=Paper=
{{Paper
|id=Vol-1520/paper19
|storemode=property
|title=Case-Based Reasoning as a Prelude to Big Data Analysis: A Case Study
|pdfUrl=https://ceur-ws.org/Vol-1520/paper19.pdf
|volume=Vol-1520
|dblpUrl=https://dblp.org/rec/conf/iccbr/MarlingBBS15
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==Case-Based Reasoning as a Prelude to Big Data Analysis: A Case Study==
https://ceur-ws.org/Vol-1520/paper19.pdf
175
Case-Based Reasoning as a Prelude to Big Data
Analysis: A Case Study
Cindy Marling1 , Razvan Bunescu1 ,
Babak Baradar-Bokaie2 and Frank Schwartz2
1
School of Electrical Engineering and Computer Science
Russ College of Engineering and Technology
Ohio University, Athens, Ohio 45701, USA
marling@ohio.edu, bunescu@ohio.edu
2
Department of Specialty Medicine
Heritage College of Osteopathic Medicine
Ohio University, Athens, Ohio 45701, USA
babak.bokaie@gmail.com, schwartf@ohio.edu
Abstract. The 4 Diabetes Support System (4DSS) is a prototypical hy-
brid case-based reasoning (CBR) system that aims to help patients with
type 1 diabetes on insulin pump therapy achieve and maintain good
blood glucose control. The CBR cycle revolves around treating blood
glucose control problems by retrieving and reusing therapeutic adjust-
ments that have been effectively used to treat similar problems in the
past. Other artificial intelligence (AI) approaches have been integrated
primarily to aid in situation assessment: knowing when a patient has a
blood glucose control problem and characterizing the type of problem
that the patient has. Over the course of ten years, emphasis has shifted
toward situation assessment and machine learning approaches for pre-
dicting blood glucose levels, as that is the area of greatest patient need.
The goal has been to make large volumes of raw insulin, blood glucose
and life-event data actionable. During the past year, newly available fit-
ness bands have provided a potentially valuable source of additional data
for controlling diabetes. Because it was initially unclear whether or how
this new data might be leveraged, a case study was conducted, and CBR
was once again called into play. This paper describes the case study and
discusses the potential of CBR to serve as a prelude to big data analysis.
1 Introduction
The World Health Organization estimates that there are 347 million people living
with diabetes [11]. From 5 to 10% of them have type 1 diabetes (T1D), the
most severe form, in which the pancreas fails to produce insulin. T1D is neither
curable nor preventable; however, it can be treated with insulin and effectively
managed through blood glucose (BG) control. Good BG control helps to delay
or prevent long-term diabetic complications, including blindness, amputations,
Copyright © 2015 for this paper by its authors. Copying permitted for private and
academic purposes. In Proceedings of the ICCBR 2015 Workshops. Frankfurt, Germany.
176
kidney failure, strokes, and heart attacks [4]. Therefore, it is important to rapidly
identify and correct BG control problems.
The 4 Diabetes Support System (4DSS) is a prototypical hybrid case-based
reasoning (CBR) system that detects and predicts BG control problems and
suggests personalized therapeutic adjustments to correct them. The 4DSS has
been extensively described in the literature [6, 7, 9]; a brief system overview is
presented in the next section. A critical research thrust that grew out of work on
the 4DSS is how to continuously, in real-time, predict that a BG control problem
is about to occur. The key is to be able to accurately predict what the BG level
will be in the next 30 to 60 minutes, which would allow enough time to intervene
and prevent predicted problems. Large volumes of raw insulin and BG data are
available for analysis. Machine learning algorithms for time series prediction have
been efficaciously applied. Studies conducted on retrospective data show that our
system predicts BG levels comparably to physicians specializing in diabetes care,
but not yet well enough for use by patients in the real world [2, 8].
Recently, commercially available fitness bands and smart watches, such as
the Basis Peak, Nike Fuelband, Fitbit, and Apple Watch, have made it practical
to inexpensively and unobtrusively collect large quantities of physiological data.
