LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 20

Search options

  1. Article ; Online: Chatbots in the fight against the COVID-19 pandemic.

    Miner, Adam S / Laranjo, Liliana / Kocaballi, A Baki

    NPJ digital medicine

    2020  Volume 3, Page(s) 65

    Abstract: We are all together in a fight against the COVID-19 pandemic. Chatbots, if effectively designed and deployed, could help us by sharing up-to-date information quickly, encouraging desired health impacting behaviors, and lessening the psychological damage ... ...

    Abstract We are all together in a fight against the COVID-19 pandemic. Chatbots, if effectively designed and deployed, could help us by sharing up-to-date information quickly, encouraging desired health impacting behaviors, and lessening the psychological damage caused by fear and isolation. Despite this potential, the risk of amplifying misinformation and the lack of prior effectiveness research is cause for concern. Immediate collaborations between healthcare workers, companies, academics and governments are merited and may aid future pandemic preparedness efforts.
    Keywords covid19
    Language English
    Publishing date 2020-05-04
    Publishing country England
    Document type Journal Article
    ISSN 2398-6352
    ISSN (online) 2398-6352
    DOI 10.1038/s41746-020-0280-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  2. Article ; Online: Perceptions of Data Set Experts on Important Characteristics of Health Data Sets Ready for Machine Learning: A Qualitative Study.

    Ng, Madelena Y / Youssef, Alaa / Miner, Adam S / Sarellano, Daniela / Long, Jin / Larson, David B / Hernandez-Boussard, Tina / Langlotz, Curtis P

    JAMA network open

    2023  Volume 6, Issue 12, Page(s) e2345892

    Abstract: Importance: The lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed ... ...

    Abstract Importance: The lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed clinical AI applications for patient care.
    Objective: To discern what constitutes high-quality and useful data sets for health and biomedical ML research purposes according to subject matter experts.
    Design, setting, and participants: This qualitative study interviewed data set experts, particularly those who are creators and ML researchers. Semistructured interviews were conducted in English and remotely through a secure video conferencing platform between August 23, 2022, and January 5, 2023. A total of 93 experts were invited to participate. Twenty experts were enrolled and interviewed. Using purposive sampling, experts were affiliated with a diverse representation of 16 health data sets/databases across organizational sectors. Content analysis was used to evaluate survey information and thematic analysis was used to analyze interview data.
    Main outcomes and measures: Data set experts' perceptions on what makes data sets AI ready.
    Results: Participants included 20 data set experts (11 [55%] men; mean [SD] age, 42 [11] years), of whom all were health data set creators, and 18 of the 20 were also ML researchers. Themes (3 main and 11 subthemes) were identified and integrated into an AI-readiness framework to show their association within the health data ecosystem. Participants partially determined the AI readiness of data sets using priority appraisal elements of accuracy, completeness, consistency, and fitness. Ethical acquisition and societal impact emerged as appraisal considerations in that participant samples have not been described to date in prior data quality frameworks. Factors that drive creation of high-quality health data sets and mitigate risks associated with data reuse in ML research were also relevant to AI readiness. The state of data availability, data quality standards, documentation, team science, and incentivization were associated with elements of AI readiness and the overall perception of data set usefulness.
    Conclusions and relevance: In this qualitative study of data set experts, participants contributed to the development of a grounded framework for AI data set quality. Data set AI readiness required the concerted appraisal of many elements and the balancing of transparency and ethical reflection against pragmatic constraints. The movement toward more reliable, relevant, and ethical AI and ML applications for patient care will inevitably require strategic updates to data set creation practices.
    MeSH term(s) Adult ; Female ; Humans ; Male ; Artificial Intelligence ; Delivery of Health Care ; Machine Learning ; Qualitative Research
    Language English
    Publishing date 2023-12-01
    Publishing country United States
    Document type Journal Article
    ISSN 2574-3805
    ISSN (online) 2574-3805
    DOI 10.1001/jamanetworkopen.2023.45892
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article ; Online: Predicting premature discontinuation of medication for opioid use disorder from electronic medical records.

