Innovative artificial intelligence-based system for supporting the treatment of affective disorders

Challenges

There are approximately 280 million people living with depression worldwide and 40 million people struggling with bipolar affective disorder. Approximately one third of patients suffering from severe mental illness require repeated hospitalisation. There is currently no tool on the market to effectively monitor the course of the illness – the patient's condition is assessed on the basis of observations of the patient's behaviour, and the ongoing monitoring of the patient's health is the responsibility of the patients themselves and their immediate family. Such monitoring is subjective, inaccurate, and unreliable.

MoodMon Innovative artificial intelligence-based system

A major problem is also the shortage of mental health professionals, which makes it difficult for the patient to have regular contact with a doctor – in Europe, on average, there are only 18 psychiatrists per 100 000 inhabitants, with the result that a patient can count on visits on average once every three months. Medical appointments scheduled in advance do not always take place when the patient actually needs them and the treatment provided is tailored to the patient's current condition.

The low effectiveness of treatment due to the shortage of specialists and the difficulty in monitoring the patient increase the cases of hospitalisation of patients and raise the cost of treatment. For people with depression, this ranges from EUR 3,000 to EUR 5,000 per patient, while the average annual cost of treating bipolar affective disorder is around EUR 7,000. Bipolar affective disorder cost the German health service around EUR 607 million in 2015 alone.

The main objective of the project was to use advanced machine learning algorithms to create an innovative application that could continuously and objectively monitor the risk of change of the patient’s mental state. The solution would enable more effective detection and earlier prediction of the patient's phase changes and speed up the contact with the doctor.

Effects

The MoodMon application uses advanced machine learning algorithms to help maintain the stable condition of patients struggling with depression and bipolar disorder, by detecting early changes in a patient's mental state. The solution allows a rapid response and prevention of serious consequences of deterioration including possible suicide attempts, optimises the number and frequency of specialist visits according to the patient's current condition, and allows the appropriate dose of medication to be adjusted.

Clinical trials of the app have shown that it can significantly reduce hospitalisations for patients with affective disorders. During the trials of the app, no cases of hospitalisations were reported, while in the two years preceding the trials, the number of such cases in the same group of patients was just around 12% compared to regular 40% of hospitalisation.

Published studies indicate that continuous and effective monitoring of patients reduces the costs associated with medical care by 30% for depression and 17% for bipolar affective disorder. Reducing the risk of hospitalisation by detecting mood phase changes earlier also reduces the patient's costs and losses due to, among other things, loss of work or difficulties in daily functioning.

It was a difficult project for both technical and medical specialists due to the interdisciplinary character. The Britenet technological team’s dedication, interest, and persistence were remarkable. Social importance of the goal made a great impression and triggered a lot of positive energy that allowed us to achieve ambitious goals in a record time with excellent quality.

Małgorzata Sochacka – Product Owner MoodMon

Solution

Patients were provided with a clear and easy-to-use app collecting data about the patient's behaviour. The app, together with a sports bracelet paired with it, collects key behavioural markers: speech, activity, and sleep quality parameters, which are used by AI models to evaluate the patient’s mental state.

Machine learning algorithms built and trained by Britenet on historical data, analyse the collected data so that the application can send an alert about a suspected imminent change in the patient's mental state. Such an alert is sent to the patient, the attending physician, and others authorised by the patient.

Doctors use the web application to provide clinical information to allow for continuous training of AI models.

Additional features of both applications improved the contact of the patient with the clinician and provided information thus improving the effectiveness of care.

The MoodMon app is the first solution to use models trained at both group and individual level. Another innovative aspect of the project we have implemented is the careful selection of parameters used to analyse the patient's condition. In collaboration with specialists we have selected the 234 most important, valuable, and predictive parameters from more than a thousand different parameters – including difficult to analyse voice parameters, such as speech speed, number and length of pauses, mel cepstral coefficients, frequency-band energy, frequency distribution, and many others. It is important to stress that the application does not analyse the content of the speech nor invades the patient's privacy in any other way.

Conclusion

In conclusion, Britenet's proficient IT developers have played a vital role in the success of Project Moodmon. Their efficient work has not only ensured timely delivery but has also contributed significantly to Moodmon's functionality. Through their dedication and expertise, they've propelled Moodmon forward, showcasing the power of effective collaboration and skillful execution. This partnership has not only transformed mental health care but also underscored the value of investing in top-notch IT talent.

Company background

Project Moodmon is an advanced telemedicine platform designed to support individuals with bipolar disorder, their doctors, and families. Moodmon provides personalised interventions and continuous monitoring, thanks to advanced machine learning algorithms offers an application that can objectively monitor changes in the patient's mental state, facilitating early detection and prediction of phase changes. Validated through extensive clinical trials. Moodmon aims to revolutionise mental health care, ultimately improving outcomes for individuals with bipolar disorder.