Top 10 “Must Haves” for Effective Predictive Modeling

Sasha Gutfraind and Russ Michael Blog

BHI® developed a top 10 list of significant factors that contribute to successful predictive modeling based on our deep experience working with really big data.

  • Answer the “so what” question before you start

    No matter how interesting your model’s output might be, if the analysis isn’t actionable, it won’t get used

  • Use massive databases

    Massive databases ensure that large enough cohorts, with multiple diseases and social factors, are represented

  • Explore available data

    Exploring available data to understand strengths and weaknesses, such as data bias

  • Be open to new methods

    Being willing to learn new data science methods for making predictions that result in innovative solutions

  • Negotiate with other experts

    Negotiating between what’s desirable and what’s possible among data science, clinical, and product experts

  • Secure access to powerful computers

    Providing a computing environment that can quickly and seamlessly process and analyze a large data set

  • Possess patient knowledge

    Having an accurate understanding of what actions are feasible and appropriate for patients

  • Pay attention to detail

    Taking time to sufficiently plan, design, develop, and validate predictive models

  • Use correct metrics

    Using correct metrics to judge predictive performance so that better decisions can be made

  • Choose the right data science platform

    Establishing a data science platform that can easily integrate a finished model into usable software products