Predictive Modeling of Depression with a Large Claim Dataset Depression has been a major concern for the employers due to its high prevalence and cost. In United States, 6.8% of the work population had depression and it is accountable for over 100 billion workplace loss every year. Early detection and timely intervention is the key to address this challenge. In this paper, we proposed to leverage a large claim dataset to build depression risk prediction model. This approach is easy to implement and scale because the claim data is accessible for employers at no additional cost. We derived a subset of over 7.2 million patients in a 3year period, with 2,099 variables including the diagnosis, procedure, medication, and health service provider information. We trained two models (Least Absolute Shrinkage and Selection Operator (LASSO) and random forest *RF)) with first two years data to predict the onset of depression in the third year. Both models achieved the best comparable performance so far according to our knowledge. The important variables identified by our algorithms are mental health related medications and diagnoses (other than depression), gender (female), and other chronic conditions such as back pain. We further validated these variables by confirming in clinical literature. We concluded that the claim data based depression prediction is a viable and low cost approach, especially for selfinsured employers.

Toward Depression Recognition Using EEG and Eye Tracking: An Ensemble Classification Model CBEM Depression, influencing millions of people, has become a major disease in the past decade. However, the assessment methods of diagnosing depression almost exclusively rely on patientreported or clinical judgments of symptom severity, which are associated with subjective biases and intensive labor. Some biosignals such as EEG and eye movements are used for automatic detection but their accuracies are not accurate enough for the real application, further improvements are needed. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, generating data subsets by the content of the experiment, then using the majority vote of subsets to determine the subjectsu0027 label. The validation of the method is testified by two different experiments which included free viewing eye tracking and taskstate EEG and these two experiments have 36, 40 subjects respectively. In these two experiments CBEM gains accuracies of 82.5% and 92.73% respectively. The results show that CBEM outperform traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and give an objective and quantitative evaluation of depression, which in the future could be used for the auxiliary diagnosis of depression.