    Lopez, Ivan / Fouladvand, Sajjad / Kollins, Scott / Chen, Chwen-Yuen Angie / Bertz, Jeremiah / Hernandez-Boussard, Tina / Lembke, Anna / Humphreys, Keith / Miner, Adam S / Chen, Jonathan H

    AMIA ... Annual Symposium proceedings. AMIA Symposium

    2024  Volume 2023, Page(s) 1067–1076

    Abstract: Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict ... ...

    Abstract Medications such as buprenorphine-naloxone are among the most effective treatments for opioid use disorder, but limited retention in treatment limits long-term outcomes. In this study, we assess the feasibility of a machine learning model to predict retention vs. attrition in medication for opioid use disorder (MOUD) treatment using electronic medical record data including concepts extracted from clinical notes. A logistic regression classifier was trained on 374 MOUD treatments with 68% resulting in potential attrition. On a held-out test set of 157 events, the full model achieved an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% CI: 0.64-0.90) and AUROC of 0.74 (95% CI: 0.62-0.87) with a limited model using only structured EMR data. Risk prediction for opioid MOUD retention vs. attrition is feasible given electronic medical record data, even without necessarily incorporating concepts extracted from clinical notes.
    MeSH term(s) Humans ; Electronic Health Records ; Area Under Curve ; Machine Learning ; Opioid-Related Disorders/drug therapy ; ROC Curve ; Analgesics, Opioid/therapeutic use
    Chemical Substances Analgesics, Opioid
    Language English
    Publishing date 2024-01-11
    Publishing country United States
    Document type Journal Article
    ISSN 1942-597X
    ISSN (online) 1942-597X
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Article ; Online: Examining the Examiners: How Medical Death Investigators Describe Suicidal, Homicidal, and Accidental Death.

    Miner, Adam S / Markowitz, David M / Peterson, Brian L / Weston, Benjamin W

    Health communication

    2020  Volume 37, Issue 4, Page(s) 467–475

    Abstract: This study describes differences in medicolegal death investigators' written descriptions for people who died by homicide, suicide, or accident. We evaluated 17 years of death descriptions from a midsized metropolitan midwestern county in the United ... ...

    Abstract This study describes differences in medicolegal death investigators' written descriptions for people who died by homicide, suicide, or accident. We evaluated 17 years of death descriptions from a midsized metropolitan midwestern county in the United States to assess how death investigators psychologically respond to different manners of death (
    MeSH term(s) Accidents ; Cause of Death ; Homicide ; Humans ; Retrospective Studies ; Suicidal Ideation ; Suicide ; United States/epidemiology
    Language English
    Publishing date 2020-12-01
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1038723-7
    ISSN 1532-7027 ; 1041-0236
    ISSN (online) 1532-7027
    ISSN 1041-0236
    DOI 10.1080/10410236.2020.1851862
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 2769: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  5. Article ; Online: Chatbots in the fight against the COVID-19 pandemic

    Miner, Adam S. / Laranjo, Liliana / Kocaballi, A. Baki

    npj Digital Medicine

    2020  Volume 3, Issue 1

    Keywords covid19
    Language English
    Publisher Springer Science and Business Media LLC
    Publishing country us
    Document type Article ; Online
    ISSN 2398-6352
    DOI 10.1038/s41746-020-0280-0
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Article: Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot.

    Ho, Annabell / Hancock, Jeff / Miner, Adam S

    The Journal of communication

    2018  Volume 68, Issue 4, Page(s) 712–733

    Abstract: Disclosing personal information to another person has beneficial emotional, relational, and psychological outcomes. When disclosers believe they are interacting with a computer instead of another person, such as a chatbot that can simulate human-to-human ...

