ClosedLoop’s Enterprise Healthcare Feature Store (EHFS) provides all the features and functionality of traditional feature stores – enabling shareability, reusability, and consistency of features from model training to production and maintenance. Further, it enables data scientists to develop a centralized feature repository with unmatched efficiency through its Healthcare Feature Library. These purpose-built features are designed to improve model explainability and help facilitate adoption of machine learning models among clinicians and other stakeholders. Beyond the library, data scientists can expand their feature store with custom features – with audit and governance support at every stage.
Download the product brief to learn more about ClosedLoop's EHFS and why it matters for AI-enabled healthcare organizations.
Healthcare feature engineering is a massive undertaking. Data scientists already spend 80% of their time on low-level data ‘wrangling’ tasks needed to conduct feature engineering, but healthcare’s complexities bring additional challenges. Data scientists must precisely define complex features related to clinical conditions, adverse events, health behaviors, and episodes of care – the building blocks of which are numerous intricate healthcare terminologies and codesets that regularly change over time. This time-consuming process becomes messy and vulnerable to mistakes as data scientists copy, paste, and share code to avoid duplicating work each time they build, productionize, or refine a model. Feature engineering and maintenance without proper governance can quickly become unmanageable and introduce errors, potentially compromising model accuracy. Such failings are consequential; the integrity of predictions is paramount if stakeholders are to improve health and financial outcomes.
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