Pharmaceutical, Biotechnology & Medical Device Manufacturers
USING ARTIFICIAL INTELLIGENCE TO BETTER UNDERSTAND PATIENT RISK FOR ALZHEIMER'S
Alzheimer's disease prevalence, incidence and affected patients and families continues to increase.
Historical research suggests patients at risk for Alzheimer's may occur and be known earlier in the patient journey.
Patient level data exists for many years which can be minded using machine learning and artificial intelligence tools. Types of data include electronic medical records, chart data, patient level data, real world data and public available incidence and diagnosis rates. Longitudinal, ten-year plus (over 10 years) data from USA and Japan is explored to understand the Risk Factors and generate a risk score for patients.
What is known?
Hx vascular disease
What is not known?
• Cause is unknown
• When and why symptoms begin
• Why amyloid, tau proteins build up
• What other risk factors?
• Any unique features of patients?
How can Machine Learning and Artificial Intelligence help patients?
• Risk factors/features: Identify features of diagnosed Alzheimer’s and dementia patients with unstructured real world data, electronic medical records and chart data. Determine and validate the model to predict if patients developed the disease.
• Risk score: Apply the risk scoring model to patient level data where data exists or DRG coding is licensed and applied.
• Patients: Enroll patients identified in clinical trials and/or medication treatment programs.