniques are applied to obtain the best set of features and reduce the data dimension
(step 3). Then the prediction model is learned using a machine learning approach (step 4). When
a new patient is admitted to the CPU, the patient’s data are collected incrementally (step 5). The
patient data are evaluated by the prediction model (step 6) to predict the possibility of
deterioration of the patient, and warnings are generated accordingly. Each of these steps is
summarized here, and more details of the dataset are given in Section 5.
1) ICU Patient Data: The details of the data and the collection process are discussed in
Section 5.
2) Preprocessing: At the preprocessing stage, we used two different datasets. These datasets
were generated from a Labevents table. The first dataset contained the average value of
applied medical tests, and the second contained the total number of times for each test
was applied.
3) Feature Selection / Dimension Reduction: attribute selection is the process of selecting a
subset of relevant features (variables, predictors) for use in model construction. The goal
here is to reduce the attributes so medical professional can identify the most important
medical lab tests used by reducing the redundant tests. In our work, we select filter
methods because they are moderately robust against the overfitting problem, as follows:
a. Attribute evaluator: InfoGrainAttributeEval
b. Search method: Ranker
c. Attribute selection mode: use full training set
4) Learning: In our experiment we use a classification technique and five of the most
popular classifier techniques: Naïve Bayes classifier, Support vector machine (SVM),

ZeroR classifier, decision tree (J48) and RandomForest. We use different types of
machine learning order to avoid random results.
5) Model: The developed model aims to predict ICU patient deterioration by mining lab test
results. Thus, observation time can be reduced in the ICUs and more actions can be taken
in the early stages.
6) New patient data: When a new patient is admitted to the ICU, all his information is stored
in the database. Some of these are incremental, such as vital sign readings, lab test
results, medication events etc. The data of the patient again go through the preprocessing
and feature extraction phases before they can be applied to the model.
7) Prediction: After each new test result, medication event, etc., the patient data are
preprocessed and features are extracted to supply to the prediction model. The model
predicts the probability of deterioration for the patient. This probability may change
when new data (e.g. more test results) are accumulated and applied to the model. When
the deterioration probability reaches a certain threshold specified by the healthcare
providers, a warning is generated. This would help the healthcare providers to take
proactive measures to save the patient from getting into a critical or fatal condition.
5. MIMIC II DATABASE
The MIMIC-II database is part of the Multiparameter Intelligent Monitoring in Intensive Care
project funded by the National Institute of Biomedical Imaging and Bioengineering at the
Laboratory of Computational Physiology at MIT, which was collected from 2001 to 2008 and
represents 26,870 adult hospital admissions. In our work, we use MIMIC-II version 2.6 because
is more stable than the newer version 3, which is still in the beta phase and needs further work of
cleaning, optimizing and testing. MIMIC-II consists of two major components: clinical data and
physiological waveforms.
The MIMIC dataset has three main features: (1) it is public; (2) it has a diverse and very large
population of ICU patients; and (3) it contains high temporal resolution data, including lab
results, electronic documentation, and bedside monitor trends and waveforms[13]. Several works
have used the MIMIC dataset, such as [14], [15] and [16].
In our work, we focus on the clinical data, the LABEVENTS and LABITEMS tables. The
Labevents table contains data of each patient’s ICU stay, as presented in table 2, and table 3
contains descriptions of the lab events. Considering medical lab choice was done because we
wanted to investigate the relationship between medical lab tests and patient deterioration so we
could identify which medical tests have a major effect on clinical decision making. For example,
the following information is about a patient who was staying at the ICU and was given a medical
test. The following information was recorded at that time:
• Subject_ID: 2
• Hadm_ID: 25967
• IcuStay_ID: 3
• ItemID: 50468
• Charttime: 6/15/2806 21:48
• Value: 0.1
• ValueNum: 0.1
• Flag: abnormal

