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A recent study by researchers from Weill Cornell Medicine and Rockefeller University suggests that Reinforcement Learning (RL), a branch of artificial intelligence, could assist doctors in creating sequential treatment plans for improved patient outcomes. However, significant enhancements are required before RL can be used in clinical environments.
Reinforcement Learning (RL) refers to a type of machine learning algorithm that makes a series of decisions over time. This method has been pivotal in recent advancements in artificial intelligence, achieving superhuman levels in games like chess and Go. RL can analyze evolving patient conditions, test results, and past responses to treatments to recommend the best next steps in personalized healthcare. It’s especially promising for managing chronic and psychiatric conditions.
This research has been published in the Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), and presented on December 13. It introduces “Episodes of Care” (EpiCare), the first benchmarking system for RL in healthcare.
Dr. Logan Grosenick, an assistant professor of neuroscience in psychiatry who directed the research, commented, “Benchmarks have fueled advancements across various machine learning sectors, such as computer vision, natural language processing, speech recognition, and autonomous vehicles. We anticipate that EpiCare will propel RL developments in the healthcare field.”
RL agents improve their strategies based on feedback, gradually learning to enhance their decision-making skills. Dr. Grosenick cautioned, “However, our research shows that while these approaches show promise, they require an excessive amount of data.”
The research team tested the performance of five advanced online RL models using EpiCare. All models surpassed a standard care benchmark, but this only happened after extensive training on thousands or even tens of thousands of simulated treatment episodes. Since RL methods cannot be directly trained on actual patients, they then explored five typical “off-policy evaluation” (OPE) strategies. These approaches leverage historical data, such as clinical trial information, to avoid the need for online data collection. However, the research revealed that the contemporary OPE techniques consistently failed to yield accurate results for healthcare data.
“Our results suggest that current leading OPE methods cannot reliably predict the performance of RL in long-term healthcare scenarios,” stated Dr. Mason Hargrave, the first author and research fellow at Rockefeller University. As discussions around OPE methods in healthcare have intensified, this discovery underscores the necessity for more precise benchmarking tools like EpiCare to evaluate existing RL practices and provide measurable improvement metrics.
Dr. Grosenick expressed hopes that this research will enhance the reliability of RL evaluations in healthcare and accelerate the creation of more effective RL algorithms and training methods suited for medical applications.
Adapting Convolutional Neural Networks for Graph Data Interpretation
In another presentation at NeurIPS on the same day, Dr. Grosenick discussed his research focused on adapting convolutional neural networks (CNNs), widely recognized for image processing, to handle more generalized graph-structured data like that from brain, gene, or protein networks. The success of CNNs in image recognition during the early 2010s set the foundation for “deep learning” and the current era of neural network-based AI applications. CNNs are employed in a range of fields, including facial recognition, autonomous vehicles, and medical image assessment.
Dr. Grosenick pointed out, “We often want to examine neuroimaging data that resembles graphs—comprising vertices and edges—rather than standard images. However, we realized there wasn’t an adequate solution that accurately mirrors CNNs and deep CNNs in the context of graph-structured data.”
Brain networks are usually structured as graphs, where brain regions (vertices) communicate information to one another along connections (edges) that denote their interaction strength. This structure also applies to gene and protein networks, behavioral data from humans and animals, and even the geometry of chemical compounds like pharmaceuticals. By directly examining these graphs, researchers can model relationships and patterns in both local and far-reaching connections more effectively.
Isaac Osafo Nkansah, a research associate who collaborated with Dr. Grosenick and is the primary author of the paper, contributed to developing the Quantized Graph Convolutional Networks (QuantNets) framework, which generalizes CNNs for graph data. “We are currently utilizing this for modeling EEG (electrical brain activity) in patients. We can create a network of 256 sensors across the scalp measuring neuronal activity—that’s essentially a graph,” Dr. Grosenick explained. “We’re condensing these extensive graphs into more digestible components to gain insight into how brain connectivity changes dynamically during treatment for depression or obsessive-compulsive disorder.”
The researchers see broad potential for QuantNets beyond their current applications. They are also investigating modeling graph-structured pose data to analyze behavior in mouse models and human facial expressions derived through computer vision.
Dr. Grosenick concluded, “While we are still working through the complexities and safety considerations of integrating cutting-edge AI techniques into patient care, each advancement—be it a new benchmarking method or a more accurate model—brings us one step closer to personalized treatment plans that could significantly enhance patient health results.”
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