Synergistic case-based reasoning in medical domains
, Stefania Montani
, Isabelle Bichindaritz
, Peter Funk
School of Electrical Engineering and Computer Science, Ohio University, Athens, OH 45701, USA
DISIT, Computer Science Institute, Università del Piemonte Orientale, 15121 Alessandria, Italy
Computer Science Department, State University of New York at Oswego, Oswego, NY 13126, USA
School of Innovation, Design and Engineering, Mälardalen University, SE-721 23 Västerås, Sweden
Clinical decision support systems
This paper presents four synergistic systems that exemplify the approaches and beneﬁts of case-based
reasoning in medical domains. It then explores how these systems couple Artiﬁcial Intelligence (AI)
research with medical research and practice, integrate multiple AI and computing methodologies, lever-
age small numbers of available cases, reason with time series data, and integrate numeric data with con-
textual and subjective information. The following systems are presented: (1) CARE-PARTNER, which
supports the long-term follow-up care of stem-cell transplantation patients; (2) the 4 Diabetes Support
System, which aids in managing patients with type 1 diabetes on insulin pump therapy; (3) Retrieval of
HEmodialysis in NEphrological Disorders, which supports hemodialysis treatment of patients with end
stage renal disease; and (4) the Mälardalen Stress System, which aids in the diagnosis and treatment
of stress-related disorders.
Ó2013 Elsevier Ltd. All rights reserved.
Case-based reasoning (CBR) systems have long found fertile
ground in health sciences domains (Begum, Ahmed, Funk, Xiong,
& Folke, 2011; Bichindaritz & Marling, 2010; Montani, 2008). Eight
international Workshops on CBR in the Health Sciences have high-
lighted the challenges and showcased the applications of CBR in
biomedical ﬁelds. At the 2012 workshop, held at the International
Conference on Case-Based Reasoning (ICCBR-12) in Lyon, several
exemplary systems were featured (Lamontagne & Recio-García,
2012). In the spirit of CBR, which promotes reasoning and learning
from concrete examples, four of these systems were selected as
cases of medical CBR systems. These systems are:
CARE-PARTNER, which supports the long-term follow-up care
of stem-cell transplantation patients.
The 4 Diabetes Support System, which aids in managing
patients with type 1 diabetes on insulin pump therapy.
Retrieval of HEmodialysis in NEphrological Disorders, which
supports hemodialysis treatment of patients with end stage
The Mälardalen Stress System, which aids in the diagnosis and
treatment of stress-related disorders.
In this paper, we present each of these systems in turn. Then we
explore the synergies enabled and exempliﬁed by these systems.
We ﬁnd a tight coupling of Artiﬁcial Intelligence (AI) research with
medical research and practice. We see integration of multiple AI
and computing technologies. We ﬁnd that complex domains de-
mand complex knowledge structures. We identify a need to fully
leverage small numbers of available cases. We encounter time ser-
ies data and develop new ways to harness it for reasoning. We inte-
grate numeric data, including biosensor signal data, with
contextual life-event data and subjective patient perceptions. In
essence, the synergistic intertwining of CBR and medicine in these
systems has led to new insights in both CBR research and develop-
ment and medical practice. It is our hope that these experiences
will be retrieved, reused, revised and retained (Aamodt & Plaza,
1994) for future CBR research and system development.
CARE-PARTNER is a decision support system for the long-term
follow-up of oncology patients who have undergone stem cell
transplantation (Bichindaritz, Kansu, & Sullivan, 1998). This system
was built between 1996 and 2000 at the Fred Hutchinson Cancer
Research Center, at the University of Washington, in Seattle. Three
physicians, Keith Sullivan, Paul Martin, and Emin Kansu, and a phy-
sician assistant, Muriel Siadak, served as the domain experts. While
CARE-PARTNER is no longer an active project, the ideas it pio-
neered have been carried over into the ongoing Mémoire project
0957-4174/$ - see front matter Ó2013 Elsevier Ltd. All rights reserved.
Corresponding author. Tel.: +1 740 593 1246.
E-mail addresses: email@example.com (C. Marling), firstname.lastname@example.org
(S. Montani), email@example.com (I. Bichindaritz), firstname.lastname@example.org (P. Funk).
Expert Systems with Applications xxx (2013) xxx–xxx
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(Bichindaritz, 2006, 2007) and have inﬂuenced numerous other
CBR systems in health sciences domains.
2.1. CARE-PARTNER system functionality and goals
CARE-PARTNER assists clinicians with the long-term follow-up
(LTFU) of stem cell transplant patients once they have returned
to their home communities. It provides online answers to ques-
tions from home care providers, who previously had to telephone
nurses, who would then relay their questions to LTFU clinicians be-
fore getting back to them with clinical answers. The electronic con-
tact management system developed to replace the old phone and
paper-based system provides advantages for research and docu-
mentation and serves as an example of a medical knowledge man-
CARE-PARTNER’s decision-support is based upon proven and
validated practice, helping to implement evidence-based medicine
(Sullivan et al., 1997). It provides the following types of decision
Interpretation for each laboratory test and procedure result.
List of differential diagnoses, ranked by likelihood; these diag-
noses are often not incompatible, since several diagnoses co-
occur to cover all the signs and symptoms exhibited by the
List of steps of laboratory tests and/or procedures for diagnostic
List of steps of planning actions for treatment.
List of pertinent documents hyperlinked to the previous ele-
ments, such as guidelines, or textbook excerpts.
