ArticleBMC bioinformatics2012
An ICT infrastructure to integrate clinical and molecular data in oncology research.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02459743 (Molecular Disease Profile of Haematological Malignancies. A Prospective Registry Study by the Rete Ematologica Lombarda), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Molecular Disease Profile of Haematological Malignancies. A Prospective Registry Study by the Rete Ematologica Lombarda (REL) Clinical Network
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled it
- What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions.JMIR formative research · 2020Review
- A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.Journal of biomedical informatics · 2019Review
- Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools.Journal of healthcare engineering · 2019Article
- Study of the uses of Information and Communication Technologies by Pain Treatment Unit Physicians.Journal of medical systems · 2017Article
- BigQ: a NoSQL based framework to handle genomic variants in i2b2.BMC bioinformatics · 2015Article
- Big Data: the challenge for small research groups in the era of cancer genomics.British journal of cancer · 2015Review
- Design and evaluation of a multimedia electronic patient record "oncoflow" with clinical workflow assistance for head and neck tumor therapy.International journal of computer assisted radiology and surgery · 2014Article
- Personalized Oncology Suite: integrating next-generation sequencing data and whole-slide bioimages.BMC bioinformatics · 2014Article
- Mission and Sustainability of Informatics for Integrating Biology and the Bedside (i2b2).EGEMS (Washington, DC) · 2014Article
- A digital repository with an extensible data model for biobanking and genomic analysis management.BMC genomics · 2014Article
- Bioinformatics in Italy: BITS2011, the Eighth Annual Meeting of the Italian Society of Bioinformatics.BMC bioinformatics · 2012Article
- A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsOnco-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.
resultsOnco-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.
conclusionsOnco-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.