Information Technology in Bio- and Medical Informatics

The need to integrate, store, process and analyse data is continuously growing as information technologies facilitate the collection of vast amounts of data. These data can be in different repositories, have different data formats and present data quality issues, requiring the adoption of appropriate strategies for data cleaning, integration and storage. After that, suitable data analytics and visualization mechanisms can be used for the analysis of the available data and for the identification of relevant knowledge that support the decision-making process. This paper presents a data analytics perspective over 10 years of pneumonia incidence in Portugal, pointing the evolution and characterization of the mortal victims of this disease. The available data about the individuals was complemented with statistical data of the country, in order to characterize the overall incidence of this disease, following a spatial analysis and visualization perspective that is supported by several analytical dashboards.

Biomedical research requires deep domain expertise to perform analyses of complex data sets, assisted by mathematical expertise provided by data scientists who design and develop sophisticated methods and tools. Such methods and tools not only require preprocessing of the data, but most of all a meaningful input selection. Usually, data scientists do not have sufficient background knowledge about the origin of the data and the biomedical problems to be solved, consequently a doctor-in-the-loop can be of great help here. In this paper we revise the viability of integrating an analysis guided visualization component in an ontology-guided data infrastructure, exemplified by the principal component analysis. We evaluated this approach by examining the potential for intelligent support of medical experts on the case of cerebral aneurysms research.

Information management in healthcare is nowadays experiencing a great revolution. After the impressive progress in digitizing medical data by private organizations, also the federal government and other public stakeholders have also started to make use of healthcare data for data analysis purposes in order to extract actionable knowledge. In this paper, we propose an architecture for supporting interoperability in healthcare systems by exploiting Big Data techniques. In particular, we describe a proposal based on big data techniques to implement a nationwide system able to improve EHR data access efficiency and reduce costs.

The general disease group of malignant neoplasms depicts one of the leading and increasing causes for death. The underlying complexity of cancer demands for abstractions to disclose an exclusive subset of information related to the disease. Our idea is to create a user interface for linking a simulation on cancer modeling to relevant additional publicly and freely available data. We are not only providing a categorized list of open datasets and queryable databases for the different types of cancer and related information, we also identify a certain subset of temporal and spatial data related to tumor growth. Furthermore, we describe the integration possibilities into a simulation tool on tumor growth that incorporates the tumor’s kinetics.

In this study, a new spatio-spectral filtering method for motor imagery signal analysis is introduced. Motor imagery is an important research area in brain computer interfacing. EEG signals related with motor imagery have characteristic frequencies originating from sensorimotor cortex. Common spatial patterns (CSP) method is a very popular and successful spatial filtering algorithm in motor imagery classification. However, CSP only optimizes spatial filters, subject specific frequency selection should be done manually, which is a meticulous process. Therefore, an automatic method for spectral filter optimization is needed. Proposed filter bank common spatio-spectral patterns (FBCSSP) algorithm optimizes spatial and spectral filters. FBCSSP method uses a network of a filter bank and two consecutive CSP layers so that proposed structure has a subject specific response in both spatial and spectral domains. We inspected the proposed method in terms of classification accuracy and physiological consistence of the created filters using publicly available data set. FBCSSP method gave higher classification accuracy than other spatio-spectral pattern methods in the literature. Also, obtained spatial and spectral filters were consistent with the spatial and spectral properties of motor imagery signals.

Segmentation is a crucial part of the signal processing as it has a significant influence on further analysis quality. Adaptive segmentation based on sliding windows is relatively simple, works quite good and can work online. It has however many tunable parameters whose proper values depend on the task and signal type. The paper proposes a method of defining optimal parameters for detection of sleep spindles in electroencephalogram. Segmentation algorithm based on Varri method was utilized. Fitness function was proposed for estimation of agreement between the segmentation result and borders of the target classification. Particle swarm optimization was used to find optimal parameters. On the data of 11 insomniac subjects the method reached \(28\,\%\) improvement in comparison to the baseline method using default parameters.

