Studies in health technology and informatics
Jan 1, 2018
Depression is the most common psychiatric disorder worldwide, which affects more than 300 million people. We aimed to detect depressed patients and healthy people automatically. We work on the PHQ-9 questionnaires and reduced it to a PHQ-5 questionna...
Studies in health technology and informatics
Jan 1, 2018
The study demonstrated an application of machine learning techniques in building a depression prediction model. We used the NSHAP II data (3,377 subjects and 261 variables) and built the models using a logistic regression with and without L1 regulari...
Studies in health technology and informatics
Jan 1, 2017
Under-diagnosis of depression and anxiety is common in older adults. This project took a mixed methods approach to explore the application of machine learning and technology for early detection of these conditions. Mood measures collected with digita...
International journal of rehabilitation research. Internationale Zeitschrift fur Rehabilitationsforschung. Revue internationale de recherches de readaptation
Sep 1, 2015
Although gait abnormality is one of the most disabling events following stroke, cognitive, and psychological impairments can be devastating. The Lokomat is a robotic that has been used widely for gait rehabilitation in several movement disorders, esp...
Studies in health technology and informatics
Jan 1, 2015
Depression in adolescence is associated with significant suicidality. Therefore, it is important to detect the risk for depression and provide timely care to adolescents. This study aims to develop an ontology for collecting and analyzing social medi...
Studies in health technology and informatics
Jan 1, 2015
Bipolar Disorder (BD) is a chronic and disabling disease that usually appears around 20 to 30 years old. Patients who suffer with BD may struggle for years to achieve a correct diagnosis, and only 50% of them generally receive adequate treatment. In ...
Studies in health technology and informatics
Jan 1, 2015
About 1 in 10 adults are reported to exhibit clinical depression and the associated personal, societal, and economic costs are significant. In this study, we applied the MTERMS NLP system and machine learning classification algorithms to identify pat...