An important system requirement for CARE-PARTNER is the
management of risk. A physician not specialized in a domain
may not be able to critique or challenge system advice, and may
not notice even severe mistakes. In the domain of stem-cell trans-
plantation, transplant complications were quite unfamiliar to
home care providers. These complications can be rapidly life-
threatening, thus imposing very high standards of safety to protect
patients. Therefore, the reliability and safety of the system were of
2.2. CARE-PARTNER system design
Fig. 1 shows CARE-PARTNER’s reasoning cycle. This multimodal
reasoning cycle combines case-based reasoning, rule-based rea-
soning, and information retrieval. CARE-PARTNER’s reasoning
steps are generalizations of the steps deﬁned in these respective
methodologies. The cooperation of the different knowledge
sources is driven by the LTFU domain, in which, as in most medical
domains, knowledge takes several forms:
1. Practice guidelines: A practice guideline is composed of sys-
tematically developed textual statements, designed for practi-
tioners and patients, which will be helpful in making clinical
decisions on the prevention, diagnosis, treatment and manage-
ment of selected conditions. Guidelines are represented as rules
that are embedded in prototypical cases.
2. Practice pathways: A practice pathway covers the same type
of knowledge elements as a practice guideline, but it is spe-
cialized to the LTFU domain. While practice guidelines are rep-
resented via text, practice pathways are expressed in the
knowledge representation formalism of the decision support
system. Practice pathways were created by a group of LTFU
experts exclusively for the CARE-PARTNER system. Pathways
correspond to prototypical cases, and they are represented as
cases in the system.
3. Practice cases: A practice case is an example of a problem-solv-
ing situation as solved by an expert or possibly a group of
experts. It is essentially a real patient problem-solving situa-
tion, and not a prototypical one as for a practice guideline or
a practice pathway. It is represented as a case in the system.
4. Medical textbooks: A collection of documents serves as docu-
mentation and explanation during the reasoning process, often
in hyperlinked form.
Intensive knowledge elicitation efforts were required to build
the case base around a knowledge base of the domain. It was deter-
mined early on in the project that cases were not available in elec-
tronic format at a level of detail required for CBR. For instance, the
patient database did not include patient treatments, or most of the
signs and symptoms, but only the main events abstracted from the
paper charts. The project team had to come up with prototypical
cases to bootstrap the system, which took over two years to devel-
op at a level of thoroughness and consistency needed to achieve
high quality decision support.
This system was unique because its proposed recommenda-
tions spanned not only diagnosis, but also lab result interpreta-
tion, and treatment planning. An extensive ontology was
developed including 1109 diseases, 452 functions (also known
as signs and symptoms), 1152 labs, 547 procedures, 2684 med-
ications, and 460 sites. Most of the terms naming these objects
were standardized using the Uniﬁed Medical Language System
(UMLS) semantic network (National Library of Medicine,
1995). Only terms not corresponding to objects in the UMLS
were added to the domain speciﬁc ontology. In particular, the
planning actions used in the treatment part of a prototypical
case did not exist in the UMLS and were all created for the
The cornerstone of the knowledge acquisition process was the
conception of prototypical cases, or clinical pathways. The 91
implemented clinical pathways primarily correspond to clinical
diagnostic categories, with some of them corresponding to essen-
tial signs and symptoms requiring speciﬁc assessment or treat-
ment actions. The clinical pathways are knowledge structures
represented using the ontology described above. A prototypical
case comprises three parts:
1. A list of ﬁndings, corresponding to signs and symptoms.
2. A diagnosis assessment plan, which is a plan to follow for con-
ﬁrming (or informing) the suspected diagnosis.
3. A treatment/solution plan, which is a plan to follow for treating
this disease when conﬁrmed, or a solution when the pathway
does not correspond to a disease.
The diagnosis assessment part and the treatment part of the
case can also be viewed as simpliﬁed algorithms, since they use
if-then-else structures, loop structures, and sequence structures of
actions in time. When instantiated with an actual patient’s data,
this provides a diagnosis assessment plan or treatment plan tai-
lored to the speciﬁc patient. In this way, the knowledge structure
allows for sophisticated adaptation when reusing a prototypical
2.3. CARE-PARTNER evaluation
Table 1 shows the results of an evaluation in which two expert
clinicians rated the system using the scale Fails to Meet Standards/
Adequate/Meets All Standards (Bichindaritz, 2006). This evaluation
covered 163 different clinical situations or cases, corresponding
to contacts between the system and a clinician, for three patients.
As seen in Table 1, the system was rated 82.2% of the time as Meets
all Standards and 12.3% of the time as Adequate, for a total of 94.5%
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Please cite this article in press as: Marling, C., et al. Synergistic case-based reasoning in medical domains. Expert Systems with Applications (2013), http://
of results judged to be at least clinically acceptable by the medical
experts. Table 1 also shows that the advice provided by the system
covers most of the clinicians’ tasks: lab results interpretation, pro-
cedure results interpretation, diagnosis, treatment, and pathways
Another part of the evaluation dealt with measuring the pro-
gress of the system when solving new contact cases. This ability
of the system to learn was evaluated on the complete charts of
three different patients. The performance of the system signiﬁ-
cantly improved between patients 1 and 3 to reach 98.6% accept-
ability for the 54 contacts in the third patient’s chart
(Bichindaritz, 2006). Since the Therac-25 accidents had recently
shown that not even 100% reliability was sufﬁcient to ensure pa-
tient safety (Leveson & Turner, 1993), the system was further ex-
tended to include a safety control module capable of referring
cases requiring particular attention to direct clinician supervision.
3. The 4 Diabetes Support System (4DSS)
The 4 Diabetes Support System (4DSS) aims to assist patients
with type 1 diabetes (T1D) and their professional caregivers
(Marling, Shubrook, & Schwartz, 2009, 2011a, 2012). Work on this
system began in 2004 and continues to this day. There are two do-
main experts: Frank Schwartz, an endocrinologist; and Jay Shu-
brook, a diabetologist. They are practicing clinicians, who have
treated several hundred T1D patients, as well as faculty members
of the Ohio University Heritage College of Osteopathic Medicine.
