1,721,531 research outputs found
Drug delivery optimization through Bayesian networks.
No full textThis paper describes how Bayesian Networks can be used in combination with compartmental models to plan Recombinant Human Erythropoietin (r-HuEPO) delivery in the treatment of anemia of chronic uremic patients. Past measurements of hematocrit or hemoglobin concentration in a patient during the therapy can be exploited to adjust the parameters of a compartmental model of the erythropoiesis. This adaptive process allows more accurate patient-specific predictions, and hence a more rational dosage planning. We describe a drug delivery optimization protocol, based on our approach. Some results obtained on real data are presented
Electronic Management of Systems in Diabetes Mellitus: Impact on Patient Outcomes
No full textThe epidemiological burden of diabetes mellitus is changing the classical model of diabetes management, in which a specialist center delivers care based on registration, recall and regular review. Primary care services are progressively assuming a crucial role in screening. prevention and management of the disease. It therefore becomes critical to improve the performance of primary care providers by suitable organizational interventions. The current advances in information technology (IT) and communications technology provide new ways for coping with organizational problems, and provide the opportunity to implement complex. multifaceted interventions in a cost-effective manner. Moreover, IT enables patients to exploit new modalities of access to healthcare services.
This review highlights the current situation in the implementation and delivery of IT solutions for diabetes care, and describes the trends towards more advanced and innovative IT-based services.
A large number of electronic patient records (EPRs), decision support tools and telemedicine solutions have been proposed and studied but a relatively low number of them have been fully exploited in clinical practice. The main reasons for this limited dissemination are related to the complexity of establishing and evaluating interventions that have a strong impact in the process of care. However, the need for a large scale reorganization of chronic care is now pushing towards the integration of the newest IT tools with new models of diabetes management
Commentary on: Although people with diabetes mellitus may prefer blood glucose self-monitoring, evidence does not suggest improved outcomes relative to urine testing, Abstracted from: Coster S et al. Monitoring blood glucose control in diabetes mellitus: a systematic review. Health Technol Assess 2000 4(12)
No full textTelemedicine and Diabetes Management: Current Challenges and Future Research Directions
No full textTelemedicine is lying between fading and future. Several clinical studies and critical reviews have been published recently, but the results are inconclusive and the adoption of telemedicine interventions in clinical practice is slow. This article discusses some of the current problems related to the adoption of telemedicine systems and focuses on the information technology solutions that appear to be most promising for diabetes management in the near future. Context awareness, user modeling, intelligent dialogues, and integrated information systems are presented. Some potential future scenarios for the adoption of telemedicine, which combine novel technologies and new organizational models, are also discussed. Within those scenarios, telemedicine may prove to be a good instrument to support health care providers in the effective management and prevention of diabetes mellitus
Drug delivery optimization through Bayesian networks: an application to erythropoietin therapy in uremic anemia.
No full textThis paper describes how Bayesian networks can be used in combination with compartmental models to plan recombinant human erythropoietin delivery in the treatment of anemia of chronic uremic patients. Past measurements of hemoglobin concentration in a patient during the therapy can be exploited to adjust the parameters of a compartmental model of erythropoiesis. This adaptive process provides more accurate patient-specific predictions, and hence a more rational dosage planning. Inferences are performed by using a stochastic simulation algorithm called Gibbs sampling. We describe a drug delivery optimization protocol based on our approach. Some results obtained on real data are presented
Big data and biomedical informatics: a challenging opportunity.
No full textBig data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations. Big data; NoSQL; cloud; data analytics; map-reduce; research reproducibilit
Reusable influence diagrams.
No full textInfluence Diagrams have been recognized as a suitable formalism for building probabilistic expert systems. Nevertheless, the most part of applications consists in stand-alone systems, concerning a very limited domain. On the other hand, Artificial Intelligence research has outlined Blackboard Architectures as the basis for building expert systems in which several knowledge sources, in general built with different formalisms, cooperate to the solution of a complex task. This paper addresses the use of influence diagrams as knowledge sources of such a system, and particularly faces the problem of reusing the same influence diagram in different inference phases. We will show that, specially in planning tasks, the modularity requirement of keeping the knowledge sources separated, may imply that an influence diagram must call another influence diagram to solve itself and to maintain the coherence of the whole set of decisions underlying the plan. Conditions for the correctness of this concatenation of knowledge sources will be provided, and an example from the medical domain of therapy planning for Acute Myeloid Leukemia will be shown, as an implemented prototype exploiting these ideas
Learning temporal probabilistic causal models from longitudinal data.
No full textMedical problems often require the analysis and interpretation of large collections of longitudinal data in terms of a structural model of the underlying physiological behavior. A suitable way to deal with this problem is to identify a temporal causal model that may effectively explain the patterns observed in the data. Here we will concentrate on probabilistic models, that provide a convenient framework to represent and manage underspecified information; in particular, we will consider the class of Causal Probabilistic Networks (CPN). We propose a method to perform structural learning of CPNs representing time-series through model selection. Starting from a set of plausible causal structures and a collection of possibly incomplete longitudinal data, we apply a learning algorithm to extract from the data the conditional probabilities describing each model. The models are then ranked according to their performance in reconstructing the original time-series, using several scoring functions, based on one-step ahead predictions. In this paper we describe the proposed methodology through an example taken from the diabetes monitoring domain. The selection process is applied to a set of input-output models that generalize the class of ARX models, where the inputs are the insulin and meal intakes and the outputs are the blood glucose levels. Although the physiological process underlying this particular application is characterized by strong non-linearities and low data reliability, we show that it is possible to obtain meaningful results, in terms of conditional probability learning and model ranking power
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