Top 10 “Must Haves” for Effective Predictive Modeling

Russ MichaelBlog

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.

  • Answering 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.

  • Using massive databases

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

  • Exploring available data

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

  • Being open to new methods

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

  • Negotiating with other experts

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

  • Securing access to powerful computers

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

  • Possessing patient knowledge

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

  • Paying attention to detail

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

  • Using correct metrics

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

  • Choosing the right data science platform

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