MENTAL.AI

Computer-aided early detection,
diagnosis and treatment of
mental disorders

Brief description of Mental.AI

Mental Health, Augmented Artificial Intelligence, Early Detection, Computer-Aided Diagnosis and Treatment, Telemedicine, Personalized Medicine, Impact on the Society


The goal of the MENTAL.AI project is to design and study new models for mental health computer-aided diagnosis, monitoring and therapy systems within the context of face-to-face and ECA-to-patient interviews. Namely, we propose to target three objectives - Monitoring, Automatic Diagnosis and Virtual Therapist -, within which we will propose solutions that overcome current limitations of state-of-the-art applications.

PARTNERS

University of Beira Interior

Bulgarian Academy of Science

Centro Hospitalar Cova da Beira

Tallinn University of Technology

University of Caen Normandie/CNRS

HEALTH DATA HUB

Hospital of Caen

University of Santiago de Compostela

University of Birmingham

Adapt Center

University of Gothenburg

OBJECTIVES

  • Mental disorders are characterized by high comorbidity, i.e. different disorders/pathologies often intersect.
  • As a consequence, the correct diagnosis is complex and can be long to determine.
  • To overcome such an issue, a new paradigm has been emerging in Psychiatry: Symptom-based analysis vs. Pathology-based analysis.
  • Medicine is also evolving towards 6P medicine
    • Personalized, Participative, Preventive, Predictive, Proof, Pathway
  • The objective of the MENTAL.AI project is to deal with 6P medicine within a symptom based strategy - Monitoring, Automatic Diagnostic and Virtual Therapist.

Many Different Symptoms

  • Apathy,
  • Avoidance,
  • Excessive fear or uneasiness,
  • Feeling of disconnection,
  • Increased sensitivity,
  • Mood changes,
  • Problems thinking,
  • Significant tiredness,
  • Sleep or appetite changes,
  • Withdrawal,
  • Etc.

6P Medicine

  • 1P - Personalized: Personalized medicine consists of adapting a medical treatment according to the individual characteristics of a patient.
  • 2P - Preventive: Preventive medicine focuses on wellness, and consists of measures taken for disease prevention.
  • 3P - Predictive: Predictive medicine is a branch of medicine that aims to identify patients at risk of developing a disease.
  • 4P - Participative: Medicine should be participatory, leading patients to be more responsible for their health and care.
  • 5P - Proof: Medicine must be based on evidence of medical service to patients, especially when it relies on connected health and telemedicine.
  • 6P - Pathway: Coordinating multiple interventions (medical, social, occupational medicine, etc.) such that the healthcare pathway is progressively articulated, according to the pathology and its evolution.

6P Medicine and Mental Health

PART I: MULTISOURCE DATA INTEGRATION

  • Data acquisition
    • Corpus of face-to-face interviews
    • Social network posts
    • Clinical data (discrete data)
  • Data annotation
    • Manual annotation
    • Standards for annotation
      • Document level
      • Phrase level
  • Data security
    • Anonymization
  • Data integration
    • Parallel between acquisition and clinical information system

PART II: DIGITAL PHENOTYPING

  • Visual indicators:
    • head pose, fatigue, mutual eye gaze, emotions, smiles, self-touches, body stiffness, etc.
  • Speech indicators:
    • voice tone, frequency, amplitude, etc.
  • Text indicators:
    • emotions, temporality, discourse coherence, abstractness, concreteness, topicality, idea intensity, rumination, misspelling, etc.
  • Behavioral indicators:
    • keystroke dynamics
  • Group behavior
    • Communicative issues, self-containment, (either within physical groups or virtual groups such as social networks), etc.
  • Skin indicator
    • Sensibility of skin via specific low noise captors

PART III: AUTOMATIC DIAGNOSIS

  • Multimodal fusion:
    • Early, late, hybrid, cascade, multi-layer fusion techniques to combine digital phenotypes, etc.
  • Multitask learning:
    • Taking advantage of different pathologies that share some common symptoms (e.g. depression and bipolarity) with shared-private architectures, etc.
    • Multiclass Multitask models to specify if some patient has depression, anxiety, schizophrenia or something else by taking advantage of relation between pathologies. This can be important as diagnosis is usually difficult between these pathologies.
  • Multilingual learning
    • Taking advantage of different languages to strengthen commonalities of the disorders, learning the specificities of each country, tackling the lack of transcripts in one language to propose robust models, etc.
  • Demographic-aware learning
    • Gender-aware models, region-aware models (different cultures may have different ways of expressing thoughts), age-aware models, etc.
  • Combined feature-based (from PART I) and deep learning architectures to reach explainable models (Hybrid AI).

PART IV: EARLY DETECTION

  • Social Networks:
    • Early prediction of mental instability of individuals (temporal models, lack of positive activities),
    • Early prediction by high comorbidity detection (i.e. drug abuse)
    • Virtual robots sending posts to warn and inform about dangers, etc.
  • Embodied Conversational Agent
    • Virtual engagement of younger populations to pass through psychological tests, trying to reach therapeutic alliance in the ECA, etc.
  • Symptom-based patient monitoring
    • Monitoring users facing a major traumatic event, who are likely to develop a mental disorder.

PART V: COMPUTER-AIDED TREATMENT

  • Mobile applications
    • Monitor adjuvant therapies (e.g. meditation, physical activity)
    • Automatic recommendation system based on data gathering and mining (e.g. proposal of personal contacts, of specific meditations or physical activities)
    • Open data linking to access contextual information
    • Embodied Conversational Agent
    • Safe Social Network
  • Virtual reality applications
    • Re-education of brain plasticity (e.g. phobias, etc.)

PART VI: IMPROVING QUALITY OF LIFE

  • Access to the Web with cognitive impairment
    • Text simplification
    • Text/image augmentation
    • Text abstractness vs. concreteness
  • Tiredness monitoring
    • Mobile application helping in managing tiredness

PART VII: IMPACT ON THE SOCIETY

  • Sociological issues
    • How much society is prepared for this digital transformation (e.g. covid-19 situation of containment changes or not mentality?)
  • Legal issues
    • Telemedicine raises issues on self-agreement, etc.
  • Public health
    • Mental health observatory.

PART VIII: OPEN PLATFORM, CLOUD COMPUTING AND SERVICE INTEGRATION

  • Human Computer Interfaces
    • User-centered development, prototyping, and users’ needs
      • Monitoring platforms
      • Embodied conversational agent
      • Chatbots on Social Network
      • Virtual Reality scenarios
      • Social Network
  • Data security issues
    • How to preserve data anonymity, ensure data security etc.
  • Project platform
    • Development of the software architecture, module integration, etc.
  • Implementation in vivo
    • Deployment in real-world scenarios.



Conclusion

The goal of the MENTAL.AI project is to design and study new models for mental health computer-aided diagnosis, monitoring and therapy systems within the context of face-to-face and ECA-to-patient interviews. In particular, we follow the augmented artificial intelligence paradigm, which focuses on the assistive role of artificial intelligence. Technology is designed to enhance human intelligence rather than replace it. As such, models and applications will be developed in straight collaboration between computer scientists, psychiatrists, psychologists, and other professionals from the medical staff.