We present a probabilistic model for classification of micro-EEG signals, recorded during deep brain stimulation surgery for Parkinson’s disease. The model uses parametric representation of neuronal background activity, estimated using normalized root-mean-square of the signal. Contrary to existing solutions using Bayes classifiers or Hidden Markov Models, our model uses smooth state-transitions represented by sigmoid functions, which ensures flexible model structure in combination with general optimizers for parameter estimation and model fitting. The presented model can easily be extended with additional parameters and constraints and is intended for fitting of a 3D anatomical model to micro-EEG data in further perspective. In an evaluation on 260 trajectories from 61 patients, the model showed classification accuracy 90.0 %, which was comparable to existing solutions. The evaluation proved the model successful in target identification and we conclude that its use for more complex tasks in the area of DBS planning and modeling is feasible.

Multidisciplinary Team Meetings (MDTM) are conducted to discuss the treatment of one or more patients. This paper discusses MDTM with a focus on tumor treatment and shows workflows in different settings, identifies organizational and technical problems in the MDTM and solutions thereof. It aims to answer the following research questions: (RQ1) What is the current state of the art in MDTM?(RQ2) How are they conducted and what is the variation in different hospital settings? (RQ3) What technical problems and possible solutions thereof exist? This is done by conducting a literature review entailing a forward search of 837 papers and a backward search. The results show that a unified workflow model for MDTM can’t be found since they are highly dependent on institutional and tumor dependent specifics. The identified problems and solutions show a lack of research towards technical solutions and process interoperability. An outlook on extending research in these areas is given.

We present in this article a multimodal research model for the retrieval of medical images based on the extracted multimedia information from a radiological collaborative social network. However, opinions shared on a medical image in a medical social network constitute a textual description that requires in most of the time cleaning using a medical thesaurus. In addition, we describe the textual description and medical image in a TF-IDF weight vector using an approach of « bag-of-words ». We use latent semantic analysis to establish relationships between textual and visual terms from the shared opinions on the medical image. Multimodal modeling will search for medical information through multimodal queries. Our model is evaluated on the basis ImageCLEFmed’2015 for which we have the ground-truth. We have carried many experiments with different descriptors and many combinations of modalities. Analysis of the results shows that the model is based on two methods can increase the performance of a research system based on only one modality, either visual or textual.

The human being, even if potentially inclined to learn, needs incentives to do it effectively. In these context, the virtual environments could simulate challenges of clinical practice and, at the same time, consider the personal experiences, allows the student’s stimulus and also offer additional theoretical content updated and of excellent quality. The purpose of the project is to develop a Clinical Case Simulation Tool (CCST), it´s supposed to be a supporter to the acquisition of clinical skills for medical education. This is an experimental study of applied technology for health education. The project is multidisciplinary between health sciences, computing and education. The development of an application to store real clinical cases is the starting point of this study. The structure of the proposed clinical case comprises the description of the case, clinical history, complementary tests, questions and further reading. The access to the application is password protected, composed of access profiles with specific characteristics such as teacher, coordinator, student. All clinical cases are linked to a specific college and discipline. The Clinical Cases simulator platform was created for storage of clinical cases and to provide technological support for preparing courses, workshops and support classroom teaching. This may be considered as an innovative approach, given the use of a digital system that enables the storage of clinical data and laboratory tests, as sounds of cardiac auscultation, pulmonary auscultation, images and videos.

In the biological domain, it is more and more common to apply several high-throughput technologies to the same set of samples. We propose a Covariate-Related Structure Extraction approach (CRSE) that explores relationships between different types of high-dimensional molecular data (views) in the context of sample covariate information from the experimental design, for example class membership. Real-world data analysis with an initial pipeline implementation of CRSE shows that the proposed approach successfully captures cross-view structures underlying multiple biologically relevant classification schemes, allowing to predict class labels to unseen examples from either view or across views.

The goal of this work is to recover semantic and structural information from medical documents in electronic format.

Despite the progressive diffusion of Electronic Health Record systems, a lot of medical information, also for legacy reasons, is available to patients and physicians in image-only or textual format. The difficulties of obtaining such information when needed result in high costs for health providers.

In this work we develop the concept of a system designed to convert legacy medical documents into a standard and interoperable format compliant with the Clinical Document Architecture model by the means of semantic annotation.

Data are the crucial component of most computer based clinical decision support systems. This review focuses on data for a system which should improve everyday life of diabetics. The aim is to identify issues arising during the process of acquisition of photos of dishes obtained by diabetic patients. Solutions are proposed that will improve the quality of subsequent processing and final conclusions. This research will lead to a proposal of some guidelines that patients should follow when taking the pictures of dishes. For this purpose, a sample of 906 photos from 6 patients including meals and text records of activities was examined carefully in order to extract useful information about how do diabetics chose to record the details, how much and how long do they follow the suggestions. Based on the analysis, representative examples are presented with corresponding suggestions for each case.

