Patrick Staples

TL;DR: Based on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively.

TL;DR: These findings show how passive smartphone data, data collected in the background during regular phone use without active input from the subjects, can provide an unprecedented and detailed view into patient behavior outside the clinic, therefore reducing patient suffering and reducing the cost of care.

TL;DR: Patients with major depressive disorder are able to utilize an app on their personal smartphones to self-assess their symptoms of major depressive Disorder with high levels of adherence and scores recorded from the app may potentially be more sensitive and better able to capture suicidality than the traditional PHQ-9.

TL;DR: By matching smartphone data with appropriate statistical methods, psychiatry can better realize the potential of mobile mental health and empower both patients and providers with novel clinical tools.

TL;DR: The relationship between data quality and future symptom-related survey responses in 16 patients with schizophrenia was examined and it was found that smartphone sensor data as well as phone-use metrics related to the completion of symptom- related surveys were significantly associated with survey results, highlighting the clinical relevance of this approach.