RESUMO
Healthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labelled data and possibly lacking interpretability. We present Clais, a collaborative artificial intelligence system for claims analysis. Clais automatically extracts human-interpretable rules from healthcare policy documents (0.72 F1-score), and it enables professionals to edit and validate the extracted rules through an intuitive user interface. Clais executes the rules on claim records to identify non-compliance: on this task Clais significantly outperforms two baseline machine learning models, and its median F1-score is 1.0 (IQR = 0.83 to 1.0) when executing the extracted rules, and 1.0 (IQR = 1.0 to 1.0) when executing the same rules after human curation. Professionals confirm through a user study the usefulness of Clais in making their workflow simpler and more effective.
Assuntos
Inteligência Artificial , Humanos , Fraude , Aprendizado de Máquina , Atenção à Saúde , Revisão da Utilização de SegurosRESUMO
Microsatellite-unstable (MSI) cancers require WRN helicase to resolve replication stress due to expanded DNA (TA)n dinucleotide repeats. WRN is a promising synthetic lethal target for MSI tumors, and WRN inhibitors are in development. In this study, we used CRISPR-Cas9 base editing to map WRN residues critical for MSI cells, validating the helicase domain as the primary drug target. Fragment-based screening led to the development of potent and highly selective WRN helicase covalent inhibitors. These compounds selectively suppressed MSI model growth in vitro and in vivo by mimicking WRN loss, inducing DNA double-strand breaks at expanded TA repeats and DNA damage. Assessment of biomarkers in preclinical models linked TA-repeat expansions and mismatch repair alterations to compound activity. Efficacy was confirmed in immunotherapy-resistant organoids and patient-derived xenograft models. The discovery of potent, selective covalent WRN inhibitors provides proof of concept for synthetic lethal targeting of WRN in MSI cancer and tools to dissect WRN biology. Significance: We report the discovery and characterization of potent, selective WRN helicase inhibitors for MSI cancer treatment, with biomarker analysis and evaluation of efficacy in vivo and in immunotherapy-refractory preclinical models. These findings pave the way to translate WRN inhibition into MSI cancer therapies and provide tools to investigate WRN biology. See related commentary by Wainberg, p. 1369.