Decision trees based on automatic learning and their use in cardiology

Computerized information systems, especially decision support systems, have become an increasingly important role in medical applications, particularly in those where important decision must be made effectively and reliably. But the possibility of using computers in medical decision making is limited by many difficulties, including the complexity of conventional computer languages, methodologies and tools. Thus a conceptual simple decision making model with the possibility of automating learning should be used. In this paper we introduce a cardiological knowledge-based system based on the decision tree approach supporting the mitral valve prolapse determination. Prolapse is defined as the displacement of a bodily part from its normal position. The term mitral valve prolaps (PMV), therefore, implies that the mitral leaflets are displaced relative to some structure, generally taken to be the mitral annulus. The implications of the PMV are the following: disturbed normal laminar blood flow, turbulence of the blood flow, injury of the chordae tendinae, the possibility of thrombus's composition, bacterial endocarditis, and finally hemodynamic changes defined as mitral insufficiency and mitral regurgitation. Uncertainty persists about how it should be diagnosed and about its clinical importance. It is our deep belief that the echocardiography enables properly trained experts armed with proper criteria to evaluate PMV almost 100%. But unfortunately, there are some problems concerned with the use of echocardiography. In that manner we have decided to start a research project aimed at finding new criteria and enabling the general practitioner to evaluate PMV using conventional methods and to select potential patients from the general population. To empower one to perform needed activities we have developed a computer tool called ROSE (computeRised prOlaps Syndrom dEtermination) based on algorithms of automatic learning. This tool supports the definition of new criteria and the selection of potential PMV-patients.     

Pheophytin a, a low molecular weight compound found in the marine brown alga Sargassum fulvellum, promotes the differentiation of PC12 cells

We identified and characterized a neurodifferentiation compound from the marine brown alga Sargassum fulvellum collected from the Japanese coastline. Several instrumental analyses revealed the compound to be pheophytin a. Pheophytin a did not itself promote neurite outgrowth of PC12 cells. However, when PC12 cells were treated with a low concentration of pheophytin a (3.9 microg/ml) in the presence of a low level of nerve growth factor (10 ng/ml), the compound produced neurite outgrowth similar to that produced by a high level of nerve growth factor (50 ng/ml). Pheophytin a also enhanced signal transduction in the mitogen-activated protein kinase signaling pathway, which is also induced by nerve growth factor. The effect of pheophytin a on neurite outgrowth of PC12 cells was completely blocked by U0126, a representative mitogen-activated protein kinase kinase inhibitor. These results suggest that pheophytin a enhances the neurodifferentiation of PC12 cells in the presence of a low level of nerve growth factor and that this effect is mediated by activation of a mitogen-activated protein kinase signaling pathway.     

Current determinants of 30-day and 3-month mortality in over 2000 aortic valve replacements: Impact of routine laboratory parameters.

OBJECTIVE: Haematological and biochemical measurements are performed routinely before surgery to exclude organ malfunction and blood cell and coagulation abnormalities. We aimed to test routinely obtained laboratory data as factors predicting operative risk. METHODS: Between 1996 and 2003, 2198 patients underwent aortic valve replacement (AVR) (908 of them with concomitant CABG) in our institute. The mean age of the study population was 69+/-11 years (range 13-91, 43% female). Clinical and laboratory parameters based on the consolidated data mart set were evaluated by multiple logistic regression analysis. RESULTS: The overall operative mortality (within 30 days) was 3.8% and the mortality after 3 months was 5.9%. In addition to clinical characteristics, the following laboratory values were identified as independent predictors of 30-day mortality: fasting blood glucose, antithrombine III, partial thromboplastine time and creatinine kinase. As independent predictors of 3-month mortality, the following laboratory values were indentified: fasting blood glucose, serum creatinine, antithrombine III, partial thromboplastine time, lactate dehydrogenase, sodium concentration and serum proteins. The discriminative power of the models increased if laboratory parameters were included in addition to preoperative clinical characteristics (from 0.75 to 0.79 and from 0.75 to 0.78 for 30-day and 3-month mortality, respectively). The discriminative power using the logistic EuroScore was lower (0.71 and 0.7, for 30-day and 3-month mortality, respectively). CONCLUSIONS: Laboratory parameters as objective markers for organ function and nutritional status are useful data for the prediction of 30-day and 3-month mortality after aortic valve replacement. Using modern methods of information technology, these valuable data which are stored electronically in most hospitals, can be used efficiently for research and quality control.