Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors.

While preliminary evidence suggests that sensors may be employed to detect presence of low mood it is still unclear whether they can be leveraged for measuring depression symptom severity. This study evaluates the feasibility and performance of assessing depressive symptom severity by using behavioral and physiological features obtained from wristband and smartphone sensors. Participants were thirty-one individuals with Major Depressive Disorder (MDD).

"Kind and Grateful": A Context-Sensitive Smartphone App Utilizing Inspirational Content to Promote Gratitude.

Background: Previous research has shown that gratitude positively influences psychological wellbeing and physical health. Grateful people are reported to feel more optimistic and happy, to better mitigate aversive experiences, and to have stronger interpersonal bonds. Gratitude interventions have been shown to result in improved sleep, more frequent exercise and stronger cardiovascular and immune systems.

Predicting students' happiness from physiology, phone, mobility, and behavioral data.

In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness.

Diagnosing Coronary Artery Disease via Data Mining Algorithms by Considering Laboratory and Echocardiography Features.

Background: Coronary artery disease (CAD) is the result of the accumulation of athermanous plaques within the walls of coronary arteries, which supply the myocardium with oxygen and nutrients. CAD leads to heart attacks or strokes and is, thus, one of the most important causes of death worldwide. Angiography, an imaging modality for blood vessels, is currently the most accurate method of diagnosing artery stenosis.

Diagnosis of coronary arteries stenosis using data mining.

Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important field of medical studies.

A data mining approach for diagnosis of coronary artery disease.

Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly.