Summary: A new multitasking model artificial intelligence algorithm based on wearable device data predicts treatment outcomes on an individual basis for people with depression.
In recent years, managing one’s mental health has become a priority with an increased focus on self-care. Depression alone affects more than 300 million people worldwide each year.
Recognizing this, there is considerable interest in leveraging popular wearables to monitor an individual’s mental health by measuring markers such as activity levels, sleep and heart rate.
A team of researchers from Washington University in St. Louis and the University of Illinois in Chicago used data from wearable devices to predict depression treatment outcomes in people who took part in a trial randomized clinic.
They developed a new machine learning model that analyzes data from two groups of patients – those randomly selected to receive treatment and those who did not receive treatment – instead of developing a separate model for each group.
This unified multitasking model is a step toward personalized medicine, in which doctors design a treatment plan specific to each patient’s needs and predict outcomes based on an individual’s data.
The research results were published in the ACM Proceedings on Interactive, Modeled, Wearable, and Pervasive Technologies and will be presented at the UbiComp 2022 conference in September.
Chenyang Lu, a Fullgraf professor at the McKelvey School of Engineering, led a team that included Ruixuan Dai, who worked in Lu’s lab as a doctoral student and is now a software engineer at Google; Thomas Kannampallil, associate professor of anesthesiology and associate director of research information at the School of Medicine and associate professor of computer science and engineering at McKelvey Engineering; and Jun Ma, MD, PhD, professor of medicine at the University of Illinois at Chicago (UIC); and colleagues to develop the model using data from a randomized clinical trial conducted by UIC with approximately 100 adults with depression and obesity.
“Integrated behavior therapy can be expensive and time-consuming,” Lu said.
“If we can make personalized predictions for individuals about the likelihood that a patient will respond to a particular treatment, then patients can only continue treatment if the model predicts that their condition is likely to improve with treatment but less likely to go without treatment.Such personalized predictions of treatment response will facilitate more targeted and cost-effective therapy.
In the trial, patients were given Fitbit wristbands and psychological tests. About two-thirds of the patients received behavioral therapy, and the remaining patients did not. Patients in the two groups were statistically similar at baseline, giving the researchers a level playing field from which to discern whether the treatment would lead to better outcomes based on individual data.
Clinical trials of behavioral therapies often involved relatively small cohorts due to the cost and duration of these interventions. The small number of patients created a challenge for a machine learning model, which generally works better with more data.
However, by combining data from both groups, the model could learn from a larger data set, which captured the differences between those who had undergone treatment and those who had not. They found that their multitasking model predicted depression outcomes better than a model examining each of the groups separately.
“We pioneered a multitasking framework, which combines the intervention group and the control group in a randomized controlled trial to jointly train a unified model to predict an individual’s personalized outcomes with and without treatment,” said Dai, who earned a doctorate in computer science. scientists in 2022.
“The model integrated clinical features and wearable data into a multi-layered architecture. This approach avoids dividing study cohorts into smaller groups for machine learning models and enables dynamic knowledge transfer between groups to optimize prediction performance with and without intervention.
“The implications of this data-driven approach extend beyond randomized clinical trials to implementation in clinical care delivery, where the ability to make personalized predictions of patient outcomes based on the treatment received, and doing this early and throughout treatment, could be useful to inform shared decision-making by the patient and treating physician to tailor the treatment plan for that patient,” Ma said.
The machine learning approach provides a promising tool for building personalized predictive models based on data collected from randomized controlled trials.
Going forward, the team plans to leverage the machine learning approach in a new randomized controlled trial of telehealth behavioral interventions using Fitbit wristbands and weight scales in patients in an intervention study. of weight loss.
About this neurotechnology and depression research news
Author: Brandie Jefferson
Contact: Brandie Jefferson – WUSTL
Image: Image is in public domain
Original research: Free access.
“Multi-task learning for randomized controlled trials: a case study in predicting depression with wearable data” by Chenyang Lu et al. ACM Proceedings on Portable and Ubiquitous Interactive Mobile Technologies
Multitasking learning for randomized controlled trials: a case study in predicting depression with wearable data
A randomized controlled trial (RCT) is used to study the safety and effectiveness of new treatments, comparing the results of patients in an intervention group with a control group. Traditionally, RCTs have relied on statistical analyzes to assess differences between treatment and control groups.
However, these statistical analyzes are generally not designed to assess the impact of the intervention at the individual level. In this article, we explore machine learning models in conjunction with an RCT for personalized predictions of a depression treatment intervention, where patients were followed longitudinally with wearable devices.
We formulate individual-level predictions in the intervention and control groups from an RCT as a multi-task learning (MTL) problem, and propose a novel MTL model specifically designed for RCTs. Instead of training separate models for the intervention and control groups, the proposed MTL model is trained on both groups, effectively expanding the training dataset.
We develop a hierarchical model architecture to aggregate data from different sources and from different longitudinal stages of the trial, which allows the MTL model to exploit commonalities and capture differences between the two groups. We evaluated the MTL approach in an RCT involving 106 patients with depression, who were randomized to receive integrated intervention treatment.
Our proposed MTL model outperforms both single-task and traditional multi-task models in predictive performance, representing a promising step in using data collected in RCTs to develop predictive models for medicine. precision.
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