This technology is a machine learning model that identifies hospitalized patients at risk of developing malnutrition using multi-parameter clinical data.
Malnutrition is common in hospitalized patients, leading to longer hospital stays, increased complications, and higher mortality rates. Current screening methods rely on varying criteria and prioritize different parameters, leading to heterogeneity in the identification and diagnosis of malnutrition. There is a need for a standardized, data-driven approach that enable accurate and efficient malnutrition risk assessment.
This technology is a machine learning model that computes a malnutrition risk-score using demographic data, vital signs, laboratory results, and clinical information to identify hospitalized patients at high risk of malnutrition. The model risk score (output) is updated daily to provide unbiased, real-time, quantitative monitoring of malnutrition, regardless of age, race, or gender. It can be integrated with existing screening workflows and electronic health record systems to support continuous clinical decision-making.
This technology has been validated in clinical settings.
IR CU26281
Licensing Contact: Joan Martinez
