https://www.selleckchem.com/ This opens a path for healthcare practitioners to personalize GH therapy at any stage of the patients' journey and improve shared decision making with patients and caregivers to achieve optimal outcomes.We collected user needs to define a process for setting up Federated Learning in a network of hospitals. We identified seven steps consortium definition, architecture implementation, clinical study definition, data collection, initialization, model training and results sharing. This process adapts certain steps from the classical centralized multicenter framework and brings new opportunities for interaction thanks to the architecture of the Federated Learning algorithms. It is open for completion to cover a variety of scenarios.The development of precision medicine in oncology to define profiles of patients who could benefit from specific and relevant anti-cancer therapies is essential. An increasing number of specific eligibility criteria are necessary to be eligible to targeted therapies. This study aimed to develop an automated algorithm based on natural language processing to detect patients and tumor characteristics to reduce the time-consuming prescreening for trial inclusions. Hence, 640 anonymized multidisciplinary team meeting (MTM) reports concerning lung cancer were extracted from one teaching hospital data warehouse in France and annotated. To automate the extraction of 52 bioclinical information corresponding to 8 major eligibility criteria, regular expressions were implemented and evaluated. The performance parameters were satisfying macroaverage F1-score 93%; rates reached 98% for precision and 92% for recall. In MTM, fill rates variabilities among patients and tumors information remained important (from 31.4% to 100%). The least reported characteristics and the most difficult to automatically collect were genetic mutations and rearrangement test results.Unstructured medical text labeling technologies are expected to be hig