CareSignal AI uses advanced machine learning to analyze continuous vital sign data and predict critical events up to 90 minutes before they occur—saving lives through early intervention.
78% probability within 90 minutes • Recommend evaluation
Current monitoring systems overwhelm clinicians with false alarms while missing genuine emergencies until it's nearly too late.
ICU nurses receive hundreds of alarms per patient daily, with false alarm rates reaching up to 99%. Critical warnings get lost in the noise.
Traditional threshold-based alerts only trigger when a patient is already deteriorating. By then, intervention options are severely limited.
Hundreds of thousands of in-hospital cardiac arrests occur annually—many could be prevented with earlier warning of deterioration.
CareSignal AI transforms reactive monitoring into proactive care by predicting deterioration before it becomes critical.
Our ML models analyze heart rate variability, respiratory patterns, blood pressure trends, and oxygen saturation simultaneously to detect subtle warning signs.
Predict critical events like tachycardia, hypotension, and hypoxia up to 90 minutes before they occur, giving clinicians time to intervene.
Patient-specific risk profiling dramatically reduces false alarms while ensuring genuine emergencies are never missed.
Deploy on existing hospital infrastructure through standard HL7 FHIR interfaces—no hardware replacement required.
Traditional vs. CareSignal Detection
State-of-the-art machine learning architecture designed specifically for real-time healthcare applications.
Efficient long-sequence modeling for continuous vital sign analysis
Pre-trained on MIMIC-IV and eICU datasets for robust performance
Real-time inference without cloud latency for critical decisions
Standard healthcare interoperability for seamless adoption
Our models have been trained and validated on real clinical data, demonstrating clinically meaningful predictive accuracy across multiple vital sign deterioration scenarios.
A founding team combining deep clinical AI research experience with world-class machine learning expertise.
Co-Founder & CEO
Honors Biomedical Engineering student at Georgia Tech with deep experience in medical AI/ML. Conducts research at Emory's Madabhushi Lab developing AI models for cancer detection. Previously interned at Netskope working with the CISO team. Co-founded a nonprofit empowering women in STEM.
Co-Founder & CTO
PhD student at CMU's Machine Learning Department, supported by NSF Graduate Research Fellowship. Georgia Tech CS & BME graduate with institute-wide teaching awards. Won Anthem's "Marketplace of the Future" healthcare challenge. Research experience at Google, AWS, Qualcomm, and Bosch.
From research validation to commercial deployment—our path to transforming patient monitoring.
CareSignal AI established with initial model development on MIMIC-IV dataset achieving promising AUC scores.
Complete retrospective validation study with Emory University Hospital using de-identified patient data.
Submit 510(k) pre-submission meeting request to begin regulatory pathway for Class II medical device clearance.
Begin prospective pilot study at partner hospital to validate real-world performance and clinical workflow integration.
Launch commercial product for hospital emergency departments and step-down units.
Expand to post-discharge home monitoring for high-risk patients with chronic conditions.
Be among the first healthcare providers to experience CareSignal AI. We're partnering with forward-thinking hospitals for our pilot program.