117,600 research outputs found
The evolution of decision support systems in healthcare: the knowledge management perspective
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High- and low-flux acetate-free biofiltration: experimental assessment of calcium mass balance and intact parathyroid hormone behaviour.
No full textWe studied total calcium mass balance and plasma intact parathyroid hormone behaviour in 10 uraemic patients who underwent acetate-free biofiltration carried out in accordance with six different dialytic schedules, where either a polyacrylonitrile or a polysulphone membrane was used. Schedules 1 and 2 involved a reinfusion flow rate of 33.3 ml/min with a dialysate calcium concentration (DCa) of 1.75 and 2 mmol/l respectively; in schedule 3, 4, 5 and 6 reinfusion flow rate amounted to 50 ml/min and DCa was respectively of 1.75, 2, 2.25 and 2.5 mmol/l. Dehydration remained unchanged in all schedules: 700 g/h. Finally high- and low-flux acetate-free biofiltration are able to induce different Ca mass balance which may suit different therapeutic contexts. Ca mass balance was either positive or negative depending on reinfusion flow rate and DCa. With a reinfusion flow rate of 33.3 ml/min a DCa of at least 2 mmol/l was necessary to obtain a positive mass balance, while with a reinfusion flow rate of 50 ml/min DCa had to equal 2.25 mmol/l. In high-flux acetate-free biofiltration, the estimation of predialytic Ca2+ and DCa values, using a simple formula, allows prediction of the mass balance that will be attained. At the end of acetate-free biofiltration, intact parathyroid hormone always decreased when a polyacrylonitrile membrane was employed while it increased, in the presence of negative Ca mass balance with a polysulphone membrane
Improving dialysis services through Information Technology: from telemedicine to data mining
No full textAssessing the quality of care for end stage renal failure patients by means of artificial intelligence methodologies
No full textEnd Stage Renal Disease is a severe chronic condition that
corresponds to the final stage of kidney failure. Hemodialysis (HD) is the most widely used treatment method for ESRD.
In order to assess the performance of HD centers, we are developing an auditing system, which resorts to (i) temporal data mining techniques, to discover relationships between the time patterns of the data automatically collected during HD sessions and the performance outcomes, and to (ii) case based reasoning (CBR) to retrieve similar time series within the HD data, in order to evaluate the frequency of critical patterns.
The overall approach has demonstrated to be suitable for knowledge discovery and critical patterns similarity assessment on real patients' data, and its use in the context of an auditing system for dialysis management is helping clinicians to improve their understanding of the patients behaviour
Learning from biomedical time series through the integration of qualitative models and fuzzy systems
No full textOur work deals with a method for the identification of the dynamics of nonlinear (patho-)physiological systems by learning from data. The key idea which underlies our approach consists in the integration of qualitative modeling methods with fuzzy logic systems. The major advantage which derives from such an integrated framework lies in its capability both to represent the structural knowledge of the system at study and to determine, by exploiting the available experimental data, a functional approximation of the system dynamics that can be used as a reasonable predictor of the patient's future state. We have successfully applied our method in the identification of the intracellular kinetics of thiamine from data collected in the intestine cells
Quality assessment of dialysis services through Intelligent Data Analysis and temporal data mining
No full textPredicting Comorbidities Using Resampling and Dynamic Bayesian Networks with Latent Variables
No full textComorbidities such as hypertension and lipid metabolism are often associated in diseases such as diabetes, and the early prediction of these is of great value when trying to manage progression. This is the start of a project to model multiple comorbidities in diabetes using dynamic Bayesian networks with latent variables in order to stratify patient cohorts. In this paper, we demonstrate some initial results on a dataset where the class imbalance problem poses an issue due to the rare occurrence of different individual comorbidities on a visit-by-visit basis. This is dealt with using a bootstrap technique that has been specifically designed for longitudinal data where the occurrence of the positive class occurs far less than the negative
Data mining with Temporal Abstractions: Learning Rules from Time Series
No full textA large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data
Patient similarity for precision medicine: A systematic review
No full textEvidence-based medicine is the most prevalent paradigm adopted by physicians. Clinical practice guidelines typically define a set of recommendations together with eligibility criteria that restrict their applicability to a specific group of patients. The ever-growing size and availability of health-related data is currently challenging the broad definitions of guideline-defined patient groups. Precision medicine leverages on genetic, phenotypic, or psychosocial characteristics to provide precise identification of patient subsets for treatment targeting. Defining a patient similarity measure is thus an essential step to allow stratification of patients into clinically-meaningful subgroups. The present review investigates the use of patient similarity as a tool to enable precision medicine. 279 articles were analyzed along four dimensions: data types considered, clinical domains of application, data analysis methods, and translational stage of findings. Cancer-related research employing molecular profiling and standard data analysis techniques such as clustering constitute the majority of the retrieved studies. Chronic and psychiatric diseases follow as the second most represented clinical domains. Interestingly, almost one quarter of the studies analyzed presented a novel methodology, with the most advanced employing data integration strategies and being portable to different clinical domains. Integration of such techniques into decision support systems constitutes and interesting trend for future research
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