Saigeware is developing a predictive analytics platform that utilizes a large, multi-marker deep phenotype dataset and machine learning models to identify high-risk stroke patients after hospitalization. The platform aims to enhance recovery outcomes by enabling remote patient monitoring and clinical decision support, thereby reducing readmissions and improving overall patient care.
Funding
$250K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Post-stroke patients face a high risk of complications and readmission after hospital discharge. Current methods for monitoring recovery and identifying at-risk individuals often lack the granularity and predictive power needed for timely intervention. This can lead to delayed treatment, increased healthcare costs, and poorer patient outcomes.
Solution
Saigeware offers a predictive analytics platform designed to identify high-risk stroke patients after hospitalization. The platform leverages a large, multi-marker, deep phenotype dataset and machine learning models to provide personalized healthcare insights. By continuously monitoring patients remotely and offering clinical decision support, Saigeware aims to reduce readmissions and improve overall recovery outcomes. The system analyzes phenotypic markers collected non-invasively via smartphones, wearables, and simple devices to stratify patient risk and enable proactive care management.
Target Audience
The primary target audience includes enterprise-level health systems, clinical trial organizations, and clinicians offering wellness programs for post-stroke patients.
Features
- Multi-marker deep phenotype dataset correlated with disease for high clinical value
- Machine learning models trained using deep phenotyping and data analysis
- Risk stratification based on a subset of phenotypic markers obtained non-invasively
- Remote patient monitoring via smartphones, wearables, and simple devices
- Clinical decision support tools to enable personalized healthcare
- Identification of at-risk populations to reduce readmissions