Detalhe da pesquisa
1.
Machine learning models to detect social distress, spiritual pain, and severe physical psychological symptoms in terminally ill patients with cancer from unstructured text data in electronic medical records.
Palliat Med
; 36(8): 1207-1216, 2022 09.
Artigo
em Inglês
| MEDLINE | ID: mdl-35773973
2.
Research Types and New Trends on the Omaha System Published From 2012 to 2019: A Scoping Review.
Comput Inform Nurs
; 40(8): 531-537, 2022 Aug 01.
Artigo
em Inglês
| MEDLINE | ID: mdl-35929744
3.
Automatic Classification of Electronic Nursing Narrative Records Based on Japanese Standard Terminology for Nursing.
Comput Inform Nurs
; 39(11): 828-834, 2021 05 12.
Artigo
em Inglês
| MEDLINE | ID: mdl-33990502
4.
Highly accurate and explainable detection of specimen mix-up using a machine learning model.
Clin Chem Lab Med
; 58(3): 375-383, 2020 02 25.
Artigo
em Inglês
| MEDLINE | ID: mdl-32031970
5.
Evaluating the Effectiveness of a Fall Risk Screening Tool Implemented in an Electronic Medical Record System.
J Nurs Care Qual
; 33(4): E1-E6, 2018.
Artigo
em Inglês
| MEDLINE | ID: mdl-29271833
6.
Establishing a Classification System for High Fall-Risk Among Inpatients Using Support Vector Machines.
Comput Inform Nurs
; 35(8): 408-416, 2017 Aug.
Artigo
em Inglês
| MEDLINE | ID: mdl-28800580
7.
Mapping Injection Order Messages to Health Level 7 Fast Healthcare Interoperability Resources to Collate Infusion Pump Data.
Appl Clin Inform
; 15(1): 1-9, 2024 01.
Artigo
em Inglês
| MEDLINE | ID: mdl-38171359
8.
Causality Assessment Between Drugs and Fatal Cerebral Haemorrhage Using Electronic Medical Records: Comparative Evaluation of Disease-Specific and Conventional Methods.
Drugs Real World Outcomes
; 2024 Feb 06.
Artigo
em Inglês
| MEDLINE | ID: mdl-38321346
9.
Relationship between research activities and individual factors among Japanese nursing researchers during the COVID-19 pandemic.
PLoS One
; 17(8): e0271001, 2022.
Artigo
em Inglês
| MEDLINE | ID: mdl-36001598
10.
Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan.
Int J Nurs Stud
; 119: 103932, 2021 Jul.
Artigo
em Inglês
| MEDLINE | ID: mdl-33975074
11.
Can Staff Distinguish Falls: Experimental Hypothesis Verification Using Japanese Incident Reports and Natural Language Processing.
Stud Health Technol Inform
; 250: 159-163, 2018.
Artigo
em Inglês
| MEDLINE | ID: mdl-29857420
12.
Construction and evaluation of FiND, a fall risk prediction model of inpatients from nursing data.
Jpn J Nurs Sci
; 13(2): 247-55, 2016 Apr.
Artigo
em Inglês
| MEDLINE | ID: mdl-27040735
13.
Evaluation of a Fall Risk Prediction Tool Using Large-Scale Data.
Stud Health Technol Inform
; 225: 800-1, 2016.
Artigo
em Inglês
| MEDLINE | ID: mdl-27332348