3.1. The diabetes management domain
There are an estimated 346 million people worldwide who have
diabetes (World Health Organization, 2012). Approximately 20
million of them have type 1 diabetes, the most severe form, in
which the pancreas fails to produce insulin. Because insulin is an
essential hormone needed to convert food into energy, T1D pa-
tients depend upon external supplies of insulin. The T1D patients
involved in the 4DSS project are treated with insulin pump ther-
apy. An insulin pump continuously infuses the patient with basal
insulin. The patient may instruct the pump to deliver additional
insulin boluses to account for meals or blood glucose excursions.
While diabetes can not yet be cured, it is actively managed
through blood glucose control. Good blood glucose control is
known to help delay or prevent long-term diabetic complications,
including blindness, amputations, kidney failure, strokes, and heart
attacks (Diabetes Control & Complications Trial Research Group,
1993). Effective blood glucose control entails vigilant self-monitor-
ing of blood glucose levels. T1D patients prick their ﬁngers from 4
to 6 times a day and use glucometers to measure their blood. They
may also wear continuous glucose monitoring (CGM) devices,
which produce blood glucose readings every 5 min.
Blood glucose monitoring data is relayed to physicians, who
must manually interpret it to ﬁnd blood glucose control problems
and recommend appropriate therapeutic adjustments. While volu-
minous blood glucose data contributes to data overload for
Reuse Conflict resolution
Cases, Pathw ays,
Fig. 1. CARE-PARTNER’s Reasoning Cycle.
CARE-PARTNER system evaluation results.
Fails to meet
Labs 54 3.7 3.7 92.6
Procedures 67 8.9 3.0 88.1
Diagnosis 68 16.2 13.2 70.6
Treatment 71 9.9 11.3 78.8
Pathways 47 8.5 8.5 83.0
Total 163 5.5 12.3 82.2
C. Marling et al. / Expert Systems with Applications xxx (2013) xxx–xxx 3
Please cite this article in press as: Marling, C., et al. Synergistic case-based reasoning in medical domains. Expert Systems with Applications (2013), http://
physicians, data concerning life events that impact blood glucose
levels is not routinely maintained. Physicians may feel, paradoxi-
cally, that they have too much data and yet not enough data at
the same time.
3.2. 4DSS system overview
The 4DSS is a hybrid case-based reasoning (CBR) system that
detects problems in blood glucose control and suggests personal-
ized therapeutic adjustments to correct them. Fig. 2 shows a graph-
ical overview of the 4DSS. The system is data driven. Blood glucose
data comes from glucometers and CGM devices. Insulin data comes
from the patient’s pump. The patient uploads blood glucose and
insulin data to Medtronic’s proprietary CareLink system (Medtron-
ic, 2012), where it is extracted and transferred to the 4DSS data-
base. The patient manually enters data about life events that
impact blood glucose levels, including food, exercise, sleep, work,
stress and illness. Originally entered via computer-based browsers,
life-event data is now entered via smart phones.
The situation assessment module scans patient data. Tradition-
ally in CBR systems, situation assessment begins with a static
description of the current problem. It is different in this domain,
because blood glucose control problems continue over time, and
because patients are not necessarily aware of problems when they
occur. The 4DSS situation assessment module has three major
components: problem detection, glycemic variability classiﬁcation,
and blood glucose prediction. These components were built using
rule-based reasoning, machine learning algorithms, and time series
prediction techniques, giving the 4DSS its hybrid character.
The problem detection component contains 18 rule-based rou-
tines that incorporate physician strategies for ﬁnding problems in
patient data. At a high level, these routines look for problems
involving: (1) hyperglycemia, or high blood glucose, which contrib-
utes to long-term diabetic complications; (2) hypoglycemia, or low
blood glucose, which may result in severe immediate reactions,
including weakness, dizziness, seizure or coma; (3) ﬂuctuations be-
tween hyper-and hypoglycemia; and (4) lapses in diabetes self-
The glycemic variability classication component assesses prob-
lems involving blood glucose ﬂuctuation. It detects the problem of
excessive glycemic variability, which is believed to presage long-
term complications caused by oxidative stress. When expert rules
proved inadequate for detecting this problem, machine learning
algorithms, including multi-layer perceptrons and support vector
machines, were introduced. These algorithms classify 24-h blood
glucose plots by variability level to match physician gestalt percep-
tion of such plots.
The blood glucose prediction component, currently under con-
struction, incorporates time series prediction techniques to antici-
pate problems before they occur. While blood glucose data is not
currently available in real time and must be scanned retrospec-
tively, we are preparing for its near-term availability. Predicting
problems even 30 min in advance would give patients time to take
preventative actions, enhancing patient safety.
The situation assessment module reports the problems it ﬁnds
to a physician, who must then select problems of interest. A se-
lected problem triggers the case retrieval module of the 4DSS.
The case retrieval module obtains the most similar cases from
the 4DSS case base.
The case base includes 80 cases, each of which contains a spe-
ciﬁc blood glucose control problem experienced by a T1D patient,
a physician’s recommended therapeutic adjustment for that prob-
lem, and the clinical outcome for the patient after making the ther-
apeutic adjustment. These cases were compiled during clinical
research studies in which: (1) T1D patients contributed blood glu-
cose, insulin and life event data; (2) physicians manually identiﬁed
Fig. 2. Overview of the 4 Diabetes Support System.
4C. Marling et al. / Expert Systems with Applications xxx (2013) xxx–xxx
blood glucose control problems and recommended solutions to pa-
tients; (3) patients made the recommended therapeutic adjust-
ments (or not); and (4) physicians examined subsequent patient
data to determine the efﬁcacy of the solutions.
To retrieve the most similar cases from the case base, the case
retrieval module employs a traditional two-step process. First, a
subset of potentially similar cases is identiﬁed, and then the near-
est neighbors are selected from that subset. In the ﬁrst step, cases
are partitioned by problem type. In the second step, a standard
nearest neighbor metric is used. Domain speciﬁc similarity func-
tions are combined with empirically determined weights to obtain
an overall score for each case. Cases scoring above a similarity
threshhold are forwarded to the adaptation module.