As this data may be indicative of patient activity impacting BG levels, it could
potentially be used to improve BG level prediction. However, due to the compli-
cated nature of the problem, it was not initially clear whether or how this data
could be leveraged. Therefore, a case study was conducted in which a patient
with T1D wore a fitness band in addition to his usual medical devices for two
months. The data was consolidated and displayed via custom visualization soft-
ware. The patient, his physician, and artificial intelligence (AI) researchers met
weekly to review and interpret the data, using a protocol like that employed to
build the 4DSS. This case-based focus shed light on how the new data could be
integrated into machine learning models and leveraged to improve BG predic-
tion. We posit that a case-based approach is especially useful in dealing with new
data sources, new patients, and new medical conditions, and that early lessons
learned through the CBR process can aid in later big data analysis.
2 Background
This section briefly describes the 4DSS and the work on machine learning models
for blood glucose prediction prior to the new case study.
2.1 The 4 Diabetes Support System
A graphical overview of the prototypical hybrid CBR system is shown in Figure
1. The patient provides BG, insulin and life-event data to the system. BG and
insulin data are uploaded from the patient’s prescribed medical devices. The
patient enters data about life events that impact BG levels, such as food, exer-
cise, sleep, work, stress and illness, using a smart phone. The data is scanned by
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Situation Assessment Module
Problem Detection
Glycemic Variability
Classification
Blood Glucose Prediction
Case Base of
Blood Glucose
Control Problems
Blood Glucose, Case Retrieval
Insulin and Life Module
Event Database
Adaptation
Module
Recommended Therapeutic Adjustment
Patient Physician
Fig. 1. Overview of the 4 Diabetes Support System, reproduced from [6]
the situation assessment module, which detects and predicts BG control prob-
lems. The most critical types of problems are: hyperglycemia, or high BG, which
contributes to long-term diabetic complications; and hypoglycemia, or low BG,
which may result in severe immediate reactions, including weakness, dizziness,
seizure or coma. The situation assessment module reports detected problems to
the physician. The physician selects a problem of interest, which is then used by
the case retrieval module to obtain the most similar case or cases from the case
base. Each retrieved case contains a specific BG control problem experienced by
a T1D patient, a physician’s recommended therapeutic adjustment, and the clin-
ical outcome for the patient after making the therapeutic adjustment. Retrieved
cases go to the adaptation module, which personalizes a retrieved solution to fit
the specific needs of the current patient. A solution is a therapeutic adjustment
comprising one or more actions that a patient can take. Adapted therapeutic
adjustments are displayed to the physician as decision support. The physician
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decides whether or not to recommend the therapeutic adjustments to the pa-
tient. Directly providing suggestions to the patient, while a long-term goal, would
require regulatory approval.
2.2 Machine Learning Models for Blood Glucose Prediction
Originally conceived of as important for situation assessment, BG prediction
has multiple potential applications that could enhance safety and quality of life
for people with T1D if incorporated into medical devices. These applications in-
clude: alerts to warn of imminent problems; decision support for taking actions to
prevent impending problems; “what if” analysis to project the effects of lifestyle
choices on BG levels; and integration with closed-loop control algorithms for
insulin pumps (aka the “artificial pancreas”). Predicting hypoglycemia is espe-
cially important, both for patient safety and because hypoglycemia is a limiting
factor for intensive insulin therapy [3].
In our BG prediction approach, a generic physiological model is used to
generate informative features for a Support Vector Regression (SVR) model that
is trained on patient specific data. The physiological model characterizes the
overall dynamics into three compartments: meal absorption, insulin dynamics,
and glucose dynamics. The parameters of the physiological model are tuned to
match published data and feedback from physicians. To account for the noise
inherent in the data, the state transition equations underlying the continuous
dynamic model are incorporated in an extended Kalman filter.
