From eHealth to iHealth: Transition to Participatory and Personalized Medicine in Mental Health

Abstract

Clinical assessment in psychiatry is commonly based on findings from brief, regularly scheduled in-person appointments. Although critically important, this approach reduces assessment to cross-sectional observations that miss essential information about disease course. The mental health provider makes all medical decisions based on this limited information. Thanks to recent technological advances such as mobile phones and other personal devices, electronic health (eHealth) data collection strategies now can provide access to real-time patient self-report data during the interval between visits. Since mobile phones are generally kept on at all times and carried everywhere, they are an ideal platform for the broad implementation of ecological momentary assessment technology. Integration of these tools into medical practice has heralded the eHealth era. Intelligent health (iHealth) further builds on and expands eHealth by adding novel built-in data analysis approaches based on (1) incorporation of new technologies into clinical practice to enhance real-time self-monitoring, (2) extension of assessment to the patient’s environment including caregivers, and (3) data processing using data mining to support medical decision making and personalized medicine. This will shift mental health care from a reactive to a proactive and personalized discipline.

The PRIORI Emotion Dataset: Linking Mood to Emotion Detected In-the-Wild

“…and final dense layers equal and validated them over a set of (2,3). The number of nodes (200,400) and the length of the convolution kernels (4,8) were also validated. We trained FFNN and Conv-Pool networks for 100 and 15, epochs respectively.…”

“…We set epoch size to 64. To train the FFNN, we performed crossvalidation to tune the number of dense layers (2,4,8) and the number of nodes in each layer (200, 400, 800). To train Conv-Pool network, we set the number of initial convolutional layers Table 3: Subject-specific mean activation and valence ratings for manic and depressed states.…”

“…Strategies for mobile monitoring for mental health have mostly relied upon self-reported diagnosis of a disorder on a device or social media to identify features indicative of the disorder [4], be it anxiety [5], BD [6], depression or anorexia [7]. However, these interactive self-reports are often incomplete or misleading [8].…”

Praxis der Teledermatologie

“…Selbst wenn ein Großteil der Patienten inzwischen im Alltags‐ und Berufsleben in der Handhabung von Computern, Smartphones und anderen digitalen Informationsträgern geschult ist, sollte jeder Patient vor Vereinbarung teledermatologischer Maßnahmen über diese aufgeklärt werden und diesen Maßnahmen explizit zustimmen. Im Zuge einer „partizipativen Entscheidungsfindung“ sind Vor‐ und Nachteile auch aus Patientensicht abzuwägen . Die Aufklärung beinhaltet dabei hinreichendes Wissen über die eigenverantwortliche Nutzung der telemedizinischen Maßnahmen, deren Nutzen, Grenzen und Risiken.…”

Biomedical Nanotechnology Related Grand Challenges and Perspectives

“…IoMT outcomes are useful in transitioning biomedical technology from the laboratory to the field. Keeping these outcomes in mind, efforts are being made to transform electronic health to intelligent health (Eysenbach, 2001;Berrouiguet et al, 2018).…”