Petr Vcelak

Semantically interoperable research medical data and meta data extraction strategy

Generally, the interoperability is the key feature to any widely used application or an information system. A system build on the basis of semantic data interpretation can also lead to an easy extensible and adaptable system, in the future. Otherwise, you have to deal with a lack of agreed terminologies or codes between standards e.g. DASTA vs. HL7, different coding structures and even variety of file formats or inconsistent database table schema. The most of these difficulties can be solved by the semantically interoperable system. We present our implementation strategy and meta data extraction methods for a research information system with an heterogeneous medical data. The medical data can have different origin, type, file format and even its version. We discuss the research information system that we primarily use for cerebrovascular brain diseases research.

On-line Blood Glucose Level Calculation

Diabetes is a silent disease. It is the 8th most common cause of death that does not hurt until it is too late and the disease has developed. Technology plays a vital role in managing diabetes and educating patients about importance of the treatment. The patient must be able to manage his blood glucose level. However, blood glucose level is measured sporadically as it causes important discomfort to the patient. Measuring glucose level in subcutaneous tissue is minimally invasive technique and thus considerably comfortable, but this level may be different from blood glucose level. We implemented a recently proposed method of blood glucose level calculation from the continuously measured subcutaneous tissue glucose level. Then, we developed a web portal that makes this method accessible to any doctors office and any diabetic patient. To the best of our knowledge, we are the very first web portal that does this. In this paper, we describe the portal.

Inflammatory bowel disease incidence in Czech children: A regional prospective study, 2000-2015

AIM To examine the incidence and trends in pediatric inflammatory bowel diseases (IBDs) over 2000-2015 and project the incidence to 2018. METHODS A 16-year prospective study of IBD patients < 19 years of age was conducted in the Czech Republic (the Pilsen region). All incident IBD cases within a well-defined geographical area were retrieved from a prospectively collected computerized clinical database. Historical Czech data were used for comparison (1990-2001). Our catchment population was determined from the census data. We calculated the incidence by relating the number of newly diagnosed cases to the size of the pediatric population-at-risk in each calendar year. Age/sex, disease type, place of residence, and race/ethnicity were identified. RESULTS In total, 170 new IBD cases [105 Crohn’s disease (CD), 48 ulcerative colitis (UC), and 17 IBD-unclassified (IBD-U)] were identified. The median age at IBD diagnosis was 14.2 years, 59.4% were males, and 97.1% were Caucasians. A male preponderance of IBD (P = 0.026) and CD (P = 0.016) was observed. With 109209 person-years in the catchment area, the average incidence of IBD per 100000 person-years was 10.0 (6.2 for CD, 2.8 for UC, and 1.0 for IBD-U) for children aged 0 to 19 years; for those aged 0 to 15 years, the incidence rate was 7.3 (4.6 for CD, 2.0 for UC, and 0.7 for IBD-U). An increase in incidence with age was observed (P = 0.0003). Over the 16-year period, the incidence increased for IBD patients (P = 0.01) and CD in particular (P < 0.0001), whereas the incidence for UC (P = 0.09) and IBD-U (P = 0.339) remained unchanged. IBD-projected data from 2016 to 2018 were 12.1, 12.3 and 12.6 per 100000 person-years, respectively. CONCLUSION Pediatric-onset IBD incidence is around its highest point. The increase, which is particularly pronounced for CD, may be challenging to relate to causes of pediatric disease.

Cerebrovascular diseases research database

Cerebrovascular diseases are one of the most common causes of death worldwide. In this paper, we analyze relationships in heterogeneous collaborating centres medical data to resolve a solution of this complex problem. Data mining is primarily based on clinical data, imaging examinations and therapeutic data stored in various data formats. The raw and mined data can be used by a registered medical doctors in the knowledge base for an evaluation of hypothesis or tests.

Collaboration and Research Information System Used for Sustainable Long-Term Research

We have to deal with some crucial difficulties when more teams collaborate in a couple of research areas at the same time. These difficulties complicates our long-term medical research and its sustainability. Each researcher use quite different data, use different tools and develops its own advanced methods for data processing. Without a commonly accepted rules there were a lot of misunderstanding, data loss or even software methods loss. Research Information System is primarily used for members collaboration and managing source data, software and research results for its life-time.

Acquisition of Confidential Patient Data Over Shared Mobile Device.

Mobile devices have already been designed for many applications. Smartphones and tablet computers are modern, widespread and affordable solutions used for various purposes. Nowadays mobile devices are widely used in telemedicine. It is usually assumed, that the device is owned and used by a single person. We focus on security concerns and constraints from a different point of view – when the device is shared. In this paper, we are proposing a novel approach to prevent leakage of patient’s confidential data when the device is used by multiple patients at the hospital’s clinic or department. We present a prototype application and discuss its use case and designed workflow.

Ontology-driven information system and management of heterogeneous research data

We adopt an ontology-driven information system design for research purposes. It is adaptable and extensible solution in the situation where the main features are the lack of a stable data model and changing and varying requests. We use ontology-driven design primarily as a collaboration tool for the research. Members directly collaborate and use this information system to ease them tasks with management of source/raw data, software applications and research results for its whole life-time. A different heterogeneous data kinds and a large volume data sets could be stored and shared together with its description meta data between research teams and its members in an uniform manner. It leads to an opportunity to process a stored data using proper methods and tools in a parallel and distributed computing environment. All achieved results are automatically stored and supplemented by an ontology-driven description meta data.

MedIDEA — Medical image data extraction and analysis

The most indicated imaging technique before operations is computed tomography. Preoperative deliberation requires not only its own evaluation of pathological findings, but also the overall anatomy, perfusion and volume of various anatomical parts, which must be accurately identified. There is a wide range for computer-assisted diagnosis, which should reduce and refine the assessment carried out by man and help better decide on the appropriate therapeutic procedure (resection, multiphase power, chemotherapy, ablation methods), and, among other things, reduce the number of unnecessary surgical procedures while on the other hand enabling the implementation of extensive resection. We aims to create a comprehensive system for computed imaging, which would allow the detection and analysis of focal lesions, determination of their segmental localisation, assessment of perfusion conditions and performance of a virtual resection including the establishment of volume resected and organ remaining. Such a system is not yet commercially available.

MetaMed — Medical meta data extraction and manipulation tool used in the semantically interoperable research information system

The MetaMed is a tool for data and meta data extraction primarily from a heterogeneous medical data. It is a non-interactive command line open source application for processing a large amount of data. An extracted meta data is stored in the Resource Description Framework and is structured by OWL ontologies. It enables a semantic interoperability. Meta data as an index data prevents us to solve different file format and version of raw files again and again for a following research. It enables a better performance. Indexed data can be directly used in a user interface and querying for an appropriate raw data can be done. The MetaMed and the research information system is confirmed with research primarily focused on a cerebrovascular diseases.