Actium Health Produces Breakthrough Machine Learning Models with Prefect

About Actium Health

Driving innovation in healthcare, Actium helps healthcare organizations coordinate every level of their health system by delivering data insights and orchestrating Next Best Actions. Powered by comprehensive analytics, the HealthOS platform provides personalized patient treatment recommendations processed by machine learning algorithms. By providing organizations with Next Best Actions at an organizational and individual level, Actium's HealthOS platform provides an intelligence layer to health systems to enable data-driven patient care and dynamic company activity.

Scaling ML Model Training

To fully deliver on Actium's vision of enabling health systems with a broad catalog of Next Best Actions (NBAs) for patients, the data team needed to take on a massive expansion of the data science work, as the data coverage included everything from cancer screenings to orthopedics to cardiology. Facing significant time pressure to stay ahead of competitors, CTO Joe Schmid and his team ramped up R&D around NBA machine learning models by experimenting on large volumes of healthcare data.

Speed and scalability were paramount, and the team needed lightning fast execution and the ability to rapidly scale out by running hundreds of experiments in parallel. The initial infrastructure led the team to Dask, a parallel computing library that natively scales Python, to unlock scalable data science and meet their specifications.

However Dask doesn’t provide higher-level workflow abstractions, which could enable the team to orchestrate and automate workflow processes. The next challenge was to create data science pipelines as parameterized workflows that covered all aspects of running machine learning experiments.

After a brief proof of concept automating data extraction from internal warehouses, Joe and his team quickly began orchestrating parallelized pipelines with Prefect Core on a 100-node Dask cluster and saw promising early results. By building their cluster with AWS on Kubernetes, they enabled rapid up-and-down scalability and could begin training machine learning models.

Newfound ML Efficiency With Prefect

The early results of running Prefect Core on a 100-node cluster exceeded expectations, accelerating machine learning research output and reducing team bandwidth. The team developed pipelines utilizing Prefect Mapping with Dask Executors for parallel execution, balancing workloads with Task Resource Tagging to delegate resource-intensive tasks to high-memory Dask workers. The newfound efficiency allowed data scientists to iterate various machine learning approaches by easily running hundreds of experiments very quickly and reliably.

The breakthroughs in the R&D phase provided by Prefect Core's workflow semantics naturally lent themselves to Prefect Cloud, a centralized platform for the team to monitor flows and logs on distributed Dask workers. With the added visibility into ongoing processes and the ability to execute workflows in remote environments, Prefect Cloud’s built-in feature set provided essential infrastructure for the team to confidently push to production. Additionally, Cloud's Task Concurrency Limits prevented overwhelming data stores when pipelines needed to store results from hundreds of experiments in a database. The Cloud UI also provided monitoring of active Prefect Agents, whose health is integral to executing workflows on Actium's own Kubernetes infrastructure.

Prefect's Hybrid Execution Model assured the team their data-sensitive development was secure, using a remote Dask environment to run their code on existing infrastructure, empowering rapid experimentation and reducing R&D implementation time.

Expediting the machine learning modeling allowed the team to prioritize their rapid development, eventually leading to the production of many successful models now used in production. The team's breakthroughs were immediately available to patients, developing models recommending various service lines such as orthopedics, oncology, and cardiology.

Driving Results and Business Impact

Faster Implementation

Prefect allowed data scientists to easily run hundreds of experiments very quickly and reliably using Dask parallelism to iterate through various machine learning approaches under time-sensitive conditions.

Healthcare Breakthroughs

Breakthroughs in successful ML modeling produced many models used in production today, recommending patients for various service lines such as orthopedics, oncology, and cardiology. Notably, one of the team’s latest breakthroughs is achieving the highest AUC score in Breast Cancer Detection performance across models, surpassing the Gail Model and Tyrer-Cuzick Model scores.

Maintained Compliance

Sensitive data and code never passes through Prefect Cloud's infrastructure, empowering the team to quickly overcome compliance hurdles and expedite R&D.

Time Saved

Prefect Cloud enhanced visibility and logging into long running flows and distributed tasks is safeguarded by failure alerts via Slack, allowing data scientists to spend their time and energy on more important projects than the constant monitoring of fragile jobs.

The team streamlined imminent ML development with Prefect flows that automate ongoing predictive model updates on a nightly basis and model retraining on a weekly or monthly basis.

Data scientists and engineers continue to develop Prefect flows for data pipeline needs, as it allows them to focus on projects at hand, rather than error handling, retry logic, logging, etc.

Ongoing Efforts

Ease of use allows Actium data scientists to expand usage of Prefect beyond ML to production-grade data engineering, standardizing future implementations of data pipelines.

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