Vermont Patent of the Month – July 2023
Detecting diseases and conditions accurately and quickly is crucial for effective treatment and patient care. Biocogniv Inc., an innovative company in the field of medical diagnostics, has developed a groundbreaking AI-based solution capable of diagnosing patients earlier than ever. By combining machine learning algorithms with routine blood tests, this method provides a powerful tool for early disease detection and risk assessment.
The machine learning model developed by Biocogniv is trained using basic metabolic panels (BMP) and complete blood count with differential (CBC w/diff) data obtained from two groups of training subjects: those diagnosed with the known disease or condition (such as COVID-19 or sepsis) and those without the disease or condition. The model classifies subject data and generates a diagnostic score that predicts the likelihood of an individual having COVID-19 or sepsis. The training process involves iteratively refining the model by excluding features that do not improve its performance.
The system uses routine tests already performed in hospitals to overcome any limitations in commercializing the solution. By inputting the results of BMP and CBC panels into the trained machine learning model, healthcare providers can obtain a risk assessment for the disease or condition in question. The model can also incorporate additional subject data parameters, including vital signs, demographics, medical history, and urine analysis, to improve diagnostic accuracy.
The applications of this method are extensive. It can be used to detect diseases and conditions beyond COVID-19 and sepsis, including pulmonary embolism, diabetic keto-acidosis, pyelonephritis, congestive heart failure, and more. The ability to provide risk scores and diagnostic predictions based on routinely collected patient data has the potential to revolutionize medical care, especially in emergency departments, urgent care facilities, and intensive care units.
By implementing this method, healthcare providers can swiftly identify high-risk patients and initiate appropriate treatment. The diagnostic or risk probability scores generated by the machine learning model can assist in making timely and informed decisions, such as ordering additional tests or starting specific treatments. The model can also be used to monitor the progress of treatment by comparing scores before and after intervention.
Biocogniv’s solution aims to save as many lives as it can. Each year, over 700,000 patients die in US hospitals, and 1 in 20 of those deaths are due to late diagnosis and intervention. By speeding up diagnosis, without compromising accuracy, patients can access treatment earlier and, hopefully, with more effect.
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