Objective: To identify potential risk factors for low umbilical cord artery pH in term, singleton pregnancies. Methods: Retrospective case-control study. Cases were deliveries characterized by umbilical cord artery \(pH \le 7.05\). Controls were with no sign of hypoxia. Results: In the database of 10637 deliveries, collected between 2014 and 2015 at the University Hospital in Brno delivery ward, we identified 99 cases. Univariate analysis of clinical features was performed. The following risk-factors were associated with low pH: the length of the first stage (odds ratio (OR) 1.40 (95 % CI 1.04–1.89)) and the length of the second stage of labor (OR 2.86 (95 % CI 1.70–4.81)), primipara (OR 2.99 (1.90–4.71)) and meconium stained fluid (OR 1.60 (1.07–2.38)). Conclusion: Among the risk factors that increase the chance of low umbilical cord artery pH at term, we identified: excessive length of the first and second stage of labour, parity, and meconium stained fluid.

In the today’s world we witness an impact of the ‘Big data’ phenomenon. Although there are many definitions and different scientists view the problem from their perspectives (web, IoT, smartphones, security, GIS, HIS, cloud systems, networks, ...), there is still need for efficient, robust and scalable algorithms that ease processing of such data.

Cancer constitutes a condition and is referred to a group of numerous different diseases, that are characterized by uncontrolled cell growth. Tumors, in the broader sense, are described by abnormal cell growth and are not exclusively cancerous. The molecular basis involves a process of multiple steps and underlying signaling pathways, building up a complex biological framework. Cancer research is based on both disciplines of quantitative and life sciences which can be connected through Bioinformatics and Systems Biology. Our study aims to provide an enhanced computational model on tumor growth towards a comprehensive simulation of miscellaneous types of neoplasms. We create model profiles by considering data from selected types of tumors. Growth parameters are evaluated for integration and compared to the different disease examples.

Herein, we describe an extension to the recently presented visualization tool for tumor growth. The integration of profiles offers exemplary simulations on different types of tumors. The enhanced bio-computational simulation provides an approach to predicting tumor growth towards personalized medicine.

The era of “big data” arose the need to have computational tools in support of biological tasks. Many types of bioinformatics tools have been developed for different biological tasks as target, pathway and gene set analysis, but integrated resources able to incorporate a unique web interface, and to manage a biological scenario involving many different data sources are still lacking. In many bioinformatics approaches several data processing and evaluation steps are required to reach the final results. In this work, we face a biological case study by exploiting the capabilities of an integrated multi-component resources database that is able to deal with complex biological scenarios. As example of our problem-solving approach we provide a case study on the analysis of functional effect of miRNA single nucleotide polymorphisms (SNPs) in cancer disease.

Monolithic applications are the most common development paradigm but they have some drawback related to the maintenance, upgrading and scaling. Microservice architectures were recently proposed in order to solve some of these issues, because they are simpler to scale and more flexible. Both architectures use a database and this component can act as a component for micro service. In the paper we present a pattern for microservice architecture that uses a database as component, and this pattern is used in an health record application. We explain also the requirements of the database for this pattern and the advantages achieved.

In this paper, we make an experimental study to compare the performances of different data mining classification algorithms for predicting osteoporosis in Tunisian postmenopausal women. This study aims to identify the best algorithms with the optimum classification parameters values and to determine the most important risk factors that have a significant impact on the osteoporosis occurrence. The obtained results show that Support Vector Machine (SVM) classifier and Artificial Neural Network (ANN) classifier give the best classification performances when dealing with the three bone statuses (normal, osteopenia, osteoporosis). On the other hand, the decision tree classifier C4.5 enables to extract the most important risk factors for osteoporosis occurrence. The selected risk factors are validated by biologists.

With the technology emerging more and more possible applications of process mining in healthcare become apparent. In most cases the goal of applying process mining to the healthcare domain is to find out what actually happened and to deliver a concise assessment of the organizational reality by mining the event logs of health information systems. To develop medical guidelines or patient pathways considering economic aspects and quality of care, a comparative analysis of different existing approaches is useful (e.g. how different hospitals execute the same process in different ways). This work discusses how to use existing process mining techniques for comparative analysis of healthcare processes and presents an approach based on the L* life-cycle model.