Hello! I'm Prerna Chikersal, a fourth year PhD 2 0:00:06.720,0:00:12.080 3 and Predicting its Onset using Longitudinal 4 Symptoms Captured by Passive Sensing: A Machine Learning Approach with Robust Feature Selection".
This work was done in collaboration with my 6 0:00:22.720,0:00:28.560 7 is a huge problem today especially for college 8 students, More than 3 in 10 college students have difficulty functioning due to depression.
Depression is also the leading cause of suicide. 10 0:00:39.360,0:00:45.280 11 of suicide,.. but... students with depression face 12 many barriers to seeking treatment, The most common barrier being the belief that "stress
is a normal part of student life". These students 14 0:00:55.600,0:01:00.880 15 detecting and monitoring depression is necessary, 16 Currently --> detecting and monitoring depression in a large cohort can only be done using periodic
psychometric tests. However ,such tests when 18 0:01:13.680,0:01:18.320 19 efficient tools to detect and monitor depression, 20 For this purpose --> in this project, we collected data from sensors embedded in students smartphones
and fitness trackers in order to capture 22 0:01:30.240,0:01:35.680 23 depression, Specifically --> we worked on detecting two 24 outcomes: post-semester depression --> which we were able to detect with an accuracy of 85.7 percent,
and change in depression, which we were able 26 0:01:47.040,0:01:51.120 27 depression several weeks in advance --> 28 and we were able to achieve an accuracy of over 80 percent, 11 to 15 weeks before the end of the semester.
Now, I'm going to give you an overview of our 30 0:02:03.200,0:02:11.600 31 From each student --> we collected data from 32 four smartphone sensors and two Fitbit sensors over a period of one semester, which is 16 weeks.
The smartphone sensors gave us scanned bluetooth 34 0:02:23.040,0:02:30.880 35 sleep and steps, We collected this data from 138 36 first-year college students, We also administered a well-validated questionnaire at the beginning
and at the end of the study to get pre-semester and 38 0:02:41.680,0:02:49.840 39 levels: no or minimal --> mild, moderate and severe, 40 We binarize the severity levels to get two outcomes: post-semester depression
which is a binary outcome where zero means that 42 0:03:00.080,0:03:05.040 43 depressive symptoms --> and change in depression where 44 zero means that the severity level remains the same and one means that the severity level worsens.
We took the data from all the six sensors 46 0:03:17.520,0:03:22.080 47 and combined the prediction of all seven 48 1-feature-set models to get the final prediction, Now --> let's move on to the results.
For detecting post-semester depression, the majority class baseline 50 0:03:35.600,0:03:41.520 51 82,3 percent. The best sensor combination 52 containing bluetooth --> calls, phone usage, and steps gave us an accuracy of 85.7 percent.
The best one feature set model is the phone usage 54 0:03:54.320,0:04:01.040 55 models are quite close in performance, This 56 graph shows the accuracy obtained for predicting post-semester depression at the end of each week
of the semester. For example. we find that at the 58 0:04:13.200,0:04:19.200 59 depression with an accuracy of 81,3 percent. 60 For the results for change in depression and other results please refer to the paper.
Now, let's talk about the implications of our work. 62 0:04:31.200,0:04:35.600 63 behavioral data and limited ground truth, 64 For example --> our prediction models can be used to carry out preemptive interventions in future
studies. One way to do this would be to have 66 0:04:46.000,0:04:50.720 67 depression is high --> and offer them assistance, 68 Thank you! Please refer to my website for the slides and transcripts of this presentation.