News and Noteworthy


Altimate’s CEO Participates in a Discussion on the Role of AI in Treating Chronic Disease

Our CEO Paul Pyzowski will be speaking at Life Science Nation’s new conference 4D Meets AI: Advancing Drugs, Devices, Diagnostics, and Digital Health on September 17, 2020.

Artificial intelligence (AI) and sensor technology used in implantable and wearable devices and managed care solutions use smart, natural language-based agents to help clinicians and patients with chronic disease management. New technologies enabling data generation by the patient will increase the data volume, which will improve timely information and education for patients and doctors in personalizing therapy. Algorithmic support and insights based on data gathered by sensors and machine learning can give individuals more day-to-day control of their disease and remove the guesswork from daily disease management.

Learn more about his session “AI HELPING MANAGE CHRONIC DISEASES” HERE.


Altimate Publishes on Predictive Analytics for Opioid Use Disorder

Monday, August 17, 2020

Our new paper on predictive analytics in opioid use disorders is available! We have developed a method to predict relapse in a time window when a clinical intervention can be made. As far as we know this is the first quantitative method using only clinical data to predict imminent, patient-specific relapse, during an outpatient visit and when a clinical intervention can be made.

The full paper can be accessed HERE.

Machine Learning Applied to Clinical Laboratory Data Predicts Patient-Specific, Near-Term Relapse in Patients in Medication for Opioid Use Disorder Treatment

We have developed a data-driven, algorithmic method for identifying patients in an out- patient buprenorphine program at high risk for relapse in the following seven days. This method uses data already available in clinical laboratory data, can be made available in a timely matter, and is easily understandable and actionable by clinicians. Use of this method could significantly reduce the rate of relapse in addiction treatment programs by targeting interventions at those patients most at risk for near term relapse.

doi: https://doi.org/10.1101/2020.08.10.20163881