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5 results on '"Alcini, Paolo"'

2. Artificial Intelligence in Healthcare : Directions of Standardization

Author

Hoc Group on Application of AI Technologies, Alcini, Paolo, Baird, Pat, Williams, Peter, Bhattacharyya, SB, Cooper, Todd, de la Cruz, Rich, Kumar, Chandan, Datta, Gora, De Marco, Dorotea Alessandra, Franken, Andreas, Geierhofer, Regina, Goldschmidt, Peter G., Juuso, Ilkka, Klimenko, Herman, Kung, Antonio, Laroche, Frederic, Lewelling, Joe, Meyer, Martin, Morozov, Sergey, Orlova, Anna, Panagiotis, Telonis, Penzel, Thomas, Ritz, Derek, Sander, Gaur, Shin, Soo-Yong, Värri, Alpo, Vladzimerskiy, Anton, Lebedev, Georgy, Lim, Chee-Peng, Chen, Yen-Wei, Vaidya, Ashlesha, Mahorkar, Charu, Jain, Lakhmi C., Tampere University, and BioMediTech

Subjects
Standardization, business.industry, Computer science, media_common.quotation_subject, Equity (finance), Legislation, Health informatics, Field (computer science), Health care, Mainstream, Quality (business), Artificial intelligence, 3111 Biomedicine, business, media_common
Abstract

Artificial intelligence (AI) can have a significant positive impact on health and healthcare. AI can be used to improve the quality, efficiency and equity of health care. However, AI has the potential to have significant negative impacts. Therefore, AI medical applications should be designed and deployed in accordance with established guidelines and legislation. There may be gaps or questions in the current regulatory framework related to the interpretation and application of the existing regulatory framework to healthcare applications that include artificial intelligence solutions. Global standardization maintains a consistent approach and can reduce the burden on stakeholders when it comes to establishing regulatory frameworks, interpreting and complying with regulatory requirements. While AI is far from new, it has only recently become mainstream. This chapter outlines the research of the authors who are members of the Hoc Group on Application of AI Technologies in Health Informatics (ISO AHG2 TC215), which was formed by ISO Technical Committee 215 to define goals and directions for standardization in the field of AI in health care. acceptedVersion

Published
2022

3. Minerva: Metadata for data discoverability and study replicability in observational studies

Author

Gutierrez, Lia, Gini, Rosa, Pajouheshnia, Romin, Hyde, Eleanor, Swertz, Morris A., Sturkenboom, Miriam, Margulis, Andrea, Franzoni, Carla, Arana, Alejandro, Ehrenstein, Vera, Gembert, Karin, Jansen, Ella, Herings, Ron, Thurin, Nicolas H., Locatelli, Igor, Jazbar, Janja, Zerovnik, Spela, Kos, Mitja, Smit, Steven, Lind, Sirje, Metspalu, Andres, Zaccagnino, Silvia, Busto, Maria Paula, Middelkoop, Bas, Barreiro-De Acosta, Manuel, Sanchez-Saez, Francisco, Rodriguez-Bernal, Clara, Gabriel Sanfélix-Gimeno, Poblador-Plou, Beatriz, Carmona-Pirez, Jonas, Gimeno-Miguel, Antonio, Gil, Miguel, Schaefer, Wiebke, Haug, Ulrike, Simou, Stefania, Quinten, Chantal, Cochino, Ana, Alcini, Paolo, Kurz, Xavier, and Perez-Gutthann, Susana

4. Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Lessons Learnt From the MINERVA Project in Europe.

Author

Gini R, Pajouheshnia R, Gutierrez L, Swertz MA, Hyde E, Sturkenboom M, Arana A, Franzoni C, Ehrenstein V, Roberto G, Gil M, Maciá MA, Schäfer W, Haug U, Thurin NH, Lassalle R, Droz-Perroteau C, Zaccagnino S, Busto MP, Middelkoop B, Gembert K, Sanchez-Saez F, Rodriguez-Bernal C, Sanfélix-Gimeno G, Hurtado I, Acosta MB, Poblador-Plou B, Carmona-Pírez J, Gimeno-Miguel A, Prados-Torres A, Schultze A, Jansen E, Herings R, Kuiper J, Locatelli I, Jazbar J, Žerovnik Š, Kos M, Smit S, Lind S, Metspalu A, Simou S, Hedenmalm K, Cochino A, Alcini P, Kurz X, and Perez-Gutthann S

Subjects
Europe, Humans, Reproducibility of Results, Data Mining methods, Pharmacoepidemiology methods, Databases, Factual, Observational Studies as Topic methods, Metadata
Published
2024
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5. Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe.

Author

Pajouheshnia R, Gini R, Gutierrez L, Swertz MA, Hyde E, Sturkenboom M, Arana A, Franzoni C, Ehrenstein V, Roberto G, Gil M, Maciá MA, Schäfer W, Haug U, Thurin NH, Lassalle R, Droz-Perroteau C, Zaccagnino S, Busto MP, Middelkoop B, Gembert K, Sanchez-Saez F, Rodriguez-Bernal C, Sanfélix-Gimeno G, Hurtado I, Acosta MB, Poblador-Plou B, Carmona-Pírez J, Gimeno-Miguel A, Prados-Torres A, Schultze A, Jansen E, Herings R, Kuiper J, Locatelli I, Jazbar J, Žerovnik Š, Kos M, Smit S, Lind S, Metspalu A, Simou S, Hedenmalm K, Cochino A, Alcini P, Kurz X, and Perez-Gutthann S

Subjects
Europe, Humans, Pilot Projects, Reproducibility of Results, Data Collection methods, Data Collection standards, Databases, Factual statistics & numerical data, Software, Pharmacoepidemiology methods, Metadata, Observational Studies as Topic methods
Abstract

Purpose: Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct., Methods: A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe., Results: A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype., Conclusions: A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe., (© 2024 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Published
2024
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