Data Science Case Study: Traditional Machine Learning

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The Key Takeaway

Minnetronix’s OneBrain case study shows how AI and Traditional Machine Learning techniques can inform medical device requirements and design.

In the case of OneBrain, our robust data science selection and development process resulted in:

  • Added flexibility through identification and early correction of data issues hidden in large swathes of data.

  • Saved cost and timeline by analysis to determine required sampling rate and most important data features for our application.

  • Minimization of future risks by working with domain experts to ensure a meaningful and repeatable output.

Flexible Capabilities for Data Science

Data science is a critical yet often misunderstood aspect of medical device development. And because medical devices have strict regulatory requirements for data, there is little margin for error in effectively incorporating data science into a device development project. Specifically, you must consider two significant barriers: the cost of change and the cost of input data.

To address the cost of change, companies must carefully plan for the future at the outset. From a data science perspective, this means selecting the optimal data science approach for the device based on both current design and future plans in the product roadmap. A typical example is that many companies are developing a first-generation commercial device while also planning for artificial intelligence & machine learning to be integrated into future generations of the device. In these cases, starting on the right data science path early in the development cycle will be vital to ensure you can meet current and future plans, without incurring dreaded cost or schedule blow-ups.

To address the cost of input data, companies must find ways to make more with less. From a data science perspective, this means following a rigorous algorithm development process that incorporates the clinical context, knowledge of clinical applications, hardware design implications, and regulatory strategy to optimize the amount of insight that can be extracted from costly and often scarce input data.

Minnetronix offers full-scale data science capabilities as a part of our core services. We are a single partner that excels at the junction of medical device and data science, meaning that our clients don’t need to manage the algorithm development themselves or seek additional support from an outside data science vendor. When partnering with Minnetronix, our data science processes are built into the core medical device development program and handled by our team of data scientists. Instead of running siloed workstreams that merge at the end, this integrated approach ensures better overall device performance and fewer development headaches.

The best way to demonstrate our data science aptitude is with a case study. This project, called OneBrain, demonstrates the importance of integrating clinical context, medical device knowledge and data science at the outset and reveals that Minnetronix’s data science team can derive significant value for our partners in their medical device development program.

“Medical device designers, in particular, must consider two significant barriers: the cost of change and the cost of input data.”

OneBrain: Predicting Neuroworsening

Algorithms are often built to support medical devices.  However, sometimes the opposite is true. Instead of building an algorithm to support a device, data scientists might create an algorithm with the ultimate intention of incorporating that algorithm into a new medical device or technology. The risk, of course, is that the algorithm you create requires technology or hardware that is impractical or impossible at the intended price point or call point. However, the potential benefits are huge; developing the algorithm ahead of the device de-risks what can often be a very costly development cycle. In this case, by developing the algorithm first, our data scientists and engineers were better able to define and translate algorithm inputs to device requirements, ensuring viable but not extraneous inputs are acquired, ultimately translating it to device requirements that are practical for the use environment.  Through early planning, a company can seize an opportunity to flip the order of operations of normal device development. There is no better example of our capabilities in this regard than OneBrain.

Patients in the Neuro-ICU often decline cognitively. This phenomenon, known as neuroworsening, is complex for physicians to predict. A device that monitors neurological signs and predicts neuroworsening could help physicians provide earlier medical intervention.

Multiple monitoring devices currently collect data that is relevant to neuroworsening in the Neuro-ICU, but do not paint the whole picture on their own; and what’s more, these devices do not interact with one another. In the case of OneBrain, unifying these data sources provided critical insights that the sum of the isolated sources could not.

After the Minnetronix team identified this opportunity, work began toward building an algorithm that would ultimately inform physicians and prevent neuroworsening. But before making the algorithm, Minnetronix had to select the right data science approach.

“Beginning algorithm development ahead of the device de-risks what can often be a very costly development cycle.”

Selecting An Approach

One critical aspect of any medical device algorithm is selecting the data science approach that minimizes the high potential cost of change. Each approach introduces different trade-offs that will occur immediately and in later development stages.

