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Building a Healthcare Machine Learning Model with Metaflow

Author(s) Goutham Bilakanti
Country India
Abstract The Metaflow data science platform to create and deploy a machine learning model for medical use cases. The pipeline proposed allows for effective data ingestion, preprocessing, model training, evaluation, and deployment along with scalability, reproducibility, and versioning. Through Metaflow's workflow management features, the system streamlines data-driven decisions in medicine. The model uses advanced machine learning to generate predictive health outcome analytics for patients, risk assessment, and individualized treatment advice. The model optimizes clinical effectiveness through automated computationally intensive analytical functions with high precision and interpretability. The structure supports dynamic adaptation to new clinical data, and as a result, enhanced early disease detection and targeted intervention. By optimizing the ML life cycle, the project also tackles some of the most vital challenges in applying healthcare AI, such as data quality, regulation, and model drift. At the end, this solution adds up to more informed, evidence-based medical decisions, improved patient outcomes, and hospital and clinic resource optimization.
Keywords Meta flow, medical analytics, predictive modeling, machine learning, risk stratification of patients, personalized treatment, automation of workflows, scalability of models, data-driven decisions, AI in health
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 3, Issue 7, July 2022
Published On 2022-07-08
Cite This Building a Healthcare Machine Learning Model with Metaflow - Goutham Bilakanti - IJLRP Volume 3, Issue 7, July 2022. DOI 10.5281/zenodo.15196855
DOI https://doi.org/10.5281/zenodo.15196855
Short DOI https://doi.org/g9d64z
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