Fig. 2. Overview of the Blood Glucose Level Prediction Process
Figure 2 shows the overall BG level prediction process. A continuous dynam-
ical system implementing the set of physiological equations is run in prediction
mode for 30 and 60 minutes. Physiological model predictions are then used as
features for an SVR model that is trained on the two weeks of data preced-
ing the test point. Furthermore, an AutoRegressive Integrated Moving Average
(ARIMA) model is trained on the same data and its predictions are used as ad-
ditional features. The models are trained to minimize Root Mean Square Error
(RMSE). SVR predictions are made at 30 and 60 minute intervals and compared
to BG levels at prediction time (t0 ), ARIMA predictions, and predictions made
by physicians specializing in diabetes care. Results are shown in Figure 3.
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Fig. 3. RMSE of the Best SVR Model vs. the t0 , ARIMA, and Physician Predictions
3 The Case Study
The recent proliferation of commercially available fitness bands provides an op-
portunity to exploit new data from inexpensive, unobtrusive, portable physiolog-
ical sensors. These sensors provide signals indicative of activities that are known
to impact BG levels, including sleep, exercise and stress. The hope is that, by
incorporating these signals, we can obtain a more accurate picture of patient
activity, while reducing or eliminating the need for the patient to self-report life
events. We conducted an N-of-1 study in order to learn whether or how this
influx of new data could be leveraged to advantage.
The subject was a middle-aged physician who has had T1D since childhood.
For two months, he wore a fitness band along with his regularly prescribed
medical devices and entered life-event data via his smart phone. The fitness
band, a Basis Peak, provided data for galvanic skin response (GSR), heart rate,
and skin and air temperatures. The medical devices, a Medtronic insulin pump
and a Dexcom continuous glucose monitoring (CGM) system, provided insulin
and BG data. All of this data was consolidated in the 4DSS database.
Once a week, the subject met with his physician and AI researchers to review
and analyze the data. The consolidated data was displayed via custom-built visu-
alization software called PhysioGraph. A screen shot from PhysioGraph, showing
the different types of data, is shown in Figure 4. BG control problems identified
by the 4DSS software, the subject, and/or his physician, were visualized and dis-
cussed. We looked for visual patterns in the fitness band data during the times
when the problems occurred.
Preliminary findings based on these visualizations were encouraging. While
even subtle patterns may be detected by machine learning algorithms, we were
able to detect some marked patterns as humans. The most pronounced pattern
was a rise in GSR with severe hypoglycemia. The most interesting pattern re-
volved around shoveling snow. The study was conducted during an unusually
harsh winter in which the patient (and most of the rest of us) had to frequently
shovel heavy snow. Shoveling snow is strenuous exercise, and exercise is known
to lower BG levels. After shoveling for extended periods, the subject sometimes
experienced hypoglycemia. This is a problem we would like to predict, because,
if alerted, the patient could take action to prevent it. There was a discernable
pattern in the fitness band data surrounding this problem. GSR and heart rate
rose, while skin and air temperature dropped. While we do not yet know if this
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Fig. 4. A Screen Shot from PhysioGraph. Data from the fitness band is displayed in section (a). This includes GSR (green), heart rate
(red), skin temperature (gold) and air temperature (cyan). Icons indicating life events appear in section (b). These icons are clickable in
PhysioGraph to view event details. BG data and insulin data are shown in section (c). The dotted curve (blue) represents CGM data,
while the solid curve (red) models the total insulin in the patient’s system.
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combination of signals will allow us to predict hypoglycemia or only detect it,
we now have leads to follow.
4 Discussion
When work began on the 4DSS in 2004, we had little data and no structured
cases. As we built our case base and collected data from over 50 T1D patients,
we developed a database that enabled us to build machine learning models for
purposes we did not envision in 2004. With more data, it becomes possible
to leverage more AI approaches for more purposes. When data or knowledge
is limited, however, CBR can be an enabling approach. As non-health-related
examples, we can think of leveraging all of the information possible from a single
oil spill, or of basing product recommendations for a customer with no purchase
history on what similar customers have bought. In the medical arena, CBR
has also proven useful for dealing with new situations. For example, CARE-
PARTNER used CBR to determine appropriate follow-up care for the earliest
stem cell transplant patients [1]. Once many patients had undergone stem cell
transplantation and received follow-up care, their collective experiences were
distilled into clinical practice guidelines that were used in lieu of CBR. Today,
nearly 20 years later, big data tools like IBM Watson Health [5] may allow us to
further evaluate, refine, and personalize treatment for these patients.