The adaptation module personalizes a solution from a retrieved
case to ﬁt the situation of a current patient. It begins with the most
similar case, but if the solution in that case is not adaptable, it con-
siders the next most similar case, and so on. A solution is a thera-
peutic adjustment composed of one or more actions that a patient
can take. During adaptation, individual actions may be deleted or
modiﬁed. For example, one possible action is to have a bedtime
snack. If the current patient is already having an adequate bedtime
snack, this action could be removed from the recommendation. In
other situations, the advice could be modiﬁed so that the patient
eats more or less food before bed, eats a different type of food be-
fore bed, or has a snack at a different time of day.
The adapted therapeutic adjustment is relayed to the physician
as decision support. The physician decides whether or not to relay
the recommendation to the patient. It has long been a goal to pro-
vide low-risk advice directly to patients, in real-time, as well as to
their physicians. However, this must remain a future goal until the
safety and efﬁcacy of the system is proven through clinical trials
and approval is obtained from governmental regulatory agencies.
3.3. 4DSS evaluation
Each component of the 4DSS is evaluated upon completion.
These evaluations have provided proof of concept, illuminated sys-
tem strengths and weaknesses, and guided system development.
Note that a deﬁnitive clinical trial, assessing system impact on pa-
tient outcomes, remains to be conducted.
The problem detection component was evaluated after the ﬁrst
and second 4DSS clinical studies. In the ﬁrst evaluation, a panel of
diabetes practitioners rated a sampling of problem detections, and
in the second, each patient’s physician rated all of the problem
detections for the patient. In the ﬁrst test, 77.5% of problem detec-
tions were rated as correct (Marling, Shubrook, & Schwartz, 2008),
while in the second, 97.9% were rated as correct (Schwartz, Ver-
nier, Shubrook, & Marling, 2010).
The glycemic variability classiﬁcation component was also eval-
uated twice. Here, ten-fold cross validation was used to determine
the accuracy, sensitivity and speciﬁcity of each potential classiﬁer,
where correctness is deﬁned as matching physician classiﬁcations.
In an early test, a naive Bayes classiﬁer matched physicians 85% of
the time (Marling, Shubrook, Vernier, Wiley, & Schwartz, 2011b).
The current best classiﬁer, a multi-layer perceptron, has accuracy,
sensitivity and speciﬁcity of 93.8%, 86.6%, and 96.6%, respectively
(Wiley, Bunescu, Marling, Shubrook, & Schwartz, 2011).
The case retrieval module was evaluated by a panel of diabetes
practitioners after the ﬁrst and second clinical studies. Leave-one-
out testing was used to provide a sampling of case retrievals for
evaluation. In the ﬁrst test, evaluators rated the retrieved cases
as similar to test cases 80% of the time and rated the retrieved solu-
tions as beneﬁcial for test patients 70% of the time (Marling et al.,
2008). In the second test, they rated retrieved cases as similar 79%
of the time and retrieved solutions as beneﬁcial 82% of the time
(Marling et al., 2011b).
The adaptation module was evaluated by showing physicians
sample problems, with both original and adapted solutions, and
eliciting feedback on a questionnaire. Physicians rated the original
solutions as being ﬁne without adjustment 47% of the time, need-
ing minor adjustment 40% of the time, and needing major adjust-
ment 13% of the time. They judged the adapted solutions to be
better than the original solutions 83% of the time.
4. Retrieval of HEmodialysis in NEphrological disorders
Retrieval of HEmodialysis in NEphrological Disorders (RHENE)
supports physicians working in the domain of end stage renal dis-
ease (ESRD). Work on RHENE began in 2004 and continues to this
day. The expert for RHENE is Roberto Bellazzi, a nephrologist at the
Vigevano Hospital in Italy.
4.1. The end stage renal disease domain
ESRD is a severe chronic condition that corresponds to the ﬁnal
stage of kidney failure. Without medical intervention, ESRD leads
to death. Hemodialysis is used to treat ESRD patients. During
hemodialysis, an electromechanical device called a hemodialyzer
clears the patient’s blood of metabolites, re-establishes acid–base
equilibrium and removes excess water. A single hemodialysis ses-
sion typically lasts for four hours. On average, a hemodialysis pa-
tient receives three treatment sessions per week. The
hemodialyzer tracks several time series variables during each ses-
sion, sampling each at intervals of from 1 to 15 min. These vari-
ables are analyzed to assess the efﬁcacy of the hemodialysis
treatment session and to ensure that the patient’s treatment ad-
heres to his or her therapy plan.
Interpreting a hemodialysis session as a case, we must deal with
cases with time series features. Interpreting time series features on
screen or on paper can be tedious and prone to errors. Physicians
may be asked to recognize small or rare irregularities in the series
itself, or to identify partial similarities with past patient situations,
which do not depend upon the individual values in the series.
While extremely important for patient care, such identiﬁcation
crequires a signiﬁcant amount of expertise. Therefore, having an
automated data interpretation and decision support system is
4.2. RHENE system overview
In time dependent domains, the need to describe process
dynamics strongly impacts both case representation and case re-
trieval, as analyzed in (Montani & Portinale, 2006). Most reported
approaches to similarity-based time series retrieval are founded
on the common premise of dimensionality reduction, which sim-
pliﬁes knowledge representation (see the survey in (Hetland,
2003)). Dimensionality is often reduced by means of a mathemat-
ical transform – such as the Discrete Fourier Transform (Agrawal,
Faloutsos, & Swami, 1993) – able to preserve or underestimate
the distance between two time series. However, mathematical
transforms have several limitations, as they can be computation-
ally complex, and usually work in a black box fashion with respect
to end users. In contrast, RHENE (Montani, Leonardi, Bottrighi, Por-
tinale, & Terenziani, 2011) implements a framework for time series
retrieval that exploits Temporal Abstractions (TA) (Shahar, 1997)
to reduce time series dimensionality, with multi-dimensional in-
dex structures to make retrieval efﬁcient.
