A Smart Guide towards the Diagnosis of Systemic Amyloidosis
Systemic amyloidoses are a family of rare diseases that can cause multi-organ failure and be life-threatening. It is acknowledged and emphasized by public health strategies that the first priority to respond to rare diseases consists in improving their visibility and recognition, favoring an early diagnosis and, if possible, an early treatment. Due to the complexity and variety of rare diseases, they are usually managed and treated in a few referral centers that have the knowledge needed to assess diagnosis and treatment as well as the necessary tools (e.g. biomarkers, diagnostic techniques) to do so. The current practice to redirect patients to a few referral centers is indeed a way to guarantee the best quality of care, but in absence of effective public strategies promoting the recognition of patients affected by rare diseases on large scale, many patients remain deprived of this solution. This problem is even more dramatic whenever the disease is life threatening. In those cases, the diagnostic delay may lead to an early death despite of treatment initiation.
The main objectives of the SMART-Amy project are:
to develop a framework according to which it is possible to formally assess the degree to which a consensus diagnostic algorithm for systemic amyloidoses is emerging, integrating for instance the International Society of Amyloidosis (ISA) guidelines and best practices applied by referral centers;
to build a universal representation of the reality covered by diagnostic guidelines for systemic amyloidoses according to Ontological Realism and the OBO Foundry principles;
to build an ontology based-clinical decision support system (CDSS) to make more reliably a diagnosis of systemic amyloidosis.
As an ancillary branch of the project core, inspired by patients’ needs, we are implementing the AmyGuide interactive tool for smartphones and tablets in order to provide, both online and offline, patient-oriented information, combining information about the disease with hints to promote health behaviors and self-care, and to manage the demand for health services. It is built on top of the Gquest application for Android operating systems, a platform conceived for computerizing and administering questionnaires.
Paola Russo, MD, specialist in Internal Medicine, conceived the project as her doctoral thesis in Bioengineering and Bioinformatics.
Prof. Silvana Quaglini (tutor)
Prof. Werner Ceusters (co-tutor and external collaborator). He is currently Professor in the Psychiatry Department of the School of Medicine and Biomedical Sciences, SUNY at Buffalo NY, Director of the Ontology Research Group of the New York State Center of Excellence in Bioinformatics and Life Sciences, and Director of Research of the UB Institute for Healthcare Informatics.
Ontology Research Group (Referent Tracking Unit, RTU) of the University at Buffalo (Buffalo NY, USA), directed by Prof. Werner Ceusters.
Biomedical Knowledge Engineering (BIKE) Laboratory of the Seoul National University (Seoul, Republic of Korea), directed by Prof. Kim Hong-Gee.