In the diabetes domain, big data is not yet publicly available; however, we an-
ticipate its near-term future availability. Three non-technical factors contribute
to this lack of data: (1) most diabetes patients do not yet wear devices or use
systems that continuously collect data; (2) medical device manufacturers do not
yet allow access to raw data in real-time, but require the use of their own pro-
prietary software; and (3) patient privacy concerns inhibit data sharing, even
when data exists. There has been a recent drive to collect and consolidate data
from all T1D patients and all types of (currently incompatible) medical devices,
spearheaded by the non-profit organization Tidepool [10]. A goal is to be able to
analyze and leverage continuous data from hundreds of thousands of patients to
improve diabetes care and outcomes for individuals. Our case engineering, based
on the limited data we have already collected, could serve to jump start such
efforts.
At the heart of any CBR system is the case. The case is a knowledge struc-
ture that, especially in complex medical domains, may embody more than a
collection of readily available feature-value pairs. The design of a case for a CBR
system begins with the analysis of real-world cases to identify problems, solutions
and outcomes. It is necessary to understand and define the features that make
cases similar to each other, reusable in different circumstances, and adaptable
to the case at hand. Features engineered for cases may provide machine learning
algorithms with better inputs than raw data or surface features.
In our case study, the fitness band provided physiological signals that were
not a part of our original case design. There were 20 times as many data points
per patient per day as we had previously collected. We did not know how the
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new data could be used to anticipate or detect blood glucose control problems
or, more fundamentally, how it related to BG levels in people with T1D. The
inputs to our SVR models are not raw data points, but features that have been
carefully engineered from the raw data based, in part, on insights gained from
cases. Sometimes, simple data combinations suffice; for example, we could see
during the case study that the difference between air and skin temperature was
more relevant than either individual measurement. Other times, we have had
to employ complex systems of equations; for example, a complex physiological
model is needed to characterize the impact of insulin on BG levels. Even as we
move toward more automated means of feature engineering and more reliance
on machine learning techniques, early use of CBR can help to provide insight
and intuition that may guide big data exploration and interpretation.
5 Summary and Conclusion
The 4DSS is a prototypical hybrid CBR system that aims to help T1D patients
achieve and maintain good BG control. As cases and data have accumulated over
ten years, the research emphasis has shifted toward using the accumulated data
to build machine learning models for BG prediction. While these models have
applicability to situation assessment within the 4DSS, their greater potential is in
facilitating a wide range of practical applications that could enhance safety and
quality of life for T1D patients. The recent proliferation of commercially available
fitness bands has presented the opportunity to incorporate new types of data
indicative of patient activity into the models to improve prediction accuracy.
However, when it was initially unclear whether or how this new data might be
leveraged, a case study was conducted, calling CBR back into play.
In the N-of-1 study, a T1D patient on insulin pump therapy with continuous
glucose monitoring wore a fitness band and entered life-event data into the 4DSS
database for two months. The aggregated data was displayed via custom-built
visualization software and reviewed at weekly intervals by the patient, his physi-
cian, and AI researchers. BG control problems were analyzed with a focus on
identifying patterns in the new data at the time the problems occurred. Some
promising patterns could be visualized, including a marked rise in GSR with
severe hypoglycemia. This case-based focus provided insight and intuition about
how the new data relates to BG levels. Work on integrating the new data into
our BG prediction models is currently underway.
6 Acknowledgments
This material is based upon work supported by the National Science Foundation
under Grant No. 1117489. Additional support was provided by Medtronic and
Ohio University. We gratefully acknowledge the contributions of diabetologist
Jay Shubrook, DO, graduate research assistants Kevin Plis and Samson Xia, and
Research Experience for Undergraduates (REU) participants Hannah Quillin
and Charlie Murphy.
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