Minnetronix has developed a framework to aid in the selection process and provide an approach to data that minimizes future risk. We generally consider four approaches to data science. Each provides benefits and drawbacks, according to six competing characteristics. To illustrate these characteristics and how they play into the selected approach to data science, we will consider the decisions made during the OneBrain project.

We understand the importance of defining requirements before the design of a medical device. This understanding led us to investigate the predictors of neuroworsening before creating our solution. Defining these requirements up-front will allow for a smoother design and manufacture of a device to predict neuroworsening.

With OneBrain, Minnetronix is designing a device to meet the algorithm’s specifications to predict neuroworsening without providing costly extraneous inputs to add to the device design and risk acquiring approval.

After our team assessed the unique needs for OneBrain, they chose the traditional ML/AI (ML/AI that isn’t deep learning) category as the best approach to meet the project’s needs. This choice ensured that the algorithm could meet performance requirements while providing enough cost and timeline certainty to the project. As a result, we felt comfortable that we could correctly identify the needed inputs for our algorithm.

Assessing the Needs for OneBrain

We chose the proper approach based on six competing variables and tweaked it to fit our desired goal.

AI Data Science Graphic OneBrain Case Study
Pictured above: Minnetronix algorithm selection framework
Input Data Efficiency

The amount of available data informs which data science paths are feasible. In the case of OneBrain, the algorithm needed to aggregate and interpret a large amount of data. Therefore, we prioritized an approach with the capability to leverage large quantities of data.

Explainability

Some algorithms are easier to interpret and translate to clinicians. For the sake of this project, the algorithm only needed to predict neuroworsening rather than provide additional clinical insights.

Computation Speed

Larger data sets require more hardware resources. We assigned hardware utilization resources a medium priority since the cost, but not the feasibility of the device, was likely dependent on it.

Cost and Timeline Certainty

This project was exploratory, so cost and timeline certainty were not a priority for the approach to data. Emphasis on cost and timeline are more common in cases where Minnetronix designs an algorithm to support a medical device.

Performance Ceiling

For this project to be successful, our algorithm needed to signal neuroworsening better than any singular vital sign (HR, spO2, ECG) could currently do. As a result, the capability ceiling requirement for this project was high.

Post-Market Growth

Post-market growth had a medium requirement. Because of the exploratory nature of this project, proving efficacy and potential for the algorithm to improve with more data held more importance.

Following the Minnetronix Process

After we mitigated the high cost of change barrier by selecting the right data science approach, only one barrier remained: the high cost of input data.

A standardized algorithm development process is vital to a device’s success. This importance exists because the cost of input data is high for medical devices. As a result, any partner needs to ensure they make the most out of that scarce data.

Minnetronix focuses on medical device development and manufacturing, meaning we have the relevant knowledge that applies to your data and know how to optimize inputs to minimize device requirements while maximizing performance.

Regardless of which approach to data science is best, every project follows Minnetronix’s trusted seven-step process. This process produces valuable data and viable products for our clients. In this instance there were, in particular, some steps that were most critical for the success of OneBrain.

Partnering with Minnetronix

The OneBrain case study displays Minnetronix’s flexibility and expertise in incorporating data science into medical device development. Not only is our team able to use data to create viable medical technologies, but it is also able to utilize existing data sets to inform new products. 

Few medical device partners can provide multiple approaches to data science in tandem with traditional device development services. This lack of expertise can necessitate working with an outside data expert or interpreting the data yourself. Working with an external data service has many disadvantages, like a lack of integration with your hardware, an absence of medical or regulatory knowledge, and an inability to choose the right approach for your overall business constraints. With Minnetronix, however, our customers can be confident in our ability to meet your needs for data science.

Even though the cost of change and input data is high, Minnetronix was able to facilitate a successful development program with OneBrain. Our expert team accomplished its goals using a robust selection and development process.

In the case of OneBrain, these processes resulted in

  • Added flexibility through identification and early correction of data issues hidden in large swathes of data.

  • Saved cost and timeline by analysis to determine required sampling rate and most important data features for our application.

  • Minimization of future risks by working with domain experts to ensure a meaningful and repeatable output.

With Minnetronix as a partner during development, you can make the most out of your data by utilizing our proven process, knowledge of clincial applications, and data scientists.

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