Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares

A novel framework for distress detection through an automated speech processing system

Based on our ongoing work, this work in progress project aims to develop an automated system to detect distress in people to enable early referral for interventions to target anxiety and depression, to mitigate suicidal ideation and to improve adherence to treatment. The project will utilize either use existing voice data to assess people into various scales of distress, or will collect voice data as per existing standards of distress measurement, to develop basic computing algorithms required to detect various attributes associated with distress, detected through a person’s voice in a telephone call to a helpline. This will be then matched with the already available psychological assessment instruments such as the Distress Thermometer for these persons. In order to trigger interventions, organizational contexts are essential as interventions rely on the type of distress. Therefore, the model will be tested on various organizational settings such as the Police, Emergency and Health along with the Distress detection instruments normally used in a psychological assessment for accuracy and validation. The outcome of the project will culminate in a fully automated integrated system, and will save significant resources to organizations. The translation of the project will be realized in step-change improvements to quality of life within the gamut of public policy

Pragmatic characteristics of patient-reported outcome measures are important for use in clinical practice

OBJECTIVES: Measures for assessing patient-reported outcomes (PROs) that may have initially been developed for research are increasingly being recommended for use in clinical practice as well. Although psychometric rigor is essential, this article focuses on pragmatic characteristics of PROs that may enhance uptake into clinical practice. STUDY DESIGN AND SETTING: Three sources were drawn on in identifying pragmatic criteria for PROs: (1) selected literature review including recommendations by other expert groups; (2) key features of several model public domain PROs; and (3) the authors' experience in developing practical PROs. RESULTS: Eight characteristics of a practical PRO include: (1) actionability (i.e., scores guide diagnostic or therapeutic actions/decision making); (2) appropriateness for the relevant clinical setting; (3) universality (i.e., for screening, severity assessment, and monitoring across multiple conditions); (4) self-administration; (5) item features (number of items and bundling issues); (6) response options (option number and dimensions, uniform vs. varying options, time frame, intervals between options); (7) scoring (simplicity and interpretability); and (8) accessibility (nonproprietary, downloadable, available in different languages and for vulnerable groups, and incorporated into electronic health records). CONCLUSION: Balancing psychometric and pragmatic factors in the development of PROs is important for accelerating the incorporation of PROs into clinical practice