• ValueUOM: K/uL
Table 2: Labevents Table Description
Name Type Null Comment
SUBJECT_ID NUMBER(7) N Foreign key, referring to a unique patient
identifier
HADM_ID NUMBER(7) Y Foreign key, referring to the hospital
admission ID of the patient
ICUSTAY_ID NUMBER(7) Y ICU stay ID
ITEMID NUMBER(7) N Foreign key, referring to an identifier for the
laboratory test name
CHARTTIME TIMESTAMP(6)
WITH TIME ZONE
N The date and time of the test
VALUE VARCHAR2(100) Y The result value of the laboratory test
VALUENUM NUMBER(38) Y The numeric representation of the laboratory
test if the result was numeric
FLAG VARCHAR2(10) Y Flag or annotation on the lab result to
compare the lab result with the previous or
next result
VALUEUOM VARCHAR2(10) Y The units of measurement for the lab result
value
Table 3: Labitems Table
Name Type Null Comment
ITEMID NUMBER(7) N Table record unique identifier, the lab item
ID
TEST_NAME VARCHAR2(50) N The name of the lab test performed
FLUID VARCHAR2(50) N The fluid on which the test was performed
CATEGORY VARCHAR2(50) N Item category
LOINC_CODE VARCHAR2(7) Y LOINC code for lab item
LOINC_DESCR
IPTION
VARCHAR2(100) Y LOINC description for lab item
6. EXPERIMENTS
We conducted four experiments to fulfil the different approaches to reach our goal of predicting
ICU patient deterioration by mining lab test results. In each experiment, a different dataset
resulted from pre-processing the MIMIC II v2.6 database.
6.1. Experiment 1: Building a Baseline of the Medical Lab Tests Average
1) Experiment Goal: The goal of this experiment was to investigate the effect of lab testing on
predicting patient deterioration. Usually, medical professionals compare the result of the lab
test with a reference range [17]. If the value is not within this range, the patient may face fatal
consequences. Thus, the patient is kept under observation and the test is repeated again
during a specific period. In our experiment, we investigated the average value of the same
repeated test and, more precisely, how the average value of lab results could assist medical
professionals in evaluating patient status.

Since we dealt with real cases, the only way to assess the quality and characteristics of a data
mining model was through the final status of the patient, i.e. whether the patient survived or
not. Thus, our evaluation criterion was how accurately our approach could predict whether
the patient died or not.
2) Building the Dataset: The dataset was constructed by taking the average test result of each
patient for each kind of test and make it one attribute. Thus one patient would be represented
as one instance having 700 attributes, one for each test. If a test was not done, then the value
of that attribute would be 0.
For example, the first patient record in the dataset would look like this:
P_ID Avg1 Avg2 ..... Avg700 Dead/Alive
1 5.3 10 0 D
3) Pre-processing: After building the dataset, some values could not be reported because they
were in text format. We used default values for these types of data. The total number of
attributes was 619 with 2900 instances.
4) Base learners: In our experiment we used five classification algorithms to construct the
model, namely NaiveBayes, SMO, ZeroR, J48 and RandomForest.
5) Evaluation: For a performance measurement, we did a 10-fold cross-validation of the dataset,
and the confusion matrix was obtained to estimate four measures: accuracy, sensitivity,
specificity and F-measure. As a result, RandomForest had the highest accuracy of 77.58%,
followed by SMO with 76.86%, J48 with 75.27%, ZeroR with 70.24% and NavieBayes with
42.96%, as shown in Table 4. RandomForest and SMO have the same F-measures. The
reason for the best performance by RandomForest is that it works relatively well when used
with high-dimensional data with a redundant/noisy set of features [12]
Table 4: Experiment 1 results
Algorithm Learning Machine
Detailed Accuracy
Accuracy
Precision
Recall
F-Measure
Bayes NavieBayes 42.96% 0.672 0.430 0.404
Functions SMO 76.86 % 0.759 0.769 0.762
Rule ZeroR 70.24 % 0.493 0.702 0.580
Tree J48 75.27% 0.749 0.753 0.751
Tree RandomForest 77.58 % 0.765 0.776 0.762
6.2. Experiment 2: Average Medical Lab Tests Feature Selection
1) Experiment Goal: The goal of this experiment was to study the relationship between feature
selection and classification accuracy. Feature selection is one of the dimensionality reduction
techniques for reducing the attribute space of a feature set. More precisely, it determines how
many features should be enough to give moderate accuracy.