Using TA allows greater interpretability of the output results
and understandability of the retrieval process. It maps huge
amounts of temporal information to a compact representation,
C. Marling et al. / Expert Systems with Applications xxx (2013) xxx–xxx 5
by aggregating adjacent time series points sharing commonalities
(e.g., the same qualitative level, the same trend direction) into a
single interval, labeled by a symbol. This technique not only sum-
marizes the original longitudinal data, but also highlights mean-
ingful data characteristics in a clear, symbolic, high level view.
The exploitation of TA for case exploration and retrieval, as well
as for data interpretation, is, to the best of our knowledge, a novel
contribution of RHENE. TA are typically applied for data interpreta-
tion, but not for case retrieval.
The basic principle of TA methods is to move from a point-based
to an interval-based representation of the data (Bellazzi, Larizza, &
Riva, 1998), where the input points (events) are the elements of the
time series, and the output intervals (episodes) aggregate adjacent
events sharing a common behavior, persistent over time. Episodes
are identiﬁed by symbols. Basic abstractions can be further subdi-
vided into state TA and trend TA. State TA are used to extract epi-
sodes associated with qualitative levels of the monitored feature,
e.g., low, normal, or high values. Trend TA are exploited to detect
speciﬁc patterns, such as increase, decrease or stationary behavior,
from the time series.
RHENE supports multi-level abstractions of the original data.
Time series values can be abstracted and queried at ﬁner or coarser
levels of detail, along two dimensions: a taxonomy of symbols, and
a taxonomy of time granularities. For instance, a taxonomy of trend
symbols can be introduced, in which the symbol I(increase) is fur-
ther specialized into I
(weak increase) and I
according to the slope. As another example, a series of two adjacent
intervals of I
, each having a duration of half an hour, can be
merged into a single I
interval, with a duration of 1 h. To scale
up from two or more values expressed at a ﬁner granularity to a
single value expressed at a coarser one, an up function is provided.
This function is domain dependent, but obeys some general con-
straints, including ‘‘persistence’’ (the result of coarsening two gran-
ules with the same symbol xis a larger granule still labeled as x)
and ‘‘monotonicity’’ (ordering among symbols, if any, is preserved)
(Montani et al., 2011).
Once abstracted, two time series can be compared to each other,
enabling the retrieval of similar cases. Retrieval depends upon a
distance metric that measures the similarity between symbols in
the taxonomy. Different distance functions can be employed, as
long as the distance of each symbol from itself is zero and other
distances are consistent with respect to any symbol ordering.
A query language was developed to facilitate case retrieval.
Queries are expressed as sequences of symbols at different levels
of detail. RHENE supports ground queries, i.e., queries composed
of symbols at the lowest abstraction level in both taxonomies,
and abstract queries, including those with symbols at different lev-
els in the symbol taxonomies. Queries as regular expressions are
also supported, making the query process ﬂexible and user
To increase retrieval efﬁciency, RHENE includes an indexing
strategy, which exploits bi-dimensional taxonomic indexes. A for-
est of index structures provides a ﬂexible indexing of cases at dif-
ferent levels of the symbol and/or time granularity taxonomies.
The root node of each index structure is represented by a string
of symbols, deﬁned at the highest level in both dimensions. An
example is shown in the right box of Fig. 3. Here, the root node I
is reﬁned along the leading time dimension from the 4-h to the
2-h granularity, so that the nodes II,ISand SIstem from it, pro-
vided that up(I,S)=Iand up(S,I)=I, where Sstands for stationarity.
From each node of the leading dimension structure, another index
can stem, keeping the time granularity abstraction level ﬁxed. The
index develops orthogonally with respect to the leading
In summary, RHENE operates in the following manner, as illus-
trated by Fig. 3:
Time series features in ESRD cases (i.e., hemodialysis sessions)
are pre-processed by a TA server at the ground level of the sym-
bol and time granularity taxonomies.
Pre-processed cases are then stored in a relational database.
When the physician needs to evaluate a patient, he or she can
query the database to retrieve similar cases at any level of
Indexes enable quick and interactive query answering.
Retrieved cases are then shown to the physician as decision
support. The physician remains responsible for making deci-
sions regarding the patient’s therapy.
4.3. RHENE evaluation
RHENE was experimentally evaluated, using a dataset of 10,388
actual hemodialysis sessions (i.e., ESRD cases) collected at the
Vigevano Hospital in Italy. The TA approach was compared to the
more classical approach implemented in an earlier version of
RHENE (Montani, Portinale, Leonardi, Bellazzi, & Bellazzi, 2006).
That approach was based on DFT for dimensionality reduction
and on spatial indexing through TV-trees for improving retrieval
performance. Quantitative experiments were run to compare the
query answering time required by the two approaches, as well as
their scalability when dealing with a case base progressively grow-
ing in size. Subsets of from 2,000 to 10,388 actual cases were used
to evaluate each system’s performance. As shown in Fig. 4, the TA-
based method proved to be much more efﬁcient in query answer-
ing than the DFT-based method, with query times under 1 s.
A qualitative comparison of the two approaches was also made,
through examination of individual case studies. Details of this
examination are available in (Montani et al., 2011). In brief, it
was found that the ability to consider trends was advantageous
for TA-based retrieval. DFT sometimes missed relevant cases, be-
cause it considers only point-to-point distances between cases,
looking for the best overall alignment. Shifts along an axis, such
as lower absolute values, could prevent DFT from ﬁnding cases
with similar trends. Furthermore, the TA-based approach provided
the user with more easily understood queries and results than
DFT’s more mathematical black box approach.
5. The Mälardalen Stress System (MSS)
The Mälardalen Stress System (MSS) provides decision support
for the diagnosis and treatment of stress (Ahmed, Begum, & Funk,
2012, 2011). This system was built between 2002 and 2011 at
Mälardalen University in Västerås, Sweden. The experts for this
system were psychologists Bo von Schéele and Erik Olsson. Dr.
von Schéele has over 30 years’ experience in clinical stress diagno-
sis and has pioneered new biosensor and biofeedback methods.
