LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 2 of total 2

Search options

  1. Article ; Online: Developing a machine learning-based short form of the positive and negative syndrome scale.

    Lin, Gong-Hong / Liu, Jen-Hsuan / Lee, Shih-Chieh / Wu, Bo-Jian / Li, Shu-Qi / Chiu, Hsien-Jane / Wang, San-Ping / Hsieh, Ching-Lin

    Asian journal of psychiatry

    2024  Volume 94, Page(s) 103965

    Abstract: Background and hypothesis: The Positive and Negative Syndrome Scale (PANSS) consists of 30 items and takes up to 50 minutes to administer and score. Therefore, this study aimed to develop and validate a machine learning-based short form of the PANSS ( ... ...

    Abstract Background and hypothesis: The Positive and Negative Syndrome Scale (PANSS) consists of 30 items and takes up to 50 minutes to administer and score. Therefore, this study aimed to develop and validate a machine learning-based short form of the PANSS (PANSS-MLSF) that reproduces the PANSS scores. Moreover, the PANSS-MLSF estimated the removed-item scores.
    Study design: The PANSS-MLSF was developed using an artificial neural network, and the removed-item scores were estimated using the eXtreme Gradient Boosting classifier algorithm. The reliability of the PANSS-MLSF was examined using Cronbach's alpha. The concurrent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the PANSS. The convergent validity was examined by the association (Pearson's r) between the PANSS-MLSF and the Clinical Global Impression-Severity, Mini-Mental State Examination, and Lawton Instrumental Activities of Daily Living Scale. The agreement of the estimated removed-item scores with their original scores was examined using Cohen's kappa.
    Study results: Our analysis included data from 573 patients with moderate severity. The two versions of the PANSS-MLSF comprised 15 items and 9 items were proposed. The PANSS-MLSF scores were similar to the PANSS scores (mean squared error=2.6-24.4 points). The reliability, concurrent validity, and convergent validity of the PANSS-MLSF were good. Moderate to good agreement between the estimated removed-item scores and the original item scores was found in 60% of the removed items.
    Conclusion: The PANSS-MLSF offers a viable way to reduce PANSS administration time, maintain score comparability, uphold reliability and validity, and even estimate scores for the removed items.
    MeSH term(s) Humans ; Activities of Daily Living ; Reproducibility of Results ; Psychometrics
    Language English
    Publishing date 2024-02-12
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2456678-0
    ISSN 1876-2026 ; 1876-2018
    ISSN (online) 1876-2026
    ISSN 1876-2018
    DOI 10.1016/j.ajp.2024.103965
    Database MEDical Literature Analysis and Retrieval System OnLINE

    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.

  2. Article ; Online: Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.

    Liu, Jen-Hsuan / Shih, Chih-Yuan / Huang, Hsien-Liang / Peng, Jen-Kuei / Cheng, Shao-Yi / Tsai, Jaw-Shiun / Lai, Feipei

    Journal of medical Internet research

    2023  Volume 25, Page(s) e47366

    Abstract: Background: An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and ... ...

    Abstract Background: An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life.
    Objective: This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings.
    Methods: This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F
    Results: From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F
    Conclusions: We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care.
    Trial registration: ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.
    MeSH term(s) Humans ; Artificial Intelligence ; Cohort Studies ; Death ; Machine Learning ; Neoplasms/therapy ; Outpatients ; Prospective Studies ; Terminal Care ; Wearable Electronic Devices
    Language English
    Publishing date 2023-08-18
    Publishing country Canada
    Document type Clinical Trial ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2028830-X
    ISSN 1438-8871 ; 1438-8871
    ISSN (online) 1438-8871
    ISSN 1438-8871
    DOI 10.2196/47366
    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.

To top