2) Building the Dataset: In this experiment we used the same dataset that we used in experiment
1.
3) Pre-processing: In this experiment we built ten datasets depending on the number of selected
features. We start with the first dataset, which contained only 10% of the total attributes.
Then each time, we increased the total feature selections by 10%. For example, dataset 1
contains 10% of the total attributes, dataset 2 contains 20% of the total attributes, dataset 3
contains 30% of the total attributes and so on till dataset 10 contains all 100% of the total
attributes.
For feature selection, we use supervised.attribute. InfoGainAttributeEval from WEKA. This
filter is a wrapper for the Weka class that computes the information gain on a class [18].
• Attribute Subset Evaluator: InfoGainAttributeEval
• Search Method: Ranker.
• Evaluation mode: evaluate all training data
4) Base learner: After generating all of the reduced datasets, we use the J48 algorithm to
construct a model.
5) Evaluation: For each reduced dataset, we applied 10-fold cross-validation for evaluating the
accuracy. Table V shows the results in numbers, and Figure 2 shows them as a chart. The
results indicate that taking only the most related 10% of the total features can give a 75.10%
accurate result, which is comparable to the accuracy of the full feature set. This indicates that
not all of the features are required to get the highest accuracy. However, there are some
fluctuations, such as at 20%, the accuracy drops a little. We conclude that selecting 50 to
80% of the attributes should give moderately satisfying accuracy.
Table 5: Experiment 2 Feature selection.
% of Features Selected
# of Features Selected
Detailed Accuracy
Accuracy
Number
ofleaves
Sizeofthe
Tree
10% 62 75.10% 200 399
20% 124 73.59% 201 401
30% 186 75.10% 185 369
40% 248 74.93% 179 357
50% 310 75.17% 189 377
60% 371 74.79% 187 373
70% 433 75.00% 189 377
80% 495 75.31% 184 367
90% 557 74.97% 183 365
100% 619 74.86% 184 367

Figure 2: Average datasets accuracy.
6.3. Experiment 3: Building a Baseline for the Total Number of Medical Lab Tests
1) Experiment Goal: The goal of this experiment was to investigate the effect of the total
number of lab tests conducted on predicting patient deterioration. Usually, medical
professionals keep requesting the same medical test over a brief period to compare the result
with a reference range [17]. If the value is not within the range, it means the patient may be in
danger, so the test is repeated again and again. Our goal was to predict at what total number a
medical professional should start immediate action and, more precisely, how the total number
of medical lab tests could assist the medical professional in evaluating the patient’s status.
2) Building the Dataset: The dataset was built by taking the total number of tests taken for each
patient for each type of test and make it one attribute. Then one patient would be represented
as one instance having 700 attributes, one for each test. If a test was not done, then the value
of that attribute would be 0.
For example, the dataset would look like this:
P_ID Count1 Count2 … Count700 Dead/Alive
1 5 0 1 D
3) Pre-processing: The dataset was randomized first, then two datasets were generated,
Count_Training_Validation_Dataset and Count_testing_Dataset. This step was repeated ten
times because we used randomization to distribute the instances between the two datasets.
4) Base learners: Five learning algorithms were used to build the model, namely NaiveBayes,
SMO, ZeroR, J48 and RandomForest.
5) Evaluation: The training data were first used to build the model and then evaluated using a
percentage split via test data. For a performance measurement, the confusion matrix was
obtained to estimate four measures: accuracy, sensitivity, specificity and F-measure. Table 6
shows that SMO and RandomForest have almost equal levels of accuracy, around 75%. Even
after testing the model with the test datasets, SMO and RandomForest still have the highest

