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Bio Conscious Tech. is a pioneer in the use of machine learning technology to developed predictive disease progression models to enable care providers to take preventive steps and improve patient care, potentially save lives.


Our first focus has been on applying our technology to identify severe Dysglycemia in patients with Diabetes. Proven in clinical pilots, our technology has benefited physicians and patients by identifying high-risk glycemic events before they occur.

Endobits – Automated Remote Patient Monitoring 

Endobits, is a decision-support system helping caregivers scale and automate their remote patient monitoring programs and keep their patients healthy.


Automated Insulin Dysregulation Detection

Using the Endobits remote patient monitoring system the physician can identify patients with severe dysglycemia and suboptimal glucose control to help adjust treatment accordingly.


Severe Hypo/
Hyper Event Detection

The Endobits Severe Events Model was developed as an additional safeguard layer on top of the Blood Glucose Decision Support model. This model uses advanced Machine Learning to analyze patient data to determine if there will be a severe hypo or severe hyper event in the near future.

For Patients

Next-Generation Patient Care 

Problem: Traditional metabolic models are very uncertain with a limited prediction horizon. 


Solution: Endobits leverages advanced artificial intelligence, and Continuous Glucose Monitoring Technology to anticipate adverse glycemic events and provide Diabetes patients and their caregivers with a significantly longer horizon for reaction.


Nocturnal Hypoglycemia Event Detection (NHE)

Our NHE model leverages the power of neural networks to analyze pre-bedtime data to determine whether a nocturnal hypoglycemia event can occur during sleep. Using this model, Endobits helps the patient avoid the possibility of dangerous lows during sleep.


 Decision Support for Patients

Leveraging advanced Machine Learning, our Endobits companion app (ECA) analyzes patient glucose data and calculates future blood glucose trends. Using this model, the patient can anticipate and avoid abnormal glucose fluctuations.

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