Collaborative and privacy-preserving workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions

Objective To develop and validate advanced natural language processing pipelines that detect 18 conditions in clinical notes written in French, among which 16 comorbidities of the Charlson index, while exploring a collaborative and privacy-preserving workflow. Materials and methods The detection pipelines relied both on rule-based and machine learning algorithms for named entity recognition and entity qualification, respectively. We used a large language model pre-trained on millions of clinical notes along with clinical notes annotated in the context of three cohort studies related to oncology, cardiology and rheumatology, respectively. The overall workflow was conceived to foster collaboration between studies while complying to the privacy constraints of the data warehouse. We estimated the added values of both the advanced technologies and the collaborative setting. Results The 18 pipelines reached macro-averaged F1-score positive predictive value, sensitivity and specificity of 95.7 (95%CI 94.5 - 96.3), 95.4 (95%CI 94.0 - 96.3), 96.0 (95%CI 94.0 - 96.7) and 99.2 (95%CI 99.0 - 99.4), respectively. F1-scores were superior to those observed using either alternative technologies or non-collaborative settings. The models were shared through a secured registry. Conclusions We demonstrated that a community of investigators working on a common clinical data warehouse could efficiently and securely collaborate to develop, validate and use sensitive artificial intelligence models. In particular, we provided efficient and robust natural language processing pipelines that detect conditions mentioned in clinical notes. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study has been supported by grants from the AP-HP Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB 00011591 of Assistance Publique - Hopitaux de Paris (AP-HP) gave ethical approval for this work (decisions CSE18-32, CSE20-55 and CSE20-93) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Models and pipelines codes are available online at