accuracy among the other techniques. The reason for this higher accuracy is that the amount
of memory required for SMO is linear in the training set size, which allows SMO to handle
very large training sets [11].
Table 6: Experiment 3 results.
Detailed Accuracy
Accuracy
Precision
Recall
F-Measure
Funtions SMO 75.44% 0.739 0.755 0.723
Rule ZeroR 70.46% 0.497 0.705 0.583
Tree J48 73.16% 0.728 0.732 0.692
Tree RandomForest 75.73% 0.742 0.757 0.739
Table 7: Experiment 3 Results
Detailed Accuracy
Accuracy
Precision
Recall
F-Measure
Funtions SMO 74.85% 0.737 0.749 0.716
Rule ZeroR 69.72% 0.486 0.697 0.573
Tree J48 72.44% 0.722 0.724 0.723
Tree RandomForest 75.30% 0.739 0.753 0.736
6.4. Experiment 4: Feature Selection for Total Number of Medical Lab Tests
1) Experiment Goal: The goal of this experiment was to study the relationship between feature
selection and classification accuracy. Feature selection is one of the dimensionality reduction
techniques for reducing the attribute space of a feature set. More precisely, it measures how
many features should be enough to give moderate accuracy.
2) Building the Dataset: In this experiment we used a count dataset.
3) Pre-processing: In the pre-processing step, we built ten datasets depending on the number of
selected features. The first dataset contained only 10% of the total attributes. Then we
increased the total feature selections by 10% with each new dataset. For example, dataset 1
contained 10% of the total attributes, dataset 2 contained 20% of the total attributes, dataset 3
contained 30% of the total attributes and so on till dataset 10 contained all 100% of the total
attributes.

4) For feature selection, we used supervised.attribute. InfoGainAttributeEval from WEKA. This
filter is a wrapper for the Weka class that computes the information gain on a class [18].
• Attribute Subset Evaluator: InfoGainAttributeEval
• Search Method: Ranker.
• Evaluation mode: evaluate on all training data
5) Base learner: After generating all reduced datasets, we used the J48 algorithm as a base
learner.
6) Evaluation: Each feature-reduced dataset went through a 10-fold cross-validation for
evaluation. Figure 3 shows the accuracy of all count datasets. The detail values are also
reported in Table 4. From the results we observe that selecting 60 to 70% of the attributes
gives the highest accuracy. This also concludes that all features (i.e., lab tests) may not be
necessary to attain a highly accurate prediction of patient deterioration.
Table 8: Experiment 4 Results
% of Features Selection
# of Features Selection
Detailed Accuracy
Accuracy
Number
ofleaves
Sizeofthe
Tree
10% 62 71.45% 237 473
20% 124 73.90% 250 499
30% 186 73.55% 247 493
40% 248 72.79% 252 503
50% 310 73.41% 252 503
60% 371 73.66% 254 507
70% 433 74.24% 254 507
80% 495 74.10% 254 507
90% 557 74.14% 265 529
100% 619 73.59% 259 517
Figure 3: Count dataset accuracy.

7. DISCUSSION
It should be noted that the feature selections were done without any domain knowledge and
without any intervention from medical experts. However, in the analysis we would like to
emphasize the merit of feature selection in choosing the best tests, which could be further verified
and confirmed by a medical expert.
First we compare the selected features selected from the two datasets, namely the average dataset
and the count dataset. Table 9 shows the 10 best features chosen by the two approaches and
highlights the common lab tests between the two approaches (i.e. using the average of tests and
count of tests). Table 10 shows more details about the common tests.
Table 9: Final Results
Detailed Accuracy
Average Dataset Count Dataset
Best ranked 10 from the 10% of selected features
50177
50090
50060
50399
50386
50440
50408
50439
50112
50383
50148
50112
50140
50399
50177
50439
50090
50440
50079
50068
Table 10: Medical Lab Test Details.

