  The response time of a biosensor is a crucial metric in safety-critical
applications such as medical diagnostics where an earlier diagnosis can
markedly improve patient outcomes. However, the speed at which a biosensor
reaches a final equilibrium state can be limited by poor mass transport and
long molecular diffusion times that increase the time it takes target molecules
to reach the active sensing region of a biosensor. While optimization of system
and sensor design can promote molecules reaching the sensing element faster, a
simpler and complementary approach for response time reduction that is widely
applicable across all sensor platforms is to use time-series forecasting to
predict the ultimate steady-state sensor response. In this work, we show that
ensembles of long short-term memory (LSTM) networks can accurately predict
equilibrium biosensor response from a small quantity of initial time-dependent
biosensor measurements, allowing for significant reduction in response time by
a mean and median factor of improvement of 18.6 and 5.1, respectively. The
ensemble of models also provides simultaneous estimation of uncertainty, which
is vital to provide confidence in the predictions and subsequent safety-related
decisions that are made. This approach is demonstrated on real-time
experimental data collected by exposing porous silicon biosensors to buffered
protein solutions using a multi-channel fluidic cell that enables the automated
measurement of 100 porous silicon biosensors in parallel. The dramatic
improvement in sensor response time achieved using LSTM network ensembles and
associated uncertainty quantification opens the door to trustworthy and faster
responding biosensors, enabling more rapid medical diagnostics for improved
patient outcomes and healthcare access, as well as quicker identification of
toxins in food and the environment.
