Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

Parameterizing And Validating Existing Algorithms For Identifying Out-Of-Bed Time Using Hip-Worn Accelerometer Data From Older Women, John Belletierre, Yiliang Zhang, Vincent Berardi, Kelsie M. Full, Jacqueline Kerr, Michael J. Lamonte, Kelly R. Evenson, Melbourne Hovell, Andrea Z. Lacroix, Chongzhi Di

Psychology Faculty Articles and Research

Objective: To parameterize and validate two existing algorithms for identifying out-of-bed time using 24-hour hip-worn accelerometer data from older women. Approach: Overall, 628 women (80±6 years old) wore ActiGraph GT3X+ accelerometers 24 hours/day for up to 7 days and concurrently completed sleep-logs. Trained staff used a validated visual analysis protocol to measure in-bed periods on accelerometer tracings (criterion). The Tracy and McVeigh algorithms were adapted for optimal use in older adults. A training set of 314 women was used to choose two key thresholds by maximizing the sum of sensitivity and specificity for each algorithm and data (vertical axis, VA, …

Changing Healthcare Provider And Parent Behaviors In The Pediatric Post‐Anesthesia‐Care‐Unit To Reduce Child Pain: Nurse And Parent Training In Postoperative Stress (Np‐Tips), Brooke N. Jenkins, Michelle Fortier, Robert S. Stevenson, Mai Makhlouf, Paulina Lim, Remy Converse, Zeev N. Kain

Psychology Faculty Articles and Research

Background

Children who undergo surgery experience significant pain in the post anesthesia care unit. Nurse and parent behaviors in the post anesthesia care unit directly impact child postoperative pain. Therefore, we have developed and evaluated (Phase 1) and then tested (Phase 2) the feasibility of a new intervention (Nurse and Parent Training in Postoperative Stress) to alter parent and nurse behaviors in a way consistent with reducing child postoperative pain.

Methods

In Phase 1, a multidisciplinary team of experts (physicians, nurses, and psychologists) developed an empirically‐based intervention which was then evaluated by experienced nurses (N = 8) and parents …