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Using AI to Understand What Causes Diseases

#artificialintelligence

Health care leaders are embracing AI. But by conducting an extensive review of case studies and research literature, we've found that their AI initiatives are predominantly focused on developing algorithms that can predict a problem such as cancer in order to make diagnoses better, faster, and less expensively. Rarely, are their organizations devoting resources to AI efforts aimed at understanding why diseases occur. To intervene as effectively as possible, both kinds of algorithms are crucial. To be clear, we are not downplaying the importance of predictive analytics to help diagnose patients.


Using AI to Understand What Causes Diseases

#artificialintelligence

Health care leaders are embracing AI. But by conducting an extensive review of case studies and research literature, we've found that their AI initiatives are predominantly focused on developing algorithms that can predict a problem such as cancer in order to make diagnoses better, faster, and less expensively. Rarely, are their organizations devoting resources to AI efforts aimed at understanding why diseases occur. To intervene as effectively as possible, both kinds of algorithms are crucial. To be clear, we are not downplaying the importance of predictive analytics to help diagnose patients.


Improving the accuracy of medical diagnosis with causal machine learning

#artificialintelligence

Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms.


GNS Healthcare Names Leslie Hoyt Chief Operating Officer

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WIRE)--GNS Healthcare (GNS), a leading precision medicine company that applies causal machine learning technology to massive and diverse data streams to better match drugs and other health interventions to individual patients, today announced the appointment of Leslie Hoyt as Chief Operating Officer. A veteran healthcare operations leader, Hoyt, who most recently served United Health Group (NYSE:UNH) as Senior Vice President at Optum Health, will help GNS scale its operations and strategically navigate its development during a time of tremendous growth for the company. "Leslie brings an enormous passion for improving the health of individuals and the overall quality of care," said Colin Hill, CEO and Co-founder of GNS. "Her experience will be an essential asset to GNS, as more of our existing customers, as well as new customers, turn to GNS for solutions to complex problems arising from the transition to value-based care. Health plans, providers and biopharmaceutical companies are seeking deeper and more timely knowledge regarding which patients are likely to progress with disease, and which interventions will result in better outcomes for those patients at a lower total cost of care."


Rare Disease Treatments to Be Discovered by Machine Learning and Simulation Platform

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Earlier today, Alexion Pharmaceuticals was granted the licensing rights to operate GNS Healthcare's Reverse Engineering and Forward Simulation (REFS) casual machine learning and simulation platform. Alexion intends to use the platform to accelerate both the research of rare diseases and the development of novel therapies. Earlier this year, it was instrumental in finding a new target for breast cancer. In May, GNS published data in the American Association for Cancer Research's (AACR) journal Cancer Research in which they announced the discovery of novel targets, including TRIB1, which had positive implications for the treatment of triple-negative breast cancer. In June, it was announced that the company would be teaming up with Genentech to improve the capability to clarify disease mechanisms, recognize new targets, and diagnose patient populations more accurately.