5.1. The stress management domain
Stress is a factor of daily life that can adversely impact health
and wellbeing. While the causes of stress can not typically be elim-
inated, patients can be taught to effectively deal with stress, min-
imizing its ill effects. A clinical measurement of stress is Finger
Temperature (FT). By placing a sensor on a patient’s ﬁngertip, a
continous temperature proﬁle can be obtained. In general, lower
ﬁnger temperatures are indicative of greater stress. However,
changes in FT vary from patient to patient, and correctly interpret-
ing and analyzing them requires knowledge and experience. More-
over, additional factors, such as the patient’s feelings, behaviors,
environment and lifestyle, also play a role in stress management.
Contextual features, indicating a patient’s perception of stress,
are collected as text and also via Visual Analog Scale (VAS) input.
6C. Marling et al. / Expert Systems with Applications xxx (2013) xxx–xxx
VAS is used to measure subjective characteristics or attitudes on a
scale of 0 to 10. Both sensor signals and textual information are
used to diagnose a patient’s level of stress.
Biofeedback training is used as treatment to control stress. Dur-
ing biofeedback training, a patient can alter his or her physiological
or psychological state while observing the changes in FT on a
graph. As the patient sees how psychophysiological change is rep-
resented on the graph, he or she can train the body and/or mind to
change the biological response to stress. Relaxation exercises may
be recommended to aid the patient in making positive changes.
5.2. MSS system overview
The MSS demonstrates how some common CBR techniques
need to be modiﬁed in order to provide clinicians with effective
decision support based on past cases relevant to the current pa-
tient. This clinical usage adds a number of speciﬁc requirements.
For example, in addition to considering case similarity, it is impor-
tant to consider case outcomes. A case with a severe outcome may
be very relevant, as a clinician may need to take precautions to
avoid a similar result. An example is that most patients’ ﬁnger tem-
peratures decrease during stress and increase during relaxation, a
normal reaction from the parasympathetic nervous system. But
in some patients, the effect on ﬁnger temperature may differ due
to rare conditions the clinician must consider during diagnosis. If
the current patient is similar to many past patients diagnosed as
not at risk for stress-related illnesses and similar to one patient
having a condition with severe health consequences if left un-
treated, the latter case is of most interest to the clinician, who
may take additional measurements to rule this case out.
To meet the needs of the medical domain, the case has been
structured with the following components: (a) symptom features,
including sensor readings and contextual data; (b) diagnosis; (c)
action taken, i.e., treatment and biofeedback training; and (d) out-
come, e.g., how quickly the patient improves. The case may also
contain (e) comments, which can be added by a clinician who
wishes to share perceptions and/or important references with col-
leagues. Comments may be added by the clinician responsible for
the case or by a domain expert.
The symptom features for a case are derived from a calibration
protocol in which the patient is asked to complete a series of
stressful, relaxing and neutral tasks while FT measurements are
collected (Ahmed, Begum, Funk, Xiong, & von Schéele, 2011). This
calibration phase is necessary, because sensor readings vary widely
from patient to patient. Sensor readings that are normal for one
person may be alarming for another individual. The patient is also
asked to supply contextual data via text and VAS input. A total of
25 sensor and contextual features comprise this section of a case.
The diagnosis part of the case holds the stress classiﬁcation of
the patient, which may be: VeryRelaxed, Relaxed, Normal/Stable,
Stressed or VeryStressed. A conﬁdence level for the classiﬁcation,
denoted as High, Medium or Low, is also recorded for each diagno-
sis. The actions taken comprise the treatment, and the outcome is
the result of the treatment, i.e., the degree of improvement after
Fig. 3. RHENE System Overview.
Fig. 4. Average time, in seconds, to answer a query as the number of cases
increases, for DFT and TA.
C. Marling et al. / Expert Systems with Applications xxx (2013) xxx–xxx 7
Biofeedback is a tool used by clinicians in treatment and overall
stress reduction. It is included in the section of the case for action
taken. The latest diagnosis is used as one of the parameter settings
for biofeedback. The patient gets feedback on parameter improve-
ment. Another important parameter is recovery time after stress,
i.e., the time it takes for a patient to recover after a stress-phase.
This parameter can be used to select relaxation exercises/treat-
ment for the patient. During the exercise, the biofeedback system
monitors the patient and calculates the recovery time after stress.
This enables patients to practice biofeedback either with a clinician
at hand or alone with a computer system and sensors. The relaxa-
tion exercise/treatment is selected to achieve good results, based
on how good the effect was on previous similar patients. If the
recovery time does not change with the selected exercise, then a
different exercise is selected. Exercise selection is based on past
cases and the result of the exercise/treatment. Different exercises
have different effects on patients. Selecting exercises and following
patient progress in this way increases the efﬁciency of the exercise.
The MSS supports stress diagnosis and treatment in three
phases: (1) analyze and classify a patient and make a risk assess-
ment; (2) determine individual levels and parameters; and (3)
adapt and conduct biofeedback training. CBR is used as the core
technology for the system, as illustrated in Fig. 5. Knowledge dis-
covery contributes part of the domain knowledge used in case re-
trieval. Since patients in the case library have already been
diagnosed/classiﬁed, these cases can be used to identify the fea-
tures that are most important for case comparison and retrieval
(Funk & Xiong, 2006). Other AI techniques, including fuzzy logic,
rule-based reasoning and textual information retrieval, are also
incorporated in supporting roles.
