
Smart Blood Analytics Swiss (SBAS) uses advanced machine learning algorithms to provide different AI models for predicting the most probable diagnoses, based solely on an individual’s blood test results.
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Blood tests play a crucial role in the detection, diagnosis and treatment of disease. As health care reform focuses on diagnosis and early intervention, the timely and accurate interpretation of blood test results is essential for the quality of future patient care and for managing downstream costs.
The interpretation of blood test results generally becomes increasingly difficult when multiple parameter values are out of the normal range or even when all the values are within the normal range.
Complete blood count with differential and common parameters from biochemistry, hemostatic, enzyme, lipid, protein & amino acids, vitamins and hormone panels are usually sufficient for our models to predict the right disease, confirm a human made diagnosis or expand the differential diagnosis.
Using machine learning algorithms we have built revolutionary models to predict hundreds of groups of diseases and medical conditions in the field of internal medicine. Our models can recognize disease-related blood laboratory patterns that are beyond medical knowledge, resulting in a higher diagnostic accuracy compared to traditional quantitative interpretations based on reference ranges or physician’s decision-based reference intervals.
Smart Blood Analytics report includes a list of most likely diseases and includes an intuitive multilevel pie chart, which helps physicians to speed up and improve diagnostics, or help patients to be better informed about the outcome of their blood tests and therefore increase their participation during the treatment decision-making process.
Our company is small, flexible and highly specialised in interpreting blood test results. Our MISSION is to EMPOWER the interpretation of blood test results in an unprecedented way. We strongly believe that artificial intelligence can bring enormous benefits to future medical research.
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