Machine learning predicts schizophrenia relapses using smartphone data
A pair of newly published studies are demonstrating how passive smartphone data can be used to effectively predict relapse episodes in schizophrenia patients. The research used machine learning to analyze behavioral data and predict schizophrenic relapses up to one month before they occurred.
The data used in both new papers was gathered from a cohort of 60 subjects with schizophrenia. Passive smartphone data, such as accelerometer readings and phone call metadata (such as frequency of calls and durations) was captured for the entire cohort. Eighteen of the subjects suffered a schizophrenic relapse during the course of the study.
A type of machine learning, dubbed encoder-decoder neural networks, was then used to analyzed the mass of data looking for anomalous behavioral patterns within 30 days of a major relapse. The results revealed an 108 percent increase in behavior anomalies could be detected in the month leading up to a relapse, suggesting this kind of system may be useful for detecting and treating patients before a major schizophrenic episode arises.
“We tried to create an approach where we could tell a clinician: not only is this participant experiencing unusual behavior, these are the specific things that are different in this particular patient,” says Dan Adler, a researcher from Cornell Tech working on the project. “If we can predict when someone’s symptoms are going to change before relapse, we can get them early treatment and possibly prevent an inpatient visit.”
As well as predicting relapses ahead of time, the system could effectively predict patients' self-assessments of their conditions. And a more granular analysis of the data revealed fine-grained symptom changes could also be predicted.
Different kinds of behavioral patterns, as tracked through passive smartphone data, could be associated with specific symptom characteristics. One of the papers, published in the journal Scientific Reports, strikingly presents a hypothetical scenario whereby the system itself could conceivably intervene in real-time to help guide subjects toward behavioral patterns that prevent a looming relapse.
“For example, if there is an unusual change in the ultradian rhythm of environment noise for a couple of hours, the system can prompt the patient to move to an environment that has a lower and more stable level of ambient noise to prevent the noise from affecting the patients’ cognitive performance,” the researchers write. “If the system notices that the patient’s phone usage in certain periods, for example in evening, has a very different pattern than in other periods (morning and afternoon), the system can intervene to change the patient’s phone usage pattern, delaying the arrival of phone notifications for instance, to avoid an increase in stress.”
Tanzeem Choudhury, from Cornell Tech and co-author on both of the new papers, suggests the system developed could be appropriated for many mental health conditions. Even major depressive episodes, he suggests, could be predicted ahead of time by passively tracking extreme behavioral changes.
“By focusing on changes in behavioral routines and misalignment with underlying biological rhythms, we expect our approach to generate clinically actionable insights that generalize across a diverse demographic of users,” says Choudhury.
The new studies were published in the journals Scientific Reports and the Journal of Medical Internet Research mHealth and uHealth.
Source: Cornell Chronicle