87,384 research outputs found
Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies
Objectives: Machine learning (ML) and natural language
processing have great potential to improve effciency and
accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers often conceptualize such an approach
as operating in isolation without much need for human
involvement, yet it remains crucial to harness human-inthe-loop practices when developing and implementing such
techniques as their absence may be catastrophic. We advocate for building ML-based technologies that collaborate
with experts within psychiatry in all stages of implementation and use to increase model performance while simultaneously increasing the practicality, robustness, and
reliability of the process.
Methods: We showcase pitfalls of the traditional ML framework and explain how it can be improved with human-inthe-loop techniques. Specifcally, we applied active learning
strategies to the automatic scoring of a story recall task
and compared the results to a traditional approach.
Results: Human-in-the-loop methodologies supplied a
greater understanding of where the model was least confdent or had knowledge gaps during training. As compared
to the traditional framework, less than half of the training
data were needed to reach a given accuracy.
Conclusions: Human-in-the-loop ML is an approach to
data collection and model creation that harnesses active learning to select the most critical data needed to
increase a model’s accuracy and generalizability more
effciently than classic random sampling would otherwise allow. Such techniques may additionally operate
as safeguards from spurious predictions and can aid in
decreasing disparities that artifcial intelligence systems
otherwise propagate
Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques
Accurate taxi time prediction can be used for more efficient runway scheduling to increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. This paper describes two different approaches to predicting taxi times, which are a data-driven analytical method using machine learning techniques and a fast-time simulation-based approach. These two taxi time prediction methods are applied to realistic flight data at Charlotte Douglas International Airport (CLT) and assessed with actual taxi time data from the human-in-the-loop simulation for CLT airport operations using various performance measurement metrics. Based on the preliminary results, we discuss how the taxi time prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast-time simulation model for implementing it with an airport scheduling algorithm in real-time operational environment
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