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  1. Article ; Online: What I stand for as

    Cipriani, Andrea

    BMJ mental health

    2023  Volume 26, Issue 1

    MeSH term(s) Mental Health ; Editorial Policies
    Language English
    Publishing date 2023-02-16
    Publishing country England
    Document type Editorial
    ISSN 2755-9734
    ISSN (online) 2755-9734
    DOI 10.1136/bmjment-2023-300664
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Carpe diem.

    Cipriani, Andrea

    Evidence-based mental health

    2022  Volume 25, Issue 4, Page(s) 143–144

    Language English
    Publishing date 2022-12-27
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 2009065-1
    ISSN 1468-960X ; 1362-0347
    ISSN (online) 1468-960X
    ISSN 1362-0347
    DOI 10.1136/ebmental-2022-300608
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Understanding and responding to the drivers of inequalities in mental health.

    Bhui, Kamaldeep / Cipriani, Andrea

    BMJ mental health

    2023  Volume 26, Issue 1

    MeSH term(s) Humans ; Mental Health ; Psychotic Disorders ; Schizophrenia
    Language English
    Publishing date 2023-12-18
    Publishing country England
    Document type Editorial
    ISSN 2755-9734
    ISSN (online) 2755-9734
    DOI 10.1136/bmjment-2023-300921
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Muscarinic drug shows efficacy in schizophrenia but much is left to be discovered.

    Cipriani, Andrea / Agunbiade, Adeola / Salanti, Georgia

    Lancet (London, England)

    2023  Volume 403, Issue 10422, Page(s) 120–122

    MeSH term(s) Humans ; Schizophrenia/drug therapy ; Cholinergic Agents/therapeutic use ; Antipsychotic Agents/therapeutic use
    Chemical Substances Cholinergic Agents ; Antipsychotic Agents
    Language English
    Publishing date 2023-12-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 3306-6
    ISSN 1474-547X ; 0023-7507 ; 0140-6736
    ISSN (online) 1474-547X
    ISSN 0023-7507 ; 0140-6736
    DOI 10.1016/S0140-6736(23)02415-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Pharmacological interventions for insomnia disorder in adults - Authors' reply.

    Cipriani, Andrea / Ostinelli, Edoardo G / Cowen, Philip J

    Lancet (London, England)

    2022  Volume 400, Issue 10366, Page(s) 1846

    MeSH term(s) Adult ; Humans ; Sleep Initiation and Maintenance Disorders/drug therapy ; Cognitive Behavioral Therapy
    Language English
    Publishing date 2022-11-24
    Publishing country England
    Document type Letter ; Comment
    ZDB-ID 3306-6
    ISSN 1474-547X ; 0023-7507 ; 0140-6736
    ISSN (online) 1474-547X
    ISSN 0023-7507 ; 0140-6736
    DOI 10.1016/S0140-6736(22)02001-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Promoting inclusivity by ensuring that all patients with mental health issues are offered research opportunities in the NHS.

    Henshall, Catherine / Jones, Helen / Smith, Tanya / Cipriani, Andrea

    Evidence-based mental health

    2022  Volume 25, Issue 1, Page(s) e1

    MeSH term(s) Anxiety Disorders ; Humans ; Mental Health ; Psychotic Disorders ; State Medicine
    Language English
    Publishing date 2022-01-07
    Publishing country England
    Document type Letter ; Research Support, Non-U.S. Gov't
    ZDB-ID 2009065-1
    ISSN 1468-960X ; 1362-0347
    ISSN (online) 1468-960X
    ISSN 1362-0347
    DOI 10.1136/ebmental-2021-300411
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research.

    Langholm, Carsten / Kowatsch, Tobias / Bucci, Sandra / Cipriani, Andrea / Torous, John

    Digital biomarkers

    2023  Volume 7, Issue 1, Page(s) 104–114

    Abstract: The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. ...

    Abstract The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following:
    Language English
    Publishing date 2023-08-25
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2504-110X
    ISSN (online) 2504-110X
    DOI 10.1159/000530698
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Explainable artificial intelligence for mental health through transparency and interpretability for understandability.

    Joyce, Dan W / Kormilitzin, Andrey / Smith, Katharine A / Cipriani, Andrea

    NPJ digital medicine

    2023  Volume 6, Issue 1, Page(s) 6

    Abstract: The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability ...

    Abstract The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to "ground" in our understanding of how algorithms and models operate and are used more consistently in the literature. We describe the TIFU (Transparency and Interpretability For Understandability) framework and examine how this applies to the landscape of AI/ML in mental health research. We argue that the need for understandablity is heightened in psychiatry because data describing the syndromes, outcomes, disorders and signs/symptoms possess probabilistic relationships to each other-as do the tentative aetiologies and multifactorial social- and psychological-determinants of disorders. If we develop and deploy AI/ML models, ensuring human understandability of the inputs, processes and outputs of these models is essential to develop trustworthy systems fit for deployment.
    Language English
    Publishing date 2023-01-18
    Publishing country England
    Document type Journal Article ; Review
    ISSN 2398-6352
    ISSN (online) 2398-6352
    DOI 10.1038/s41746-023-00751-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: IF.

    Cipriani, Andrea / Cortese, Samuele / Furukawa, Toshi A

    Evidence-based mental health

    2021  

    Language English
    Publishing date 2021-07-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 2009065-1
    ISSN 1468-960X ; 1362-0347
    ISSN (online) 1468-960X
    ISSN 1362-0347
    DOI 10.1136/ebmental-2021-300301
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article: Teaching Telepsychiatry Skills: Building on the Lessons of the COVID-19 Pandemic to Enhance Mental Health Care in the Future.

    Smith, Katharine / Torous, John / Cipriani, Andrea

    JMIR mental health

    2022  Volume 9, Issue 10, Page(s) e37939

    Abstract: COVID-19 has accelerated the use of telehealth and technology in mental health care, creating new avenues to increase both access to and quality of care. As video visits and synchronous telehealth become more routine, the field is now on the verge of ... ...

    Abstract COVID-19 has accelerated the use of telehealth and technology in mental health care, creating new avenues to increase both access to and quality of care. As video visits and synchronous telehealth become more routine, the field is now on the verge of embracing asynchronous telehealth, with the potential to radically transform mental health. However, sustaining the use of basic synchronous telehealth, let alone embracing asynchronous telehealth, requires new and immediate effort. Programs to increase digital literacy and competencies among both clinicians and patients are now critical to ensure all parties have the knowledge, confidence, and ability to equitably benefit from emerging innovations. This editorial outlines the immediate potential as well as concrete steps toward realizing the potential of a new, more personalized, scalable mental health system.
    Language English
    Publishing date 2022-10-14
    Publishing country Canada
    Document type Editorial
    ZDB-ID 2798262-2
    ISSN 2368-7959
    ISSN 2368-7959
    DOI 10.2196/37939
    Database MEDical Literature Analysis and Retrieval System OnLINE

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