首页|VU Amsterdam Reports Findings in Hemophilia A (A Generative and Causal Pharmacokinetic Model for Factor Ⅷ in Hemophilia A: A Machine Learning Framework for Continuous Model Refinement)
VU Amsterdam Reports Findings in Hemophilia A (A Generative and Causal Pharmacokinetic Model for Factor Ⅷ in Hemophilia A: A Machine Learning Framework for Continuous Model Refinement)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Hematologic Diseases and Conditions - Hemophilia A is the subject of a report. According to news reporting originating in Amsterdam, Netherlands, by NewsRx journalists, research stated, “In rare diseases, such as hemophilia A, the development of accurate population pharmacokinetic (PK) models is often hindered by the limited availability of data. Most PK models are specific to a single recombinant factor Ⅷ (rFVIII) concentrate or measurement assay, and are generally unsuited for answering counterfactual (‘what-if’) queries.” The news reporters obtained a quote from the research from VU Amsterdam, “Ideally, data from multiple hemophilia treatment centers are combined but this is generally difficult as patient data are kept private. In this work, we utilize causal inference techniques to produce a hybrid machine learning (ML) PK model that corrects for differences between rFVIII concentrates and measurement assays. Next, we augment this model with a generative model that can simulate realistic virtual patients as well as impute missing data. This model can be shared instead of actual patient data, resolving privacy issues. The hybrid ML-PK model was trained on chromogenic assay data of lonoctocog alfa and predictive performance was then evaluated on an external data set of patients who received octocog alfa with FVIII levels measured using the one-stage assay. The model presented higher accuracy compared with three previous PK models developed on data similar to the external data set (root mean squared error = 14.6 IU/dL vs. mean of 17.7 IU/dL). Finally, we show that the generative model can be used to accurately impute missing data (<18% error).”
AmsterdamNetherlandsEuropeBlood Coagulation FactorsCyborgsEmerging TechnologiesFactor VIIIHealth and MedicineHematologic Diseases and ConditionsHematologyHemic and Lymphatic Diseases and ConditionsHemophiliaHemophilia AInherited Blood Coagulation DisordersMachine LearningPharmacokineticsPharmacology.
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NSTL
Wiley
