DIETI Contact person / Partner: Francesco Verde

Abstract

In subjects with autoimmunity (i.e., multiple sclerosis), an important aspect of the immune system is how T lymphocytes respond to T cell receptor (TCR) stimulation. This is a key event controlling the fate of naïve T lymphocytes impacting on activation, differentiation and regulation of the immune response, which has been shown to be impaired in subjects with autoimmunity. T cell responsiveness is inherently stochastic (i.e., it cannot be predicted a priori) due to different sources of noise that are intrinsically produced by the underlying biochemical reactions. Such a noise may influence T cell activation grade by degrading the information transmission from the TCR.

Objective

The aim is to help the oncologist to better understand when a change of therapy is needed through machine learning methodologies and statistical analysis of clinical variables such as the patient's medical condition, the aggressiveness of the cancer, the effectiveness of the treatments.

DIETI Contact person / Partner: Antonio Maria Rinaldi

Abstract

The need of having advanced tools for data analysis is one of the most important trends in the current economic, technological and social scenario and, nowadays, it is mainly based on methodologies and techniques of artificial intelligence and BigData. One of the goals of modern medicine is the "precision medicine", whose purpose is to offer personalized treatments based on the specific characteristics of patients and pathologies. In this context, new analysis techniques such as Radiomics is becoming a novel research field. The analysis of the large number of data derived from medical images allows to recognize many characteristics of cancers and they can be integrated with other molecular and genomic characteristics by obtaining further correlations.

Objective

The implementation of a decision support system based on a sematic multimedia bigdata to support the analysis of medical images represented by novel radiomics descriptors and texture analysis.

DIETI Contact person / Partner: Nicola Pasquino

Abstract

In oncological patients, pain is one of the most disabling symptoms. The correct approach to pain treatment has a positive impact on her prospectives to afford other therapies for recovery. Therapist defines treatment based on subjective pain scales and continuously refine drug dosages, based on the patient responses, in a loop that sometimes hardly converging. This project aims at creating an automatic pain assessment protocol, based on the collection of multimodal data generated during a patient's life. Patients will be remotely monitored, recording texts (questionnaires), video (self-report), biometric parameters (recorded via personal devices), drug timing scheduling, etcetera. This project falls in the area of big data technologies for health. Physicians will cooperate with computer scientists and data analysts to design, process, and evaluate this flux of data to be used both for real-time monitoring and training of AI systems.

Objective

• the creation of a dataset for pain assessment substituting subjective scale for self-evaluation;
• the definition of an interventionist protocol in the field of refining treatments and drug dosages;
• the design of AI-based decision making systems in the field of big data for oncological purposes.

DIETI Contact person / Partner: Anna Rita Fasolino

Abstract

Software applications for the Health care are becoming more and more widespread and complex, as they can rely on the most modern ICT technologies. To cite a few, they can be developed as event-based and context-aware mobile systems, can be integrated with distributed software architectures for data collection, use machine learning techniques to support decision-making processes, or exploit virtual or augmented reality technologies. To manage the complexity of developing this type of system and assure their quality, acceptability and dependability, it is increasingly necessary to invest in the search for new development paradigms, architectures and techniques that simplify their development and validation.

Objective

• Develop innovative model driven solutions for the development, testing, validation and certification of mobile, sensor-based and context-aware solutions for health care domain;
• Carry out experimental validation of the proposed approaches by case studies in selected health care fields