Clustering Lab value working with medical data

Data mining is a relatively new field of research that its objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available [27]. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this thesis is to evaluate data mining tools in medical and health care applications to develop a tool that can help make rather accurate decisions. In this thesis, the goal is finding a pattern among patients who got pneumonia by clustering of lab data values which have been recorded every day. By this pattern we can generalize it to the patients who did not have been diagnosed by this disease whose lab values shows the same trend as pneumonia patients does. There are 10 tables which have been extracted from a big data base of a hospital in Jena for my work .In ICU (intensive care unit), COPRA system which is a patient management system has been used. All the tables and data stored in German Language database.

Securing medical research data with a rights management system

We propose a digital rights management approach for sharing electronic health records for research purposes and argue advantages of the approach. We give an outline of our implementation, discuss challenges that we faced and future directions.

Fertility-preservation counselling and treatment for medical reasons: data from a multinational network of over 5000 women.

Fertility-preservation techniques for medical reasons are increasingly offered in national networks. Knowledge of the characteristics of counselled patients and techniques used are essential. The FertiPROTEKT network registry was analysed between 2007 and 2013, and included up to 85 university and non-university centres in Germany, Austria and Switzerland; 5159 women were counselled and 4060 women underwent fertility preservation. In 2013, fertility-preservation counselling for medical reasons increased significantly among nullipara and women aged between 21 and 35 years (n = 1043; P < 0.001). Frequency of GnRH applications slowly decreased, whereas tissue, oocytes and zygote cryopreservation increased. In 2013, women with breast cancer mainly opted for tissue freezing, whereas women with lymphoma opted for GnRH agonist. Women younger than 20 years predominantly opted for GnRH agonists and ovarian tissue cryopreservation; women aged between 20 and 40 years underwent a variety of techniques; and women over 40 years opted for GnRH agonists. The average number of aspirated oocytes per stimulation cycle decreased as age increased (< 30 years: 12.9; 31-35 years: 12.3; 36-46: 9.0; > 41 years: 5.7). For ovarian tissue cryopreservation, removal and cryopreservation of fewer than one ovary was preferred and carried out in 97% of cases in 2013.

Cloud Computing Service for Managing Large Medical Image Data-Sets Using Balanced Collaborative Agents

Managing large medical image collections is an increasingly demanding important issue in many hospitals and other medical settings. A huge amount of this information is daily generated, which requires robust and agile systems. In this paper we present a distributed multi-agent system capable of managing very large medical image datasets. In this approach, agents extract low-level information from images and store them in a data structure implemented in a relational database. The data structure can also store semantic information related to images and particular regions. A distinctive aspect of our work is that a single image can be divided so that the resultant sub-images can be stored and managed separately by different agents to improve performance in data accessing and processing. The system also offers the possibility of applying some region-based operations and filters on images, facilitating image classification. These operations can be performed directly on data structures in the database.

Feasibility of developing a medical condition data collection system for driver licensing. Final report.

National Highway Traffic Safety Administration, Office of Driver and Pedestrian Research, Washington, D.C.

Architecture of a community medical history data base /

"UIUCDCS-R-73-589"

Online mining abnormal period patterns from multiple medical sensor data streams

With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient's status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients' streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.

Data integration from two microarray platforms identifies bi-allelic genetic inactivation of RIC8A in a breast cancer cell line

Background: Using array comparative genomic hybridization (aCGH), a large number of deleted genomic regions have been identified in human cancers. However, subsequent efforts to identify target genes selected for inactivation in these regions have often been challenging. Methods: We integrated here genome-wide copy number data with gene expression data and non-sense mediated mRNA decay rates in breast cancer cell lines to prioritize gene candidates that are likely to be tumour suppressor genes inactivated by bi-allelic genetic events. The candidates were sequenced to identify potential mutations. Results: This integrated genomic approach led to the identification of RIC8A at 11p15 as a putative candidate target gene for the genomic deletion in the ZR-75-1 breast cancer cell line. We identified a truncating mutation in this cell line, leading to loss of expression and rapid decay of the transcript. We screened 127 breast cancers for RIC8A mutations, but did not find any pathogenic mutations. No promoter hypermethylation in these tumours was detected either. However, analysis of gene expression data from breast tumours identified a small group of aggressive tumours that displayed low levels of RIC8A transcripts. qRT-PCR analysis of 38 breast tumours showed a strong association between low RIC8A expression and the presence of TP53 mutations (P = 0.006). Conclusion: We demonstrate a data integration strategy leading to the identification of RIC8A as a gene undergoing a classical double-hit genetic inactivation in a breast cancer cell line, as well as in vivo evidence of loss of RIC8A expression in a subgroup of aggressive TP53 mutant breast cancers.

