Marazzi Fabio, Tagliaferri Luca, Masiello Valeria, Moschella Francesca, Colloca Giuseppe Ferdinando, Corvari Barbara, Sanchez Alejandro Martin, Capocchiano Nikola Dino, Pastorino Roberta, Iacomini Chiara, Lenkowicz Jacopo, Masciocchi Carlotta, Patarnello Stefano, Franceschini Gianluca, Gambacorta Maria Antonietta, Masetti Riccardo, Valentini Vincenzo
Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, UOC di Radioterapia Oncologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00186 Rome, Italy.
Dipartimento di Scienze della Salute della Donna e del Bambino e di Sanità Pubblica, UOC di Chirurgia Senologica, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00186 Roma, Italy.
J Pers Med. 2021 Jan 22;11(2):65. doi: 10.3390/jpm11020065.
Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer.
A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs).
Data were classified into "Not organized, not 'ontologized' data", "Organized, not 'ontologized' data", and "Organized and 'ontologized' data". Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available.
GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system.
人工智能（AI）在日常生活中的流程管理中应用日益广泛。在医学领域，AI正成为计算机化系统的一部分，用于管理信息并促进证据生成。在此，我们展示了AI在医院现有IT系统中的应用开发，以创建一个用于管理乳腺癌领域临床和研究流程的数据集市。
一个由放射肿瘤学家、流行病学家、医学肿瘤学家、乳腺外科医生、数据科学家和数据管理专家组成的多学科团队共同努力，识别医院系统内的相关数据和来源。使用开源数据科学软件包和行业解决方案的组合来设计目标框架。为了在实际案例中直接验证数据集市，工作团队定义了肿瘤病理学和概念验证（PoC）的临床目的。
数据被分类为“未整理、未‘本体化’的数据”、“已整理、未‘本体化’的数据”和“已整理且‘本体化’的数据”。识别出的真实世界数据（RWD）档案基于本体、医院数据仓库、PDF文档和电子报告的平台。通过与结构化数据直接连接或文本挖掘技术进行数据提取。进行了两个概念验证，通过它们测试了放疗等待时间间隔和乳腺科的绩效指标，并得出了可用结果。
创建了GENERATOR乳腺数据集市以支持乳腺癌护理路径。基于AI的流程会自动从不同来源提取数据，并将其用于生成趋势研究和临床证据。需要进一步的研究和更多的概念验证来挖掘该系统的所有潜力。
