Data Mining Techniques for Complex User-Generated Data

Nowadays, the amount of collected information is continuously growing in a variety of different domains. Data mining techniques are powerful instruments to effectively analyze these large data collections and extract hidden and useful knowledge. Vast amount of User-Generated Data (UGD) is being created every day, such as user behavior, user-generated content, user exploitation of available services and user mobility in different domains. Some common critical issues arise for the UGD analysis process such as the large dataset cardinality and dimensionality, the variable data distribution and inherent sparseness, and the heterogeneous data to model the different facets of the targeted domain. Consequently, the extraction of useful knowledge from such data collections is a challenging task, and proper data mining solutions should be devised for the problem under analysis. In this thesis work, we focus on the design and development of innovative solutions to support data mining activities over User-Generated Data characterised by different critical issues, via the integration of different data mining techniques in a unified frame- work. Real datasets coming from three example domains characterized by the above critical issues are considered as reference cases, i.e., health care, social network, and ur- ban environment domains. Experimental results show the effectiveness of the proposed approaches to discover useful knowledge from different domains

PATIENT WP4-Deliverable: Curriculum for Handover Training in Medical Education [Public Part]

What is handover? Handover is the accurate, reliable communication of task-relevant information between doctors and patients and from one care-giver to another. This occurs in many situations in healthcare. Why is handover important? Improperly conducted handovers lead to wrong treatment, delays in medical diagnosis, life threatening adverse events, patient complaints, increased health care expenditure, increased length of stay hospital and a range of other effects that impact on the health system(1). This is how accurate performed and well-structured handovers improve patient safety, i.e. “absence of preventable harm to a patient during the process of health care” (2). How to teach handover? The best way to teach practical skills is, to let students perform the skill. To decrease the risk for real patients simulation is the teaching method of choice. Therefore and on the basis of the project’s preceding results (3,4), this curriculum is divided into three modules: Module 1 – Risk and Error Management Module 2 – Effective Communication Module 3 – SimulationPATIEN

Addressing Ascertainment Bias in the Study of Cardiovascular Disease Burden in Opioid Use Disorders - Application of Natural Language Processing of Electronic Health Records

In the United States, the prevalence of long-term exposure to opioid drugs, for both medically and nonmedically indicated purposes, has increased considerably since the mid-1990’s. Concerns have emerged about the potential health effects of opioid use. There is also growing interest in other possible connections with opioid use including cardiovascular disease. Electronic health records (EHR) contain information about patient care in the form of structured codes and unstructured notes. Natural language processing (NLP) provides a tool for processing unstructured textual data in EHR clinical notes and extracts useful information for research with structured formats. The purpose of this dissertation was to 1) to summarize peer-reviewed literature on the association between non-acute opioid and cardiovascular disease (CVD) and identify the gap of this research topic; 2) to apply NLP algorithm to estimate the extent of opioid use disorder (OUD) among hospital inpatients that cannot be identified using ICD-10-CM codes; and 3) to determine the extent to which estimates of the association between OUD and CVD may be biased by misclassification of OUD cases that are not identifiable using ICD-10-CM codes. First, we conducted a scoping review of the epidemiological literature on nonacute opioid use and CVD. We summarized the current evidence about the association between NOU and CVD, and identified some open questions on this topic. Then, we developed a Natural Language Processing algorithm to identify cases of OUD in electronic healthcare records that were not assigned an ICD-10-CM code for OUD by medical records coders, but for which strong evidence of OUD exists in the unstructured clinical notes. Lastly, we estimated the association between OUD and six types of CVD, arrhythmia, myocardial infarction, stroke, heart failure, ischemic heart disease, and infective endocarditis, classifying OUD in two ways: defining OUD cases by ICD-10-CM codes alone, and using a combination of cases identified by ICD-10-CM codes and cases identified using NLP algorithm. We assessed the effect of misclassification of OUD status when using ICD-10-CM codes alone

Magnetic Resonance Imaging to Enhance the Diagnosis of Fetal Brain Abnormalities in utero

Purpose This thesis aims to determine the diagnostic performance of in utero MR (iuMR) imaging to diagnose fetal brain abnormalities and describes the development, application and processing of a 3D volume MR acquisition. Methods A systematic review and meta-analysis of existing evidence was conducted. A prospective multicentre study of pregnant women, with a fetal brain abnormality on ultrasound (USS), was undertaken – The MERIDIAN study. In addition, an investigation of fetuses with no brain abnormality on USS was undertaken. Diagnostic accuracy and confidence, as well as positive and negative predictive values, were calculated. A 3D image acquisition technique was introduced, its ability to aid diagnosis measured and computational post-processing applied. Fetal brain volumes were extracted from the 3D data using image segmentation and these were assessed for reproducibility and validity. Resultant data allowed 3D models of fetal brains to be printed. Normally developing fetal brain volumes were plotted graphically thereby allowing comparison with abnormal fetuses. Results A total of 570 complete datasets were available from 903 eligible participants. Diagnostic accuracy was 68% for USS and 93% for iuMR. 95% of diagnoses made by iuMR were reported with high confidence compared to 82% on USS. Changes in pregnancy management occurred in 33% of cases. Positive and negative predictive values of iuMR were 93% and 99.5% respectively. 3D image quality was diagnostic in 89.6%, of which 91.4% gave an accurate diagnosis. Intra- and inter-observer agreement of brain volume measurements was high. Agreement between computer based and brain model volume measurements was also high. Conclusions iuMR imaging improves diagnostic accuracy and confidence for fetal brain abnormalities, influencing pregnancy management in a high proportion of cases. 3D imaging enables versatile visualisation of fetal brain anatomy and reliable extraction of volumes. This additional quantitative information could improve diagnosis in relevant cases