Objectives: To evaluate the predictive validity of an adapted version of the Minimum Data Set (MDS) Mortality Risk Index-Revised (MMRI-R) based on MDS version 3.0 assessment items (MMRI-v3) and to compare the predictive validity of the MMRI-v3 with that of a single MDS item indicating limited life expectancy (LLE).
Design: Retrospective, cross-sectional study of MDS assessments. Other data sources included the Veterans Affairs (VA) Residential History File and Vital Status File.
Setting: VA nursing homes (NHs).
Participants: Veterans aged 65 and older newly admitted to VA NHs between July 1, 2012, and September 30, 2015.
Measurements: The dependent variable was death within 6 months of admission date. Independent variables included MDS items used to calculate MMRI-v3 scores (renal failure, chronic heart failure, sex, age, dehydration, cancer, unintentional weight loss, shortness of breath, activity of daily living scale, poor appetite, acute change in mental status) and the MDS item indicating LLE.
Results: The predictive ability of the MMRI-v3 for 6-month mortality (c-statistic 0.81) is as good as that of the original MMRI-R (c-statistic 0.76). Scores generated using the MMRI-v3 had greater predictive ability than that of the single MDS indicator for LLE (c-statistic 0.76); using the 2 together resulted in greater predictive ability (c-statistic 0.86).
Conclusion: The MMRI-v3 is a useful tool in research and clinical practice that accurately predicts 6-month mortality in veterans residing in Veterans Affairs NHs. Identification of residents with LLE has great utility for studying palliative care interventions and may be helpful in guiding allocation of these services in clinical practice. J Am Geriatr Soc 66:2353-2359, 2018.
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http://dx.doi.org/10.1111/jgs.15579 | DOI Listing |
Sci Rep
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Department of Civil Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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Department of Physics and Astronomy, Purdue University, West Lafayette, IN, USA.
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December 2024
Macao Institute of Materials Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macau SAR 999078, China.
The powerful data processing and pattern recognition capabilities of machine learning (ML) technology have provided technical support for the innovation in computational chemistry. Compared with traditional ML and deep learning (DL) techniques, transformers possess fine-grained feature-capturing abilities, which are able to efficiently and accurately model the dependencies of long-sequence data, simulate complex and diverse chemical spaces, and explore the computational logic behind the data. In this Perspective, we provide an overview of the application of transformer models in computational chemistry.
View Article and Find Full Text PDFEcol Lett
January 2025
School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
Ecosystem models are often used to predict the consequences of management interventions in applied ecology and conservation. These models are often high-dimensional and nonlinear, yet limited data are available to calibrate or validate them. Consequently, their utility as decision-support tools is unclear.
View Article and Find Full Text PDFFront Comput Neurosci
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School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
This study aims to enhance the classification accuracy of adverse events associated with the da Vinci surgical robot through advanced natural language processing techniques, thereby ensuring medical device safety and protecting patient health. Addressing the issues of incomplete and inconsistent adverse event records, we employed a deep learning model that combines BERT and BiLSTM to predict whether adverse event reports resulted in patient harm. We developed the Bert-BiLSTM-Att_dropout model specifically for text classification tasks with small datasets, optimizing the model's generalization ability and key information capture through the integration of dropout and attention mechanisms.
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