inCognita Data Science components

Volv has additional capabilities that make up the inCognita methodology, which enrich the insights and learnings that the core predictive modelling produces.

For example, we have created a new method for leveraging passively-collected smartphone data, and performing machine learning on this to detect and monitor brain disorders such as depression and Parkinson’s disease.

inCognita AI & Data Science

Volv’s approach is unique and provides state of the art performance, defining disease state more accurately than any other current known method.

The ubiquity of smartphones in modern life suggests the possibility to use them to continuously monitor patients, for instance to detect undiagnosed diseases or track treatment progress.

Such data collection and analysis may be especially beneficial to patients with

  1. mental disorders, as these individuals can experience intermittent symptoms and impaired decision-making, which may impede diagnosis and care-seeking, and
  2. progressive neurological diseases, as real-time monitoring could facilitate earlier diagnosis and more effective treatment.

Volv has developed a new method of leveraging passively-collected smartphone data and using machine learning to detect and monitor brain disorders such as depression and Parkinson’s disease. Crucially, the algorithm is able learn accurate, interpretable models from small numbers of labelled examples, for example from smartphone users for whom sensor data has been gathered and disease status has been determined. Predictive modelling is achieved by learning from augmented training sets comprising both real and ‘synthetic’ patients. This approach is shown to outperform state-of-the-art techniques in experiments involving disparate brain disorders and multiple patient datasets.

The Volv approach is shown to outperform state-of-the-art techniques in experiments involving disparate brain disorders and multiple patient datasets.

Volv is exploring the applicability of the approach to other disorders.

Volv I+FLL Algorithms

Learning from Synthetic Patients

In some applications, the labeled training data is extremely limited and/or class-imbalanced, and this is an obstacle to effective learning. This situation arises, for example, because it is often difficult to collect data traces for individuals with confirmed diagnoses for the Target Disease (TD) of interest, and because typical datasets have far fewer ‘cases’ (patients with the TD) than ‘controls’ (unaffected patients). We therefore use synthetic patients to address the issue of class imbalance.

inCognita - continuous monitoring of mental disorders and progressive neurological diseases

inCognita helps detect signals related to mental disorders and progressive neurological diseases. inCognita brings real-time monitoring that can facilitate earlier diagnosis and more effective treatments. inCognita is a unique data science framework that Volv have developed.

Find out more about inCognita
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value
Please enter a value