PhD Thesis information
Knowledge Management and Decision Support in Diabetes Care through Multi
Modal Reasoning
Extended Abstract
In order to overcome the limitations shown by classical Expert Systems,
today the trend in decision support systems definition in the medical domain
is the one of integrating the existing Hospital Information System (HIS)
with different methodologies and technologies, able to co-operate in a transparent
way with respect to the user, and to promote information exchange among the
human agents involved in the patients management process. This goal is achieved
by coupling the decision support functionality with the Knowledge Management
(KM) task, being KM a discipline for promoting knowledge growth, communication
and preservation within an organization. Instead of being stand-alone solutions
to afford particular problems, decision support systems can today be considered
as building blocks of the HIS within a larger KM perspective.
In medical applications, two knowledge types are generally available: explicit
knowledge, corresponding to the already well established domain knowledge,
and implicit knowledge, consisting of individual expertise, organizational
practices, and past cases. Both implicit and explicit knowledge need to be
managed and maintained, in order to support the needs and the complexity
of medical decision making. To take advantage from the exploitation of both
implicit and explicit knowledge, the recent advances in Information Technology
have led to the design of KM and decision support systems able to integrate
a set of information sources, relying on different knowledge representation
formalisms. Various reasoning methodologies can then be applied, defining
a Multi Modal Reasoning (MMR) strategy meant to overcome the single approaches
limitations, and to better cope with the target domain problems. Particular
attention has received the combination of Rule Based Reasoning (RBR) and
Case Based Reasoning (CBR), being rules the most successful knowledge representation
formalism for intelligent systems, and being CBR well suited for integration
with formalisms grounded on general, declarative knowledge. In particular,
CBR is a reasoning paradigm able to exploit the information embedded into
already solved instances of problems, called cases. The common basis of most
of the MMR approaches described in the literature is that CBR and RBR are
used in a quite exclusive way. On the contrary, only a very tight integration,
taking place within the general problem solving cycle, can effectively overcome
the intrinsic weaknesses that the two reasoning paradigms may show. Realizing
a tight integration between RBR and CBR has been our aim while defining and
implementing the MMR system described in this PhD thesis, meant to perform
KM in the context of type 1 diabetic patients management, and to provide decision
support to physicians when dealing with therapy revision.
Diabetes Mellitus is a major chronic disease in the industrialized countries.
In particular, type 1 diabetic patients patients need insulin injections
to regulate blood glucose metabolism, in order to prevent acute episodes,
such as ketoacidosis and coma, as well as later life invalidating complications.
Intensive Insulin Therapy, consisting in 3 to 4 injections every day, or
in the use of sub-cutaneous insulin pumps, may reduce the outlined risks,
but, on the other hand, increases costs and the risk of severe hypoglycemias.
Optimizing insulin administration and therapy implementation is therefore
a complex task: patients are required to collect their monitoring data (e.g.
Blood Glucose Level measurements and insulin doses) several times a day.
Moreover, every 2-4 months they undergo control visits, during which physicians
are expected to asses their metabolic condition, and, if needed, to adjust
the current insulin therapy. In order to revise the therapeutic protocol,
physicians may rely both on structured knowledge (e.g. on insulin pharmacodynamics),
and on previous experience (e.g. the observation that a certain protocol
has been applied on that patient or to patients with similar characteristics
in the past, with a particular outcome). Integration among RBR and CBR seems
therefore a natural solution when building a decision support system to be
applied in this context.
From an implementation perspective, the reasoning process carried out by
our system consists in a series of tasks, whose execution is scheduled by
the RBR system. First, the possible patient's metabolic alterations are identified
from the monitoring data; then, a set of reliable suggestions to cope with
them are proposed, and finally the most suitable suggestions are applied
to the current insulin therapy. RBR is meant to be general enough to be safely
applicable to any patient category. Therefore, it typically proposes quantitatively
small changes to the insulin therapy presently adopted by the patient.
On the other hand, rule parameters can be tuned on the basis of past experience
and of specific features, extracted from Case Based retrieval, thus tailoring
the therapeutic advice to the single patient's needs. In particular, the
case library has been structured resorting to a taxonomy of mutually exclusive
prototypical classes, that express typical problems that may occur to type
1 diabetic patients in the age of infancy and puberty. Therefore, before retrieving
past cases similar to the current one, the system classifies the current
case as belonging to one of such classes. Classification implements context
detection: the patient is categorized as being experiencing a particular
clinical course condition or associated disease, and her/his data can be
better interpreted, in the light of the specific situation she/he is currently
living. Moreover, classification is crucial in making efficient the retrieval
by restricting search only to relevant parts of the whole case library. Retrieval
can in fact be limited to the most probable class, or to a subset of all
the classes in the taxonomy. Classification and retrieval results are then
exploited to specialize the behavior of the metabolic alteration identification
task and of the therapy adjustment task respectively.
The clinical correctness of the suggestions proposed by the MMR system has
been tested through a formal evaluation procedure. In particular, two diabetologists
were asked to perform a fully-crossed, blind review of the therapies proposed
by RBR, by MMR, and by two colleagues, to 30 real patient cases. 98/118 therapies
were judged as acceptable for the physicians, and 101/120 for the systems.
The MMR and the RBR tools hence proved to be able to work at the physician
level; on the other hand, no benefit could be found by exploiting MMR in
comparison to RBR. The reason for such an unsatisfactory result was due to
a too small case library, not sufficiently representative of some patients
categories (presence of "competence gaps"). To cope with the problem of misleading
retrieval information, we plan to implement a control strategy, that will
enable the exploitation of retrieval results only if a sufficiently large
number of similar cases has been retrieved, and if they are similar enough
to the input case to justify their use. In this way, MMR will support decision
making at various levels of complexity, as the case library grows: first,
when the stored information is poor, RBR will come out to be the most competent
methodology, and it will be applied without relying on CBR results. As far
as new information is stored in the case library, retrieval results will
become more reliable, and will be exploited by the MMR methodology.
Moreover, by simply introducing the system into clinical practice, it will
be possible to enrich the case library content with further information,
since new patients' cases will automatically be stored during every day activity,
without requiring an additional workload from physicians. In this way, the
MMR therapeutic suggestions reliability will be enhanced; our application
is in fact a (anytime) continuously learning system, whose competence is
increased as new data are collected.
The system was developed in the context of the EU-funded project T-IDDM
(HC-1047), and will be exploited as a KM and decision support functionality
within EU-funded project M2DM (IST-1999-10315).
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