Joshua Curtiss
Northeastern University, MA·Psychology·Early-career
Emotional disorders such as depression and anxiety are among the leading causes of disability worldwide. Predicting how a person's symptoms will change could allow earlier, better-tailored treatment, but accurate forecasts are hard to make.
- Fields
Psychology
Medicine & Clinical Research
Computer Science
- Topics
emotional disorders
depression
anxiety
- Grants
- 1 known grant, through Apr 2029
What they work on
- Evaluate whether forecasting machine learning using dynamical systems and network features from ecological momentary assessment data can predict future symptoms across emotional disorders.
- Test whether adding dynamic time-series features from passive sensor data, collected continuously over four weeks, improves symptom prediction beyond self-reported EMA data.
- Develop dynamic time-series phenotypes, or subtypes, of emotional disorders from combined EMA and sensor data, using unsupervised machine learning.
More about the research
Most predictions rely on a single snapshot of symptoms. These disorders, however, unfold as behaviors and feelings that shift over time. This project treats symptoms as dynamic systems and builds machine learning tools that forecast their future course.
Why it matters
More accurate forecasts of symptom changes could let clinicians monitor people with depression, anxiety and related disorders more closely. They could also deliver "just-in-time" interventions before symptoms worsen and tailor care to each person. Better prediction may help reduce one of the largest causes of disability and financial burden worldwide.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Melvin McInnis
University of Michigan, MI·Medicine & Clinical Research
Test whether average levels of emotional valence and arousal, measured by phone self-ratings (EMA) and by the PRIORI speech algorithm, predict self-reported and clinician-rated mood severity in 160 adults with bipolar disorder.
Leslie Adams
Stanford University, CA·Public Health & Epidemiology·Early-career
Modify an existing smartphone digital phenotyping protocol, combining ecological momentary assessment surveys with passive GPS and accelerometer data, so it is appropriate for Black youth.
Madhukar Trivedi
University of Texas Southwestern Medical Center, TX·Medicine & Clinical Research
Investigate how the amount of social media use and positive or negative online experiences affect depression, anxiety and suicidal ideation trajectories over six months in 100 at-risk Texas youth.
The school
Northeastern University: 27 labs on PI Pages, $347M a year on research, 251 research doctorates a year.
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