Novel Nature Inspired Techniques in Medical Data Mining

AbstractIn this work we have studied, evaluated and proposed different swarm intelligence techniques for mining information from loosely structured medical textual records with no a priori knowledge (a large dataset). The output of this task is a set of ordered/nominal attributes suitable for rule discovery mining.Information mining from textual data becomes a very challenging task when the structure of the text record is loose without any rules. The task becomes even harder when natural language is used and no a priori knowledge is available.First, classical approaches such as basic statistic approaches, single and multiple word frequency analysis, etc., have been used to simplify the textual data and provide an overview of the data. Finally, an ant-inspired self-learning approach has been used to automatically provide a simplified dominant structure, presenting structure of the records in the human readable form that can be further utilized in the mining process as it describes the vast majority of the records.Note that this project is an ongoing process (and research) and new data are irregularly received from the medical facility, justifying the need for robust and fool-proof algorithms

Application of Internet of Things in Health Care

The paper focuses on the continuously growing area of Internet of Things and its application to health care. We discuss several important aspects, namely quality, and relevance of data acquired. We illustrate IoT by a case study of diabetes mellitus personalised treatment. Modern type 1 diabetes mellitus therapy is now unimaginable without intensive glycaemia monitoring. In the last decade the possibility of real time continuous glucose monitoring (RT-CGMS) was realised along with integration to some types of insulin pump. Currently the research focuses on continuous glucose monitoring systems that have following advantages: non-invasiveness, high customer acceptance; comfort in use; ease in use; accuracy; long-term measurement up to 4 weeks; calibrating unit integrated; alerts for low or highs of glucose level; enabling higher lifestyle flexibility, e.g. physical activity, food, medication; wireless data and energy transmission; infection risk is minimised. Obviously several sensors are necessary to acquire the contextual data, in particular vital parameters, physical activity, and stress. All measured data must be collected and evaluated in parallel. The aim is to identify the mutual relations in measured parameters, the differences among patients and finally the most important parameters for development of personalised data models

Audit trails in OpenSLEX : paving the road for process mining in healthcare

The analysis of organizational and medical treatment pro-cesses is crucial for the future development of the healthcare domain. Recent approaches to enable process mining on healthcare data make use of the hospital information systems' Audit Trails. In this work, methods are proposed to integrate Audit Trail data into the generic OpenSLEX meta model to allow for an analysis of healthcare data from different perspectives (e.g. patients, doctors, resources). Instead of flattening the event data in a single log file the proposed methodology preserves as much information as possible in the first stages of data extraction and preparation. By building on established standardized data and message specifications for auditing in healthcare, we increase the range of analysis opportunities in the healthcare domain

Prvky a vztahy v systému odpadového hospodářství

Účelem projektu je vyhodnotit opatření přijímaná podniky z pohledu integrovaného systému řízení a z pohledu výrobku s ukončenou životností, který se stal odpadem. Cílem je najít slabá místa na podnikové úrovni, informace o nich je podkladem pro analýzu příčin a je podkladem pro státní správu, která může na tomto věcném základě zvolit a podporovat nástroje působící na uzavírání materiálových toků, předcházení vzniku odpadů, snižování jejich nebezpečnosti a minimalizace množství nevyužívaných odpadů. V roce 2004 byly zpracovány studie k etapě B1.1 Podklad k vytvoření modelu produkční a odbytové bilance systému odpadového hospodářství a B1.2 Studie k návrhu metodiky