Fully automated solution improves diagnostic accuracy: Study
Combining the diagnoses of numerous diagnosticians into a collective solution is a strong technique for improving diagnostic accuracy.
Representative image.
CALIFORNIA: Every year, an estimated 250,000 people in the United States die as a result of preventable medical errors. Many of these mistakes occur throughout the diagnostic process.
Combining the diagnoses of numerous diagnosticians into a collective solution is a strong technique for improving diagnostic accuracy. However, in general, in medical diagnostics, there has been a scarcity of strategies for aggregating independent diagnoses.
As a result, researchers from the Max Planck Institute for Human Development, the Institute for Cognitive Sciences and Technologies (ISTC), and the Norwegian University of Science and Technology developed a fully automated solution based on knowledge engineering methodologies.
The researchers put their method to the test on 1,333 medical cases from The Human Diagnosis Project (Human Dx), each of which was independently diagnosed by ten diagnosticians.
"Our results show the life-saving potential of tapping into the collective intelligence," said first author Ralf Kurvers.
He is a senior research scientist at the Center for Adaptive Rationality of the Max Planck Institute for Human Development and his research focuses on social and collective decision-making in humans and animals.
Collective intelligence has been proven to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and diagnostics in radiology and dermatology (e.g., Kurvers et al., PNAS, 2016). However, collective intelligence has been mostly applied to relatively simple decision tasks.
Applications in more open-ended tasks, such as emergency management or general medical diagnostics, are largely lacking due to the challenge of integrating unstandardized inputs from different people.
To overcome this hurdle, the researchers used semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology, a comprehensive multilingual clinical terminology, for standardization.
"A key contribution of our work is that, while the human-provided diagnoses maintain their primacy, our aggregation and evaluation procedures are fully automated, avoiding possible biases in the generation of the final diagnosis and allowing the process to be more time- and cost-efficient," added co-author Vito Trianni from the Institute for Cognitive Sciences and Technologies (ISTC) in Rome.