5.3. MSS evaluation
The MSS system has been evaluated using a number of different
methods. Diagnostic performance has been compared with that of
clinicians, including clinical experts and junior clinicians with lim-
ited practice in diagnosing stress using sensor readings. Using mul-
tiple test sets, the system was able to classify over 80% of cases
correctly, compared to between 57% and 69% for junior clinicians
and 73% for a senior clinician (Ahmed et al., 2012). In comparing
system outputs to clinician diagnoses, it was important to consider
the consistency of clinician responses when confronted with the
same patient more than once. In another test of diagnostic perfor-
mance, leave one out cross validation was used. In this evaluation,
cases were removed from the case library one at a time to see how
well the system could classify each one. Using a fuzzy matching
algorithm, the MSS was able to perform close to expert level, as re-
ported in (Begum, Ahmed, Funk, Xiong, & von Schéele, 2009). In
addition to judging overall accuracy, it was important to consider
the sensitivity and speciﬁcity of the system. From a clinical per-
spective, missing a stressed patient (false negative) is less accept-
able than identifying a healthy individual as stressed (false
positive). A false positive would likely be identiﬁed as such before
beginning treatment, based on additional pre-treatment
6. Research synergies and trends
In this section, we elaborate on the research synergies and
trends highlighted by CARE-PARTNER, the 4DSS, RHENE, and the
6.1. Coupling AI research with medical research and practice
All four systems were built through strong collaborations with
medical researchers and clinicians. While we have focused above
on AI research and development, there has been a tight coupling
with medical research and practice, as well. For example, CARE-
PARTNER supported evidence-based practice for oncology. At the
time of the original study, stem cell transplantation was a very
new procedure, and there were no established guidelines for the
long-term follow-up care of patients after they left the cancer cen-
ter. By compiling cases of patient problems, solutions, and out-
comes, CARE-PARTNER aided in deﬁning best clinical practices.
The 4DSS, RHENE, and the MSS can all be characterized as promot-
ing the practice of personalized medicine. These systems look at
how individual patients respond to diabetes therapy, hemodialysis,
Fig. 5. Overview of the Mälardalen Stress System.
8C. Marling et al. / Expert Systems with Applications xxx (2013) xxx–xxx
and stress, respectively, and suggest personalized therapeutic
adjustments based on individual patient needs.
The 4DSS contributes to medical research in the areas of glyce-
mic variability (GV) measurement and blood glucose prediction.
Because there was no accepted metric for GV in routine clinical
use, but 4DSS experts wanted to detect excessive GV, new metrics
were developed for the 4DSS. These metrics were subsequently
published and presented to the diabetes technology community
(Schwartz et al., 2010), and the 4DSS project is now leading the
way in developing a consensus GV metric for routine clinical use.
Current research on blood glucose prediction not only aids in intel-
ligent decision support, but potentially aids in the development of
an ‘‘artiﬁcial pancreas,’’ a project of the Juvenile Diabetes Research
RHENE supports medical practice and research as well. Hemod-
ialysis sessions are ordinarily judged only on the basis of macro-
scopic observations of time series features. RHENE provides a
deeper insight into the clinical situation, highlighting types of
anomalies which, if not leading to immediate hemodialysis failure,
could produce poor therapeutic results in the long run. (See, for
example, the experiments in Montani et al. (2006)). Moreover,
the use of RHENE may lead to the identiﬁcation of systematic map-
pings between TA trend behaviors (e.g., an increase in diastolic
pressure) and speciﬁc pathologies or complications, in an applica-
tion domain where this kind of knowledge does not currently exist.
Expert clinicians with the MSS project found that their work on
the system helped them to reﬁne their own methods and ap-
proaches for diagnosing patients. They also found that the system-
atic analysis of symptoms required by the system improved the
quality and value of the electronic health records (EHRs) that they
maintained for their patients. The ﬁnal outcomes of treatments had
not always been recorded in detail prior to system development. As
tracking outcomes was essential for case-based reasoning, the
EHRs for patients of participating physicians became more com-
plete. Furthermore, the close collaboration made it more explicit
how clinical experts, while reading symptoms and measurements,
would recognize similarities to past patients that would aid in
diagnosis and treatment. This led to the discovery of new features
that were valuable in the diagnosis process (Funk & Xiong, 2006,
6.2. Integrating multiple AI and computing methodologies
All four systems are multi-modal, or hybrid, systems, which
synergistically combine CBR with other AI and computing method-
ologies. CARE-PARTNER combines CBR with rule-based reasoning
and information retrieval. Its knowledge base encompasses both
theoretical knowledge and experiential knowledge, which may
be expressed either in a controlled vocabulary or textually. The
4DSS integrates rule-based reasoning for problem detection and
case adaptation, multiple machine learning algorithms for glyce-
mic variability classiﬁcation, and support vector regression for
blood glucose prediction. RHENE incorporates temporal abstrac-
tions for representing, comparing, and retrieving cases. The MSS
leverages rule-based reasoning, fuzzy logic, and information retrie-
val. Fuzzy rule-based classiﬁcation is used when the domain
knowledge is precise (e.g., if X and Y are high, then we know that
Z is high) but the terminology used is not precise (e.g., the same
absolute value may be high or low, depending on context). Infor-
mation retrieval is used to handle the free text given in the case,
e.g., the patient history.
Driven by the demands of speciﬁc medical domains, these inte-
grations push the envelope of how knowledge-based systems can
be engineered. For example, hemodialyzers generate data that
could not be readily reasoned about without some form of feature
reduction or abstraction. So, RHENE introduced a novel combina-
tion of TA and CBR that could be extended to other domains having
time series data. Similarly, CARE-PARTNER’s early information re-
trieval integration has become increasingly relevant as information
from electronic health records and large medical corpora become
6.3. Leveraging small numbers of available cases
In this era of Big Data, there is much emphasis on extracting
useful information from large volumes of available data. For med-
ical decision support applications, however, it is still unusual to
have all of the relevant data at hand. Although RHENE did have
thousands of cases to work with, CARE-PARTNER, the 4DSS, and
the MSS effectively leveraged small numbers of available cases.
CARE-PARTNER, for example, began with an incomplete patient
database containing extracts from paper-based patient charts.