    Abstract Disclosing personal information to another person has beneficial emotional, relational, and psychological outcomes. When disclosers believe they are interacting with a computer instead of another person, such as a chatbot that can simulate human-to-human conversation, outcomes may be undermined, enhanced, or equivalent. Our experiment examined downstream effects after emotional versus factual disclosures in conversations with a supposed chatbot or person. The effects of emotional disclosure were equivalent whether participants thought they were disclosing to a chatbot or to a person. This study advances current understanding of disclosure and whether its impact is altered by technology, providing support for media equivalency as a primary mechanism for the consequences of disclosing to a chatbot.
    Language English
    Publishing date 2018-05-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 3010-7
    ISSN 1460-2466 ; 0021-9916
    ISSN (online) 1460-2466
    ISSN 0021-9916
    DOI 10.1093/joc/jqy026
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.B 941: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 242: Show issues

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  8. Book ; Online: Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support

    Sharma, Ashish / Lin, Inna W. / Miner, Adam S. / Atkins, David C. / Althoff, Tim

    2022  

    Abstract: Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text. However, such Human-AI collaboration poses challenges for more ...

    Abstract Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text. However, such Human-AI collaboration poses challenges for more complex, creative tasks, such as carrying out empathic conversations, due to difficulties of AI systems in understanding complex human emotions and the open-ended nature of these tasks. Here, we focus on peer-to-peer mental health support, a setting in which empathy is critical for success, and examine how AI can collaborate with humans to facilitate peer empathy during textual, online supportive conversations. We develop Hailey, an AI-in-the-loop agent that provides just-in-time feedback to help participants who provide support (peer supporters) respond more empathically to those seeking help (support seekers). We evaluate Hailey in a non-clinical randomized controlled trial with real-world peer supporters on TalkLife (N=300), a large online peer-to-peer support platform. We show that our Human-AI collaboration approach leads to a 19.60% increase in conversational empathy between peers overall. Furthermore, we find a larger 38.88% increase in empathy within the subsample of peer supporters who self-identify as experiencing difficulty providing support. We systematically analyze the Human-AI collaboration patterns and find that peer supporters are able to use the AI feedback both directly and indirectly without becoming overly reliant on AI while reporting improved self-efficacy post-feedback. Our findings demonstrate the potential of feedback-driven, AI-in-the-loop writing systems to empower humans in open-ended, social, creative tasks such as empathic conversations.
    Keywords Computer Science - Computation and Language ; Computer Science - Human-Computer Interaction ; Computer Science - Social and Information Networks
    Subject code 303
    Publishing date 2022-03-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  9. Book ; Online: Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction

    Sharma, Ashish / Rushton, Kevin / Lin, Inna Wanyin / Wadden, David / Lucas, Khendra G. / Miner, Adam S. / Nguyen, Theresa / Althoff, Tim

    2023  

    Abstract: A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful "reframed thought." Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental ... ...

    Abstract A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful "reframed thought." Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental health stigma commonly limit people's access to therapy. In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts. Based on psychology literature, we define a framework of seven linguistic attributes that can be used to reframe a thought. We develop automated metrics to measure these attributes and validate them with expert judgements from mental health practitioners. We collect a dataset of 600 situations, thoughts and reframes from practitioners and use it to train a retrieval-enhanced in-context learning model that effectively generates reframed thoughts and controls their linguistic attributes. To investigate what constitutes a "high-quality" reframe, we conduct an IRB-approved randomized field study on a large mental health website with over 2,000 participants. Amongst other findings, we show that people prefer highly empathic or specific reframes, as opposed to reframes that are overly positive. Our findings provide key implications for the use of LMs to assist people in overcoming negative thoughts.

    Comment: Accepted for publication at ACL 2023
    Keywords Computer Science - Computation and Language ; Computer Science - Human-Computer Interaction ; Computer Science - Social and Information Networks
    Subject code 121
    Publishing date 2023-05-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  10. Article ; Online: Human-Machine Collaboration in Cancer and Beyond: The Centaur Care Model.

    Goldstein, Ian M / Lawrence, Julie / Miner, Adam S

    JAMA oncology

    2017  Volume 3, Issue 10, Page(s) 1303–1304

    MeSH term(s) Adult ; Algorithms ; Delivery of Health Care/methods ; Humans ; Models, Theoretical ; Neoplasms/diagnosis ; Organizational Innovation ; Physician's Role ; Point-of-Care Systems ; Software
    Language English
    Publishing date 2017-09-01
    Publishing country United States
    Document type Journal Article
    ISSN 2374-2445
    ISSN (online) 2374-2445
    DOI 10.1001/jamaoncol.2016.6413
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top