Bioinformatics in Italy: BITS2011, the Eighth Annual Meeting of the Italian Society of Bioinformatics

The BITS2011 meeting, held in Pisa on June 20-22, 2011, brought together more than 120 Italian researchers working in the field of Bioinformatics, as well as students in Bioinformatics, Computational Biology, Biology, Computer Sciences, and Engineering, representing a landscape of Italian bioinformatics research

Decision-making for tracheostomy in amyotrophic lateral sclerosis (ALS): a retrospective study

Background: ALS patients should discuss the issue of tracheostomy before the onset of terminal respiratory failure. While the process of shared decision-making is desirable, there are few data on the practical application of this real-life situation. Aim of the study: To determine how a decision-making process is actually carried out, we analysed the episodes of acute respiratory failure preceding tracheostomy. Methods: We studied the charts of a group of ALS patients after tracheostomy. An interview focusing on the existence of anticipated directives was carried out. Tracheostomies were classified as planned or unplanned according to the presence of a decision plan. Results: A total of 209 ALS patients were cared for during a three-year period. Of these patients, 34 (16%) were tracheotomised. In 38% of cases, tracheostomy was planned, 41% were unplanned, and 21% remained undiagnosed. Conclusions: A minority of ALS patients make a voluntary decision for tracheostomy before the procedure is conducted. The advising process of care still presents limits that have been thus far poorly addressed. In the future, we will need to develop guidelines for the timing and content of the shared-decision making process

An ICT infrastructure to integrate clinical and molecular data in oncology research.

BACKGROUND: The ONCO-i2b2 platform is a bioinformatics tool designed to integrate clinical and research data and support translational research in oncology. It is implemented by the University of Pavia and the IRCCS Fondazione Maugeri hospital (FSM), and grounded on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) research center. I2b2 has delivered an open source suite based on a data warehouse, which is efficiently interrogated to find sets of interesting patients through a query tool interface. METHODS: Onco-i2b2 integrates data coming from multiple sources and allows the users to jointly query them. I2b2 data are then stored in a data warehouse, where facts are hierarchically structured as ontologies. Onco-i2b2 gathers data from the FSM pathology unit (PU) database and from the hospital biobank and merges them with the clinical information from the hospital information system. Our main effort was to provide a robust integrated research environment, giving a particular emphasis to the integration process and facing different challenges, consecutively listed: biospecimen samples privacy and anonymization; synchronization of the biobank database with the i2b2 data warehouse through a series of Extract, Transform, Load (ETL) operations; development and integration of a Natural Language Processing (NLP) module, to retrieve coded information, such as SNOMED terms and malignant tumors (TNM) classifications, and clinical tests results from unstructured medical records. Furthermore, we have developed an internal SNOMED ontology rested on the NCBO BioPortal web services. RESULTS: Onco-i2b2 manages data of more than 6,500 patients with breast cancer diagnosis collected between 2001 and 2011 (over 390 of them have at least one biological sample in the cancer biobank), more than 47,000 visits and 96,000 observations over 960 medical concepts. CONCLUSIONS: Onco-i2b2 is a concrete example of how integrated Information and Communication Technology architecture can be implemented to support translational research. The next steps of our project will involve the extension of its capabilities by implementing new plug-in devoted to bioinformatics data analysis as well as a temporal query module

CARDIO-i2b2: integrating arrhythmogenic disease data in i2b2.

The CARDIO-i2b2 project is an initiative to customize the i2b2 bioinformatics tool with the aim to integrate clinical and research data in order to support translational research in cardiology. In this work we describe the implementation and the customization of i2b2 to manage the data of arrhytmogenic disease patients collected at the Fondazione Salvatore Maugeri of Pavia in a joint project with the NYU Langone Medical Center (New York, USA). The i2b2 clinical research chart data warehouse is populated with the data obtained by the research database called TRIAD. The research infrastructure is extended by the development of new plug-ins for the i2b2 web client application able to properly select and export phenotypic data and to perform data analysis

The ONCO-I2b2 Project: Integrating Biobank Information and Clinical Data to Support Translational Research in Oncology.

Abstract The University of Pavia and the IRCCS Fondazione Salvatore Maugeri of Pavia (FSM), has recently started an IT initiative to support clinical research in oncology, called ONCO-i2b2. ONCO-i2b2, funded by the Lombardia region, grounds on the software developed by the Informatics for Integrating Biology and the Bedside (i2b2) NIH project. Using i2b2 and new software modules purposely designed, data coming from multiple sources are integrated and jointly queried. The core of the integration process stands in retrieving and merging data from the biobank management software and from the FSM hospital information system. The integration process is based on a ontology of the problem domain and on open-source software integration modules. A Natural Language Processing module has been implemented, too. This module automatically extracts clinical information of oncology patients from unstructured medical records. The system currently manages more than two thousands patients and will be further implemented and improved in the next two years

Renal tubular impairment during riluzole therapy

clinical/scientific not

Renal tubular impairment during riluzole therapy.

clinical/scientific not

From data to the decision: A software architecture to integrate predictive modelling in clinical settings

The application of statistics and mathematics over large amounts of data is providing healthcare systems with new tools for screening and managing multiple diseases. Nonetheless, these tools have many technical and clinical limitations as they are based on datasets with concrete characteristics. This proposition paper describes a novel architecture focused on providing a validation framework for discrimination and prediction models in the screening of Type 2 diabetes. For that, the architecture has been designed to gather different data sources under a common data structure and, furthermore, to be controlled by a centralized component ( Orchestrator) in charge of directing the interaction flows among data sources, models and graphical user interfaces. This innovative approach aims to overcome the data-dependency of the models by providing a validation framework for the models as they are used within clinical settings