ΑΙhub.org
 

Digital health interventions: predicting individual success using machine learning

by
13 April 2020



share this:

Health apps could be better tailored to the individual needs of patients. A statistical technique from the field of machine learning is now making it possible to predict the success of smartphone-based interventions more accurately. These are the findings of an international research team led by the University of Basel and reported in the Journal of Affective Disorders.

Health apps are increasingly used in the context of physical and mental illnesses. Usually, they do not replace traditional treatments but act as adds-on – for example, to improve mood in cases of depression. Smartphone-based interventions are of particular relevance in low or middle-income countries, where traditional treatment options are not always or only partially available.

Predicting improvement in mood

However, the impact of these apps varies from individual to individual. And even in the same person, the interventions have stronger or weaker effects depending on the situation. A research group from the Faculty of Psychology at the University of Basel, led by Professor Marion Tegethoff and Professor Gunther Meinlschmidt, investigated how the impact of smartphone-based interventions can be predicted more accurately. To this end, they used data from 324 smartphone-based interventions that aimed to regulate mood.

They employed a statistical technique from the field of machine learning, a specific form of the random forest method. This classification method can be used to process large volumes of data. The strength of this procedure is that it allows researchers to offer the decision trees relevant and theory-driven information, such as how tired or restless a subject is. The “learning forest” combines these characteristics with each other in multiple different ways and allows for predictions that reflect the complexity of real life more effectively than those of traditional prediction methods.

Halving the number of unsuccessful uses

In the case described, approximately 6 out of 10 interventions resulted in no mood improvements. In the interventions predicted to be successful by machine learning, however, this number was only around 3 in 10. Hence, with this new technique, the number of unsuccessful uses could be cut by half.

“We know that many patients quickly abandon digital interventions after they start using them. If an app is only effective in one out of every two or three uses, people soon lose motivation and see little point in using it any longer. Therefore, the new approach could potentially lead to patients using smartphone-based interventions for longer periods,” explains Professor Meinlschmidt, first author of the article. Further, the study delivers important information on how interventions can be better tailored to the individual, in terms of personalized treatment, in future. One could envisage using the approach in many other fields where mobile apps are applied.

The study was supported by the Swiss National Science Foundation and the National Research Foundation of Korea, and conducted in collaboration with Korea University, Harvard Medical School, the International Psychoanalytic University Berlin and RWTH Aachen, under the leadership of the University of Basel.

Read the research paper to find out more:

Personalized prediction of smartphone-based psychotherapeutic micro-intervention success using machine learning
Gunther Meinlschmidt, Marion Tegethoff, Angelo Belardi, Esther Stalujanis, Minkyung Oh, Eun Kyung Jung, Hyun-Chul Kim, Seung-Schik Yoo, Jong-Hwan Lee
Journal of Affective Disorders (2019)




University of Basel




            AIhub is supported by:


Related posts :



#AAAI2024 invited talk: Milind Tambe – using ML for social good

Winner of the 2024 AAAI Award for Artificial Intelligence for the Benefit of Humanity, Milind spoke about recent projects.
01 March 2024, by

AIhub monthly digest: February 2024 – causal relations in text, applied reinforcement learning, and AAAI 2024

Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.
29 February 2024, by

#AAAI2024 in tweets: part two

Find out what the conference participants got up to during the second half of the event.
28 February 2024, by

Unlocking the potential of entity-centric knowledge graphs: transforming healthcare and beyond

The concept of entity-centric knowledge graphs holds promise in reshaping how we organize, access, and leverage data.
27 February 2024, by and

Congratulations to the #AAAI2024 outstanding paper winners

The winners of the outstanding papers were announced at the conference during the opening ceremony.
26 February 2024, by

#AAAI2024 in tweets: part one

Find out what the conference participants have been up to over the past few days.
23 February 2024, by





©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association