March 2009
Issue #26
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Biomedical Research

Medical Informatics:
New Methods for Application
at the Bedside and Scientific Study

Cathy M Helgason and Thomas H Jobe

Departments of Neurology and Psychiatry,
University of Illinois, College of Medicine at Chicago,
Chicago, Illinois

Medical Informatics refers not only to computer and engineering applications of scientific and technical progress to medical diagnosis and treatment. Researchers both in the United States and in Italy have focused on developing methods to discover the dynamics of disease processes and treatment effects.

Members of the SMC Technical Committee on Medical Informatics, Massimo Buscema and Enzo Grossi, have developed the use of Artificial Adaptive Systems, specifically Autocontractive Mapping in discovering dynamic inter relationships among variables over time which can reflect what is happening in a single patient or a group of patients [1]. This has been most recently used to study variables in patients with stroke who are taking antiplatelet therapy. What has long been suspected by clinicians is that different patients require a different dose or type of anti platelet therapy in order to get the desired effect of platelet inhibition for the prevention of heart attack and stroke.

The autocontractive maps of different individuals show different weighted connections amongst variable markers of platelet inhibition as measured at repeated intervals over time. While this method has been used to study the effect of antiplatelet agents in stroke patients, it needs to be further developed for use at the bedside for patient monitoring in the clinical setting.

Other members of the same Technical Committee have developed a method for using fuzzy measures to represent the dynamics of disease and treatment in patients and to measure the outcome of scientific experiments. Cathy Helgason and Thomas Jobe have developed a method to measure not only the similarity between two patients represented as fuzzy sets in the geometry of the unit hypercube, but to do so in such a way that different patient contexts are taken into the measure [2]. For the first time a particular patient at the bedside can be measurably compared to himself/herself at different points in time, to any other patient, including the average patient of a large clinical trial. This latter measure allows the statistic of that trial to be specifically applied to that unique patient at the bedside.

References

[1] M Buscema, PM Rossini, E Grossi, “Babiloni: The Implicit Function as Sqashing Time for EEG Analysis: Theory, in Artificial Adaptive Systems in Medicine: New Theories and Models for New Apllications”, Massimo Buscema, Enzo Grossi ( eds) 2008: Aracne editrice S.l.l. cod. A728/08: pp 153-171.

[2] Helgason CM, Jobe TH, “Measurabe Prediction for the Single Patient and the Results of Large Double Blind Controlled Randomized Trials”, Plos One 3(4): e1909: doi: 10.1371/journal.pone.0001909.

About the Authors

Cathy M. Helgason was born in 1948. She attended medical school at the University of Iceland and completed her Residency in Neurology and a Fellowship in Cerebrovascular Diseases at the University of Chicago. Dr Helgason is Professor of Neurology at the University of Illinois College of Medicine at Chicago. Dr Helgason's research has focused on the dynamic process of disease progression in the individual patient. Dr Helgason is a member of the TC on Medical Informatics, SMC society.

Thomas H. Jobe was born in 1943. He attended medical school at the University of Chicago in 1969 graduated from SUNY after having completed a Residency in Psychiatry. He is now Professor of Psychiatry at the University of Illinois College of Medicine at Chicago. Dr Jobe's research has focused on Fuzzy Logic in medicine and long term outcome of psychiatric disorders. He is a member of the TC on Medical Informatics SMC society.