Download the Free Prime PubMed App to your smartphone or tablet.

Available for iPhone or iPad:

Unbound PubMed app for iOS iPhone iPadAlso Available:
Unbound PubMed app for Android

Available for Mac and Windows Desktops and laptops:

Unbound PubMed app for WindowsUnbound PubMed app for MAC OS Yosemite Macbook Air pro
(Nursing Research[TA])
3,941 results
  • Mortality Risk in Homebound Older Adults Predicted from Routinely Collected Nursing Data. [Journal Article]
  • NRNurs Res 2018 Dec 06
  • Sullivan SS, Hewner S, … Westra BL
  • CONCLUSIONS: The AUC and 95% CI for the decision tree are slightly less accurate than logistic regression and ANN; however, decision tree was more accurate in detecting mortality. The OASIS data set was useful to predict 12-month mortality risk. Decision tree is an interpretable predictive model developed from routinely collected nursing data that may be incorporated into a decision support tool to identify older adults at risk for death.
  • Construct Validity of the Multi-Source Interference Task to Examine Attention in Heart Failure. [Journal Article]
  • NRNurs Res 2018 Nov/Dec; 67(6):465-472
  • Jung M, Jonides J, … Pressler SJ
  • CONCLUSIONS: Construct validity of the MSIT was supported, in part, among patients with HF. The MSIT is a sensitive measure of detecting worse directed attention among patients with HF compared with healthy adults. The preliminary findings support the use of the MSIT as a measure of directed attention in HF. Confirmation is warranted for current findings in larger samples.
  • Machine Learning Methods for Identifying Critical Data Elements in Nursing Documentation. [Journal Article]
  • NRNurs Res 2018 Aug 28
  • Bose E, Maganti S, … Monsen KA
  • CONCLUSIONS: Using mRMR as a feature selection technique, out of 206 features, 50 features were selected with scores greater than zero and generalized linear model (GLM) applied on the 50 features achieved highest accuracy of 86.2% on a held-out test set. Using glmnet as a feature selection technique and obtaining feature importance, 63 features had importance scores greater than zero and GLM applied on them achieved the highest accuracy of 95.5% on a held-out test set.Feature selection techniques show promise towards reducing PHN documentation burden by identifying the most critical data elements needed to predict risk status. Further studies to refine the process of feature selection can aid in informing public health nurses' focus on client-specific and targeted interventions in the delivery of care.
New Search Next