Predicting Decision-Making Time for Diagnosis over NGS Cycles: An Interpretable Machine Learning Approach

Motivation Genome sequencing processes are commonly followed by computational analysis in medical diagnosis. The analyses are generally performed once the sequencing process has finished. However, in time-critical applications, it is crucial to start diagnosis once sufficient evidence has been accumulated. This research aims to define a proof-of-principle for predicting earlier time for decision-making using a machine learning approach. The method is evaluated on Illumina sequencing cycles for pathogen diagnosis. Results We utilized a Long-Short Term Memory (LSTM) approach to make predictions for the early decision-making time in time-critical clinical applications. We modeled the (meta-)information obtained from NGS intermediate cycles to investigate whether there are any changes to expect in the remaining sequencing cycles. We tested our model on different patient datasets, resulting in high accuracy of over 98%, indicating the model is independent of a dataset. Furthermore, we can save several hours of turnaround time by using the early prediction results. We used the SHapley Additive exPlanations (SHAP) framework for the interpretation and assessment of the LSTM classifier. Availability The source code is available at https://gitlab.com/dacs-hpi/ngs-biclass. Contact Bernhard.Renard@hpi.de