High-dose therapy and autologous stem cell transplantation in first relapse for diffuse large B cell lymphoma in the rituximab era:an analysis based on data from the European Blood and Marrow Transplantation Registry

Autologous stem cell transplantation (ASCT) consolidation remains the treatment of choice for patients with relapsed diffuse large B cell lymphoma. The impact of rituximab combined with chemotherapy in either first- or second-line therapy on the ultimate results of ASCT remains to be determined, however. This study was designed to evaluate the benefit of ASCT in patients achieving a second complete remission after salvage chemotherapy by retrospectively comparing the disease-free survival (DFS) after ASCT for each patient with the duration of the first complete remission (CR1). Between 1990 and 2005, a total of 470 patients who had undergone ASCT and reported to the European Blood and Bone Transplantation Registry with Medical Essential Data Form B information were evaluated. Of these 470 patients, 351 (74%) had not received rituximab before ASCT, and 119 (25%) had received rituximab before ASCT. The median duration of CR1 was 11 months. The median time from diagnosis to ASCT was 24 months. The BEAM protocol was the most frequently used conditioning regimen (67%). After ASCT, the 5-year overall survival was 63% (95% confidence interval, 58%-67%) and 5-year DFS was 48% (95% confidence interval, 43%-53%) for the entire patient population. Statistical analysis showed a significant increase in DFS after ASCT compared with duration of CR1 (median, 51 months versus 11 months; P < .001). This difference was also highly significant for patients with previous exposure to rituximab (median, 10 months versus not reached; P < .001) and for patients who had experienced relapse before 1 year (median, 6 months versus 47 months; P < .001). Our data indicate that ASCT can significantly increase DFS compared with the duration of CR1 in relapsed diffuse large B cell lymphoma and can alter the disease course even in patients with high-risk disease previously treated with rituximab.

Medical information extraction in European Portuguese

The electronic storage of medical patient data is becoming a daily experience in most of the practices and hospitals worldwide. However, much of the data available is in free-form text, a convenient way of expressing concepts and events, but especially challenging if one wants to perform automatic searches, summarization or statistical analysis. Information Extraction can relieve some of these problems by offering a semantically informed interpretation and abstraction of the texts. MedInX, the Medical Information eXtraction system presented in this document, is the first information extraction system developed to process textual clinical discharge records written in Portuguese. The main goal of the system is to improve access to the information locked up in unstructured text, and, consequently, the efficiency of the health care process, by allowing faster and reliable access to quality information on health, for both patient and health professionals. MedInX components are based on Natural Language Processing principles, and provide several mechanisms to read, process and utilize external resources, such as terminologies and ontologies, in the process of automatic mapping of free text reports onto a structured representation. However, the flexible and scalable architecture of the system, also allowed its application to the task of Named Entity Recognition on a shared evaluation contest focused on Portuguese general domain free-form texts. The evaluation of the system on a set of authentic hospital discharge letters indicates that the system performs with 95% F-measure, on the task of entity recognition, and 95% precision on the task of relation extraction. Example applications, demonstrating the use of MedInX capabilities in real applications in the hospital setting, are also presented in this document. These applications were designed to answer common clinical problems related with the automatic coding of diagnoses and other health-related conditions described in the documents, according to the international classification systems ICD-9-CM and ICF. The automatic review of the content and completeness of the documents is an example of another developed application, denominated MedInX Clinical Audit system.

Enhancing medical named entity recognition with an extended segment representation technique

Objective : The objective of this paper is to formulate an extended segment representation (SR) technique to enhance named entity recognition (NER) in medical applications.

Methods : An extension to the IOBES (Inside/Outside/Begin/End/Single) SR technique is formulated. In the proposed extension, a new class is assigned to words that do not belong to a named entity (NE) in one context but appear as an NE in other contexts. Ambiguity in such cases can negatively affect the results of classification-based NER techniques. Assigning a separate class to words that can potentially cause ambiguity in NER allows a classifier to detect NEs more accurately; therefore increasing classification accuracy.

Results : The proposed SR technique is evaluated using the i2b2 2010 medical challenge data set with eight different classifiers. Each classifier is trained separately to extract three different medical NEs, namely treatment, problem, and test. From the three experimental results, the extended SR technique is able to improve the average F1-measure results pertaining to seven out of eight classifiers. The kNN classifier shows an average reduction of 0.18% across three experiments, while the C4.5 classifier records an average improvement of 9.33%.

A laboratory study of the transfer of U-234 and U-238 during water-rock interactions in the Carnmenellis granite (Cornwall, England) and implications for the interpretation of field data

Laboratory time-scale experiments were conducted on gravels from the Carnmenellis granite, Cornwall, England, with the purpose of evaluating the release of natural uranium isotopes to the water phase. The implications of these results for the production of enhanced U-234/U-238 activity ratios in Cornish groundwaters are discussed. It is suggested that the U-234/U-238 lab data can be used to interpret activity ratios from Cornwall, even when the observed inverse relationship between dissolved U and U-234/U-238 in leachates/etchates is taken into account. (C) 2001 Elsevier B.V. Ltd. All rights reserved.

The application of medical informatics to the veterinary management programs at companion animal practices in Alberta, Canada: a case study.

Companion animals closely share their domestic environment with people and have the potential to, act as sources of zoonotic diseases. They also have the potential to be sentinels of infectious and noninfectious, diseases. With the exception of rabies, there has been minimal ongoing surveillance of, companion animals in Canada. We developed customized data extraction software, the University of, Calgary Data Extraction Program (UCDEP), to automatically extract and warehouse the electronic, medical records (EMR) from participating private veterinary practices to make them available for, disease surveillance and knowledge creation for evidence-based practice. It was not possible to build, generic data extraction software; the UCDEP required customization to meet the specific software, capabilities of the veterinary practices. The UCDEP, tailored to the participating veterinary practices', management software, was capable of extracting data from the EMR with greater than 99%, completeness and accuracy. The experiences of the people developing and using the UCDEP and the, quality of the extracted data were evaluated. The electronic medical record data stored in the data, warehouse may be a valuable resource for surveillance and evidence-based medical research.