Welcome to CognitiveCare

Using Al and Math to detect diseases early

Our Purpose

Spot and detect diseases and their symptoms early.

Our Work

Work at the intersection of AI, Medicine and Biology to deliver precise, actionable insights on early signs of diseases.

<

How Are We Different

We model and assess every patient uniquely.

First, we go beyond Pharmacogenomics (stratified medicine) that determines the current stratification of patients and govern their treatments.

Second, we bring two most important dimensions – deeper understanding of the patient and the drug-disease behaviors to further understand every patient in complete detail and then determine the optimal medical intervention for that patient.

Third, our insights are delivered using precise individual scores (represented by survival and adverse events) with a detailed explanation of how individual dimensions contributed towards the score.

Fourth, we learn. Our algorithms continuously take into account the real-time feed-back from caregivers and improvise the scores on a regular basis.

Finally, by leveraging cutting-edge capabilities of AI and computing, we analyze any bit of relevant data (example: genetics, images, doctor notes, audio and videos, social media, healthcare records, pathology etc.) for unparalleled insights about diseases, their networks and the impact of interventions.

Our Focus Areas

Advanced Math, Statistics and Artificial Intelligence models that can analyze almost every bit of healthcare data and generate precise quantitative scores for patients.

Maternal, Infant and Fetal Health

MIHIC (Maternal Infant Health Insights and Cognitive Intelligence) Scores representing the propensity of 48 individual risks in mothers, fetus and infants during pregnancies. MIHIC scores will help identify not just the probability of any of these risks occurring in the mother/infant/fetus but helps arrive at the most optimal intervention by understanding which pregnancies are of high risk and which are not.

Neuropsychiatry

NPHIC (Neuro Psychiatric Health Insights and Cognitive Intelligence) Scores representing the propensity of suicide, addiction, depression, anxiety and substance abuse in the citizens. By detecting these behaviors, we help identify the patients with the highest risk of succumbing to these conditions and find ways to intervene and prevent them.

Pathology Disorders

PADIC (Pathology Disorders Insights and Cognitive Intelligence) scores representing more than 800 conditions of possible pathology disorders in the human anatomy. By mining massive volumes of pathology data, our scoring engines can generate propensity for risks (such as NASH, Renal Failure, Heart Disease, Arthritis, Acute mineral and vitamin deficiency etc.) using biomarkers and social determinants.

Our Platform

Central Idea

It is the Risk Scoring Engine that identifies and scores (quantifies) the propensity of a certain risk (of symptoms, outcome, disease) in a human being by analyzing clinical (biomarkers, images, drug usage, doctor notes/audios/videos); social (income, demographics, profession); lifestyle (habits, addictions etc.) and genetic determinants. The scores can thus offer specific, precise and patient specific insights at an individual level and thus empowering the doctors to analyze and intervene further.

Data: Complete view of the patient - clinical, social, lifestyle and genetic data

Outputs: RIsk Scores for every possible symptom/outcome/disease.

Solutions

Integrated medical and computational research is central to our existence

A representative sample of our integrated research uncovered the following hidden patterns/networks:
  • Hidden patterns of NASH twice than the conventional detection methods
  • Uncovering the etiology of renal failure
  • Determining the optimal treatment pathway with raised ALKP
  • Probabilistic conversion from acute to chronic renal failures
  • Survival Scores for Multiple myeloma including outcomes and adverse events
  • Peri-natal and pre-natal maternity risk detection