Reliable ML for Clinical Prediction
DOI:
https://doi.org/10.5281/zenodo.22172924Keywords:
Predictive Analytics in Healthcare, Clinical Prediction Models, Resource Allocation Optimization, Workflow Optimization, Risk Stratification, Treatment Response Prediction, Healthcare Machine Learning, Production-Grade ML Pipelines, End-to-End Analytics Frameworks, Clinical Decision Support Systems, Healthcare MLOps, Model Deployment in Healthcare, Continuous Healthcare ML Operations, Experimentation Pipelines, Translational Analytics, Operationalizing ML in Healthcare, Data-to-Decision Pipelines, Scalable Healthcare AI, Reliable Clinical ML Systems, Analytics-Driven Care Delivery.Abstract
Predictive analytics leverages past occurrences to answer questions about future events, thereby enabling healthcare organizations to identify exceptional cases and optimize strategies and actions. Healthcare predictive analytics applies predictive analytics to clinical prediction, resource allocation, workflow optimization, risk stratification, treatment response prediction, and other tasks with different objectives and stakeholder perspectives. Successful solutions can be instrumental in improving healthcare outcomes, increasing operational efficiency, and achieving better return on investment, thereby stimulating interest among analysts and clinicians. However, the innovation gap in applying machine learning to healthcare is primarily associated with production-grade models rather than algorithmic novelty. Machine learning is maturing into a viable technology for many forms of prediction, but the surround systems and processes needed for real-world adoption remain largely unsolved. Production-grade pipelines fill this need by providing the end-to-end framework connecting clinicians’ predictive ideas to analytically-driven changes in healthcare delivery. They support experimentation by enabling analysts or researchers to quickly build and test pipelines on small-scale data using diverse models without requiring detailed technical expertise. For the clinical effort to yield substantial benefits, however, the pipelines must be production-grade, thereby allowing a solution to work reliably and continuously once analysts uncover an interesting prediction application.
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