A5012918421- Biomedical Text Mining and Ontologies
- Semantic Web and Ontologies
- Clinical practice guidelines implementation
- Electronic Health Records Systems
- Business Process Modeling and Analysis
- Advanced Database Systems and Queries
- Data Management and Algorithms
- Machine Learning in Healthcare
- Constraint Satisfaction and Optimization
- Time Series Analysis and Forecasting
- Logic, Reasoning, and Knowledge
- Health Systems, Economic Evaluations, Quality of Life
- Artificial Intelligence in Healthcare and Education
- Service-Oriented Architecture and Web Services
- Chronic Disease Management Strategies
- Multi-Agent Systems and Negotiation
- COVID-19 diagnosis using AI
- Artificial Intelligence in Healthcare
- AI-based Problem Solving and Planning
- Statistical Methods in Clinical Trials
- Data Quality and Management
- Formal Methods in Verification
- Data Visualization and Analytics
- Topic Modeling
- Cancer Treatment and Pharmacology
Università degli Studi del Piemonte Orientale “Amedeo Avogadro”
2014-2025
Azienda Ospedaliera Nazionale SS. Antonio e Biagio e Cesare Arrigo
2023
Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti
2023
Consorzio di Bioingegneria e Informatica Medica
2022
Piedmont University
2007
Abstract In traditional medical education, learners are mostly trained to diagnose and treat patients through supervised practice. Artificial Intelligence simulation techniques can complement such an educational this paper, we present GLARE-Edu, innovative system in which AI knowledge-based methodologies exploited train “how act” on based the evidence-based best practices provided by clinical practice guidelines. GLARE-Edu is being developed a multi-disciplinary team involving physicians...
In this paper, we present GLARE, a domain-independent prototypical system for acquiring, representing and executing clinical guidelines. GLARE has been built within 7-year project with Azienda Ospedaliera San Giovanni Battista in Turin (one of the largest hospitals Italy) successfully tested on guidelines different domains, including bladder cancer, reflux esophagitis, heart failure. is characterized by adoption advanced Artificial Intelligence (AI) techniques, to support medical decision...
One of the most relevant obstacles to use and dissemination clinical guidelines is gap between generality (as defined, e.g., by physicians’ committees) peculiarities specific context application. In particular, general do not take into account fact that tools needed for laboratory instrumental investigations might be unavailable at a given hospital. Moreover, computer-based guideline managers must also integrated with Hospital Information System (HIS), usually different DBMS are...
Knowledge-based clinical decision making is one of the most challenging activities physicians. Clinical Practice Guidelines are commonly recognized as a useful tool to help physicians in such by encoding indications provided evidence-based medicine. Computer-based approaches can provide facilities put guidelines into practice and support decision-making. Specifically, GLARE (GuideLine Acquisition, Representation Execution) domain-independent prototypical providing advanced Artificial...
Abstract Background This study aimed to explore whether explainable Artificial Intelligence methods can be fruitfully used improve the medical management of patients suffering from complex diseases, and in particular predict death risk hospitalized with SARS-Cov-2 based on admission data. Methods work is an observational ambispective that comprised older than 18 years a positive diagnosis were admitted hospital Azienda Ospedaliera “SS Antonio e Biagio Cesare Arrigo”, Alessandria, Italy...
The cooperative construction of data/knowledge bases has recently had a significant impulse (see, e.g., Wikipedia [1]).In cases in which quality and reliability are crucial, proposals update/insertion/deletion need to be evaluated by experts.To the best our knowledge, no theoretical framework been devised model semantics update proposal/evaluation relational context.Since time is an intrinsic part most domains (as well as process itself), semantic approaches temporal databases (specifically,...
In the hemodialysis domain, we are implementing a case‐based, closed‐loop architecture aimed at configuring temporal abstractions (TA), which will be applied to time series data. The advantage of case‐based approach is one “quickly” obtaining suitable TA parameter configuration, simply by looking most similar already configured case, where cases indexed means contextual information. retrieved together with data, then used as an input processing module, able provide set qualitative states,...