Nanowear, a New York-based connected care and companion diagnostic platform built on FDA-cleared nanotechnology, is delivering a new standard for remote patient monitoring. Our cloth-based nanosensors capture and transmit 15+ medical grade bio-markers, enabling our machine-learning algorithms to alert care providers of worsening patient status. As we began our approach to the development of these algorithms, we were faced with two challenges: First, doing trials with hundreds of thousands of patients is expensive and very time consuming. Second, we needed to develop a solution that could get FDA clearance, which means that it had to be easy to understand and verify. In this article, we’ll show how we met those challenges.
Perhaps all of machine learning (ML) seems like flashy new technology, but it actually represents a spectrum of approaches spanning orders of magnitude in complexity. On the simpler side, we have approaches like linear regression over a few engineered inputs that could contain just a handful of learned parameters. On the other side, approaches like Neural Networks (NNs) are able to learn how to construct their own features, and could leverage millions of parameters. Both are powerful tools meant for different applications of data and classes of datasets. In deciding which approach is right for your product, there are several questions to ask yourself including: Is your dataset rich enough for a model to learn its own features? Or can you forgo explainability for the model’s output? If you answered no to either, then using a simpler model (and thus explainable) on engineered features is probably the way to go. Also it’s always okay to start with something simpler first, or mix the two.
In healthcare, NNs are often used for discovering insights from large pre-existing datasets from hospital systems, such as EMRs or imagery. Simpler algorithms based on pre-engineered features are often used in concert with medical devices, as a way to leverage decades of existing medical research, and in turn require less training data.