Ninety-one prototypical cases were built through extensive knowl-
edge engineering efforts, enabling domain knowledge to supple-
ment the available data. The 4DSS began with an abundance of
blood glucose data, but the contextual life events needed to inter-
pret it were not routinely maintained. A series of three clinical re-
search studies was conducted, in which 80 problem/solution/
outcome cases were developed from actual patient data. Each case
contains over 140 data elements, hierarchically organized, to rep-
resent a problem solving experience. The MSS project conducted
clinical studies to capture and represent patient stress proﬁles.
However, case coverage was initially incomplete, because some
proﬁles, such as the Very Relaxed proﬁle, were not well repre-
sented. Artiﬁcial cases were generated to augment the actual pa-
tient cases by using generalized sensor features and a fuzzy
inference system. This improved the system’s ability to retrieve
applicable cases (Ahmed, Begum, Funk, & Xiong, 2009). While there
are many ways to leverage large numbers of exemplars, the ability
to solve problems with just a few knowledge-rich cases is a
strength of the CBR approach.
6.4. Reasoning with time series data
All four systems reason about cases that develop over time. This
is in contrast to most CBR systems, in which a snapshot of current
conditions is sufﬁcient for assessing and reasoning about the prob-
lem at hand. CARE-PARTNER, for example, is for long-term follow-
up care, and the patient’s past history informs current care.
Time series data for the 4DSS comes in the form of blood glu-
cose sensor data, collected for up to 90 days at 5 min intervals, jux-
taposed with the life-event and insulin data reported over the
same period of time. For problem detection, rule-based pattern
recognition routines, based on expert strategies, were imple-
mented. For glycemic variability classiﬁcation, several domain
dependent and independent features are extracted from the sensor
data, for use by classiﬁcation algorithms. Blood glucose prediction
is tackled as a time series forecasting problem, using support vec-
RHENE’s time series data comes from the hemodialyzer, over a
4-h period, in increments of from 1 to 15 min. RHENE has the tight-
est integration with time series data, using TA to represent, com-
pare, and retrieve cases. Time series data for the MSS comes from
ﬁnger temperature sensors, which are worn by patients during
15-min calibration sessions. The system extracts features from
the sensor data, including the recovery rate after stress, the slope
during periods of stress, and the difference between relaxed and
stressed ﬁnger temperature readings. The extracted features are
used to build cases for the system.
C. Marling et al. / Expert Systems with Applications xxx (2013) xxx–xxx 9
6.5. Integrating numeric data with contextual and subjective
In medical domains, it is often necessary to consider hard nu-
meric data in light of contextual information and subjective patient
perceptions. For one thing, the world around the patient impacts
the patient’s health. For another, therapeutic recommendations
that do not suit a patient’s individual lifestyle and preferences
may be ignored, no matter how medically advisable. In the 4DSS,
numeric blood glucose data is considered in light of the life events
that inﬂuence blood glucose levels, including when and what the
patient eats, when and how intensely the patient exercises,
whether the patient is at home or at work, awake or asleep, feels
stressed or ill, and so on. The efﬁcacy of a recommended therapeu-
tic intervention is evaluated in light of whether or not the patient
actually implemented the recommendation. In the MSS, ﬁnger
temperature sensor signals that are indicative of stress are consid-
ered together with the factors in the patient’s life that could cause
the stress. CBR allows the ﬂexible use of disparate types of data
within cases, suiting it to these medical domains.
7. Summary and conclusion
In this paper, we have presented four synergistic systems that
exemplify the approaches and beneﬁts of case-based reasoning in
medical domains. These systems are CARE-PARTNER, the 4 Diabe-
tes Support System, Retrieval of HEmodialysis in NEphrological
Disorders, and the Mälardalen Stress System. We have explored
how these systems couple AI research with medical research and
practice, integrate multiple AI and computing methodologies,
leverage small numbers of available cases, reason with time series
data, and integrate numeric data with contextual and subjective
information. We hope that these cases of medical CBR systems will
inform future medical CBR research and development.
Work on CARE-PARTNER was supported by Grant R01HS09407
from the Agency on Health Care Policy and Research (AHCPR), and
by a Scholarship Grant from the University of Washington, Tacoma.
Work on the 4DSS is supported, in part, with funding from the
National Science Foundation under the Smart Health and Wellbe-
ing program (award IIS-1117489). Additional support comes from
Medtronic, the Ohio University Russ College Biomedical Engineer-
ing Fund, the Ohio University Heritage College of Osteopathic Med-
icine Research and Scholarly Affairs Committee, and the Ohio
University Diabetes Research Initiative.
Work on the RHENE system has been supported with funding
from the Italian Ministry of Education (within the project MIUR
PRIN – 2004–2006 – Intelligent Analysis of Hemodialysis Monitor-
ing Data to Improve Care Processes), and by Regione Piemonte
(within the project Regione Piemonte-Polo Innovazione ICT –
2010–2012 – MASP: Ricerca e Sviluppo Sperimentale di un Sistema
per il Controllo a Distanza di Aree Sensibili e Protette, Finalizzato
all’Erogazione di Servizi Innovativi Orientati al Monitoraggio Cog-
nitivo ed Attuativo di Politiche Ambientali).
Work on the MSS has been supported by funding from: the Swed-
ish Knowledge Foundation Project AIM, Artiﬁcial Intelligence for
medical application: Diagnosing stress based on psychophysiologi-
cal sensors, project number 2001/0068 (2002–2004); IPOS, Intelli-
gent integrated sensor systems for diagnosis, treatment and
healthcare, project number KKS HÖG 2004/0341 (2005–2008);
Improved productivity and life quality through reduced stress,
Sparbanksstiftelsen Nya, project reference 2081 (2009); and Multi-
sensors for stress monitoring, NovaMedTech, FP7 68737 (2008–
All four systems were made possible through the extensive ef-
forts of many collaborators, including domain experts, clinical staff,
graduate research assistants, and patients. The authors gratefully
acknowledge their contributions and support.
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