Translational medicine is an emerging effort in medical practice that seeks to transfer scientific results from laboratories to clinical practice, for the patient diagnosis and treatment (also referred as “bench-to-bedside”). The perspective change has occurred recently as a result of the genomics and bioinformatics revolution. However, this situation has been accompanied by a serious problem: the generation of large amounts of information is causing a major bottleneck in medical research and its application. This information is in structured format, mainly related to molecular biology research, as well as text, from research results.
The main goal of the project is to analyse, experiment and develop new text and data mining techniques in an interrelated way, in intelligent medical information systems. New techniques of both types will be developed, more efficient, interrelated, and better adapted to specific aspects of the domain. An intelligent information access system based on them will be developed, offering advanced functionalities able to interrelate medical information, mainly information (text and data) from clinical records and scientific documentation, making use of standard resources of the domain (e.g. UMLS, SNOMED, Gene Ontology). An open source platform will be developed integrating all the elements. An evaluation will be conducted, analysing the new techniques efficacy as well as the whole system, in an open environment with final users.
Translational medicine is an emerging effort in medical practice that seeks to transfer scientific results from laboratories to clinical practice, for the patient diagnosis and treatment (also referred as “bench-to-bedside”). The perspective change has occurred recently as a result of the genomics and bioinformatics revolution. However, this situation has been accompanied by a serious problem: the generation of large amounts of information is causing a major bottleneck in medical research and its application. This information is in structured format, mainly related to molecular biology research, as well as text, from research results.
The main goal of the project is to analyse, experiment and develop new text and data mining techniques in an interrelated way, in intelligent medical information systems. New techniques of both types will be developed, more efficient, interrelated, and better adapted to specific aspects of the domain. An intelligent information access system based on them will be developed, offering advanced functionalities able to interrelate medical information, mainly information (text and data) from clinical records and scientific documentation, making use of standard resources of the domain (e.g. UMLS, SNOMED, Gene Ontology). An open source platform will be developed integrating all the elements. An evaluation will be conducted, analysing the new techniques efficacy as well as the whole system, in an open environment with final users.
Outline
Translational medicine is an emerging effort in medical practice that seeks to transfer scientific results from laboratories to clinical practice, for the patient diagnosis and treatment (also referred as “bench-to-bedside”). The perspective change has occurred recently as a result of the genomics and bioinformatics revolution. However, this situation has been accompanied by a serious problem: the generation of large amounts of information is causing a major bottleneck in medical research and its application. This information is in structured format, mainly related to molecular biology research, as well as text, from research results.
The main goal of the project is to analyse, experiment and develop new text and data mining techniques in an interrelated way, in intelligent medical information systems. New techniques of both types will be developed, more efficient, interrelated, and better adapted to specific aspects of the domain. An intelligent information access system based on them will be developed, offering advanced functionalities able to interrelate medical information, mainly information (text and data) from clinical records and scientific documentation, making use of standard resources of the domain (e.g. UMLS, SNOMED, Gene Ontology). An open source platform will be developed integrating all the elements. An evaluation will be conducted, analysing the new techniques efficacy as well as the whole system, in an open environment with final users.