In the current health ecosystem, most digital health platforms that are AI-based tend to be “retrospective” and singular-outcome solutions. At Life Singularity we offer a breakthrough predictive health platform that is able to deliver personalized, geospatial monitoring while focusing on the prevention of future health events through prospective virtual care.
Built on over 150 million health records, environmental analyses, and emotional/facial analytic data, our platform is capable of benefiting every sector involved in healthcare. From payers, providers and employers to governmental agencies and life scientists, Life Singularity’s platform is able to save not only costs, but lives.
Life Singularity’s Health-Equity Driven Models for vulnerable or underserved patient populations highlighting novel approaches in Geospatial AI to Homelessness Prevention – Risk Prediction, Prevention and Allocation of New Resources; CVD Cases and Mortality by Race, SDOH and Other Comorbidities; Racial / Ethnic Segregation and Access to Covid 19 Testing—Geospatial Analyses of Covid 19 Testing Sites; Opioid/drug Overdose Mortality in Urban and Rural Areas by SDOH – Flagging At-Risk Communities Early; Obesity Prevention; and Predicting COVID-19 Admissions leveraging Wastewater Epidemiology and Distribution of SARS-COV-2 Virus in every US County.
Synthetic Cohorts are digital non-identical, yet highly similar, synthetic data records that preserve the statistical properties of the original data, that pose new opportunities to advance research
Leveraging Transfer Learning and Federated Learning, Life Singularity’s pre-trained multi-modal predictive health models will be customized on customer synthetic data to identify complex behavioral and physiological digital biomarkers, robust phenotypes, micro-cohorts, novel markers for multi-year scale risk stratification
Life Singularity digital twins / virtual health assistants combine empathy with innovative avatar-based technology, advanced sensing of emotions, behaviors and lifestyles, and Artificial Intelligence (AI), with a strong focus on capturing Social Determinants of Health (SDOH) for predicting “prospective” virtual care for prevention of health events. Our Digital Twins can serve as the “Community Health Worker Extensions” to address SDOH and as someone who the patients can TRUST without being judged.
A population of patients people who are likely to have a heightened vulnerability to severe complications from COVID-19 can be identified leveraging demographics, social determinants of health, prior medical history, procedures, lab data, and vital signs, to determine the risk of hypertension, CHF, diabetes, and other co-morbidities. Vulnerability due to underlying chronic conditions when combined with surgical risk and exposure risk can help us stratify COVID-19 Suspected Positivity risk across patient populations. Assignment of a vulnerability risk score to a patient can then help us redirect PPE and testing supplies towards medium to high risk patients and find other solutions for low risk populations.
COVID-19 self-reported symptoms can provide additional insights into exposure history, recoveries, and re-infections. Monitoring key symptoms of COVID-19 in Asymptomatic/Symptomatic patients with real-time surveillance for viral infections would be key in early detection and understanding of patient risk trajectory.
Quantum annealing is a promising heuristic method to solve combinatorial optimization problems, and efforts to quantify performance on real-world problems provide insights into how this approach may be best used in practice. We investigated the empirical performance of quantum annealing to solve the Supply Chain Scheduling Problem (SCSP) with hard constraints using the D-Wave 2000Q quantum annealing device. SCSP seeks the optimal assignment for a set of physicians, PPE, testing supplies, procedures (e.g. orthopedic or cardiovascular), patient risk, and procedure urgency to daily shifts under an accompanying set of constraints on schedule and personnel. After reducing SCSP to a novel Ising-type Hamiltonian or a QUBO (Quadratic Unconstrained Binary Optimization) model, we evaluated the solution quality obtained from the D-Wave 2000Q against the constraint requirements as well as the diversity of solutions. For the supply chain problem of our focus, our results indicate that quantum annealing recovers satisfying solutions for SCSP and suggests the heuristic method is potentially achievable for practical use. Our Platform was demonstrated at the NVIDIA Inception Presentation at NVIDIA AI GTC20 for COVID-19 Remote Sensing and Early Detection [A21390] https://www.nvidia.com/en-us/gtc/start-ups/
COVID-19 self-reported symptoms can provide additional insights into exposure history, recoveries, and re-infections. Monitoring key symptoms of COVID-19 in Asymptomatic/Symptomatic patients with real-time surveillance for viral infections would be key in early detection and understanding of patient risk trajectory.
contact@lifesingularity.com
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