Indica News Bureau-
Machine learning tools could identify more patients with undiagnosed rare diseases years earlier than current methods, potentially improving outcomes and reducing costs and morbidity, says study They say: The findings of this study, led by researchers at UCLA Health, are described below. scientific translational medicine.
“Patients with rare diseases may face long delays in diagnosis and treatment, which can result in unnecessary testing, disease progression, psychological stress, and financial burden.” said Manish Butte, M.D., professor of pediatrics and human genetics at UCLA. , and microbiology/immunology who care for these patients at the UCLA clinic.
“Machine learning and other artificial intelligence techniques are making inroads into healthcare. Using these tools, we can identify patterns in electronic medical records that are similar to patterns of patients known to have this disease. We developed an approach to speed the diagnosis of undiagnosed patients by identifying patterns in
The study focused on diseases collectively known as common variable immunodeficiency diseases (CVID). This disease often goes undiagnosed for years or decades after symptoms appear because the disease is rare, symptoms vary widely from person to person, and symptoms tend to overlap with other diseases. Common disorders.
Butte and Bogdan Pasaniuk, Ph.D., professor of computational medicine, human genetics, pathology, and laboratory medicine at the UCLA David Geffen School of Medicine, are part of the team that developed a machine learning tool called PheNet, borrowing from the term “phenotype.” led. ”, an observable characteristic or characteristic of a disease in an individual. PheNet learns the phenotypic patterns from her verified CVID cases and uses this knowledge to rank patients by their likelihood of having CVID.
Because CVID does not have a single clinical symptom, identifying the EHR “signature” of this disease is a complex task. To get around this, researchers created a computational algorithm that can infer EHR characteristics from the health records of known CVID patients and disease patterns described in the medical literature. The system calculates a numerical score for each patient and prioritizes those with the highest likelihood of CVID. These high-scoring patients are what the researchers describe as “hidden in the health care system” and are recommended for referral to an immunology specialist. When the UCLA team applied his PheNet to his extensive UCLA electronic medical records database and conducted a blinded review of the top 100 patients identified by the system, they found that 74% of these patients It turned out that he was likely suffering from CVID.
“We have shown that artificial intelligence algorithms such as PheNet can provide clinical benefit by speeding the diagnosis of CVID, and we hope this will be applied to other rare diseases as well. ,” Pasaniuk said. “Implementation at all five UC medical centers is already having an impact. We are now expanding to other diseases while improving the accuracy of our approach to better identify CVID.” We also plan to teach the system to read medical notes to gather more information about patients and their illnesses.”