LOINC is an abbreviation for logical observation identifiers names and codes. LOINC is clinical
terminology important for laboratory test orders and results [19]. ARUP Laboratories [20] is a
national clinical and anatomic pathology reference laboratory and a worldwide leader in
innovative laboratory research and development. We used their web page and others to clarify
more about the medical lab tests in table 10 as follows:
• UREAN (50177): This test is conducted using the patient’s blood. This test is
recommended to screen for kidney dysfunction in patients with known risk factors (e.g.
hypertension, diabetes, obesity, family history of kidney disease). The panel includes
albumin, calcium, carbon dioxide, creatinine, chloride, glucose, phosphorous, potassium,
sodium and BUN and a calculated anion gap value. Usually, the result is reported within
24 hours [20].
• CREAT (50090): This test is conducted using the patient’s blood. It is a screening test to
evaluate kidney function [20].
• INR(PT) (50399): This test is conducted using the patient’s blood by coagulation assay
[13].
• PTT (50440): This test is carried out to answer two main questions: does the patient have
antiphospholipid syndrome (APLS), and does the patient have von Willebrand disease? If
so, which type? It is carried out by mechanical clot detection [21].
• PT (50439): This test is conducted using the patient’s blood by coagulation assay [13].

• GLUCOSE (50112): This test is used to check glucose, which is a common medical
analytic measured in blood samples. Eating or fasting prior to taking a blood sample has
an effect on the result. Higher than usual glucose levels may be a sign of prediabetes or
diabetes mellitus [22].
• The result of the top 10 selected features from the average dataset allows us to build a
model using decision tree J48. This model would allow a medical professional to predict
the status of a patient in the ICU as follows:
For example, if the lab test (name: PTT, ID 50440, LOINC: 3173-2) result value is <=
20.757143, then the probability is very high (772.0/22.0~ 97.2%) that the patient is going to die
(class:1). This model has 78.6897% overall accuracy.
8. CONCLUSION AND FUTURE WORK
In this paper, we presented our proposed approach to reduce the observation time in the ICU by
predicting patient deterioration in its early stages. In our work, we presented experiments 1 and 3
to build a model to predict patient deterioration. Experiments 2 and 4 identified the most
important medical lab tests, then highlighted the common tests between the two datasets. The
four experiments would help medical professionals to take better decisions in a very short time.
For future work, the authors are planning to carry out more experiments using bigger data. Big
data analytics would bring potential benefits to support taking the right decision to enhance the
efficiency, accuracy and timeliness of clinical decision making in the ICU.
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AUTHORS
Noura Al Nuaimi is pursuing a PhD in Information Technology with Dr Mohammad Mehedy Masud at
United Arab Emirates University (UAEU). She holds an MSc in Business Administration from Abu Dhabi
University and a BSc in Software Engineering from UAEU. Her research interests focus on data mining
and knowledge discovery, cloud computing, health information systems, search engines and natural
language processing. She has published research papers in IEEE Computer Society and IEEE Xplore.
Dr Mohammad Mehedy Masud is currently an Assistant Professor at the United Arab Emirates University
(UAEU). He joined the College of Information Technology at UAEU in spring 2012. He received his PhD
from University of Texas at Dallas (UTD) in December 2009. His research interests are in data mining,
especially data stream mining and big data mining. He has published more than 30 research papers in
journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), Journal of Knowledge
and Information Systems (KAIS), ACM Transactions on Management Information Systems (ACM TMIS)
and peer-reviewed conferences including IEEE International Conference on Data Mining (ICDM),
European Conference on Machine Learning (ECML/PKDD) and Pacific Asia Conference on KDD. He is
the principal inventor of a US patent application and lead author of the book “Data Mining Tools for

Malware Detection”. Dr Masud has served as a program committee member of several prestigious
conferences and has been serving as the official reviewer of several journals, including IEEE TKDE, IEEE
TNNLS and DMKD. During his service at the UAEU he has secured several internal and external grants as
PI and co-PI.
Farhan Mohammed is a graduate from the College of Information Technology in United Arab Emirates
University specializing in Information Technology Management. He obtained his Bachelor’s in
Management Information Systems from United Arab Emirates University, Al Ain, UAE. He has worked
under several professors and published four conference papers and a journal paper for IEEE sponsored
conferences. Currently he is working as a research assistant in data mining in the health industry to develop
models on health deterioration prediction. His area of interests lies in smart cities, UAVs, data mining, and
image and pattern recognition.

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