scholarly journals Integration of Evidence into a Detailed Clinical Model-based Electronic Nursing Record System

2012 ◽  
Vol 18 (2) ◽  
pp. 136 ◽  
Author(s):  
Hyeoun-Ae Park ◽  
Yul Ha Min ◽  
Eunjoo Jeon ◽  
Eunja Chung
Keyword(s):  
Record System ◽  
Clinical Model ◽  
Model Based ◽  
Nursing Record

Radiomics Analysis Based on CT’s greater-omental caking for Predicting Pathological Grading of Pseudomyxoma Peritonei

2021 ◽  
Author(s):  
Nan Zhou ◽  
Ruixue Dou ◽  
Xichao Zhai ◽  
Jingyang Fang ◽  
Jiajun Wang ◽  
...  
Keyword(s):  
Roc Curve ◽  
Pseudomyxoma Peritonei ◽  
Diagnostic Efficiency ◽  
Combined Model ◽  
Clinical Model ◽  
Model Based ◽  
Preoperative Serum ◽  
Imaging Observation ◽  
Omental Caking ◽  
The Impact

Abstract Purpose: The objective of this study was to predict the preoperative pathological grading and survival period of Pseudomyxoma peritonei (PMP) by establishing models, including a radiomics model with greater mental caking as the imaging observation index, a clinical model including clinical indexes, and a combination model of these two.Methods: A total of 88 PMP patients were selected. Clinical data of patients, including age, sex, preoperative serum tumor markers [CEA, CA125, and CA199], survival time, and preoperative computed tomography (CT) images were analyzed. Three models (clinical model, radiomics model and joint model) were used to predict PMP pathological grading. The models’ diagnostic efficiency was compared and analyzed by building the receiver operating characteristic (ROC) curve. Simultaneously, the impact of PMP’s different pathological grades was evaluated.Results: The results showed that the radiomics model based on the CT’s greater omental caking, an area under the ROC curve ([AUC] = 0.878), and the combined model (AUC = 0.899) had diagnostic power n for determining PMP pathological grade.Conclusion: The imaging radiomics model based on CT greater omental caking can be used to predict PMP pathological grade, which is important in the treatment selection method and prognosis assessment.

The PEN & PAD Medical Record Model: Development of a Nursing Record for Hospital-based Care of the Elderly

1994 ◽  
Vol 33 (05) ◽  
pp. 464-472 ◽  
Author(s):  
N. Hardiker ◽  
J. Kirby ◽  
R. Tallis ◽  
M. Gonsalkarale ◽  
H. A. Heathfield
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
General Practitioners ◽  
Model Development ◽  
The Elderly ◽  
Nursing Profession ◽  
Record System ◽  
Professional Background ◽  
Nursing Record ◽  
Computer Based

Abstract:The PEN & PAD Medical Record model describes a framework for an information model, designed to meet the requirements of an electronic medical record. This model has been successfully tested in a computer-based record system for General Practitioners as part of the PEN & PAD (GP) Project.Experiences of using the model for developing computer-based nursing records are reported. Results show that there are some problems with directly applying the model to the nursing domain. Whilst the main purpose of the nursing record is to document and communicate a patient’s care, it has several other, possibly incompatible, roles. Furthermore, the structure and content of the information contained within the nursing record is heavily influenced by the need for the nursing profession to visibly demonstrate the philosophical frameworks underlying their work. By providing new insights into the professional background of nursing records, this work has highlighted the need for nurses to clarify and make explicit their uses of information, and also provided them with some tools to assist in this task.

A Basic Study on Application of Voice Recognition Input to an Electronic Nursing Record System -Evaluation of the Function as an Input Interface-

2010 ◽  
Vol 36 (3) ◽  
pp. 1053-1058 ◽  
Author(s):  
Terutaka Marukami ◽  
Shoko Tani ◽  
Atsuko Matsuda ◽  
Keiko Takemoto ◽  
Akiko Shindo ◽  
...  
Keyword(s):  
Voice Recognition ◽  
System Evaluation ◽  
Basic Study ◽  
Record System ◽  
Input Interface ◽  
Nursing Record

scholarly journals Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

2020 ◽  
Author(s):  
Yae Won Park ◽  
Dongmin Choi ◽  
Mina Park ◽  
Sung Jun Ahn ◽  
Sung Soo ahn ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Combined Model ◽  
Test Set ◽  
Clinical Model ◽  
Model Based ◽  
Clinical Profiles

Abstract Background: Noninvasive identification of amyloid β (Aβ) is important in mild cognitive impairment (MCI) patients for better clinical management. This study aimed to evaluate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ 42 status when integrated with clinical and genetic profiles.Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to the training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampi were extracted from T1-weighted images of magnetic resonance imaging (MRI). A previously defined cutoff (< 192 pg/mL) was applied for CSF Aβ 42 status. After feature selection, random forest with subsampling methods were trained to predict the CSF Aβ 42 with three models: 1) a radiomics model; 2) a clinical model based on clinical and genetic profiles including demographics, APOE ε4 genotype, and neuropsychological tests; and 3) a combined model based on radiomics and clinical profiles. The prediction performance of the classifier was validated in the test set using the area under the receiver operating characteristic curve (AUC). Results: The radiomics model identified 33 radiomics features to predict CSF Aβ 42 , which showed an AUC of 0.674 in the best performing radiomics model in the test set. The clinical model identified 6 clinical features to predict CSF Aβ 42 , which showed an AUC of 0.758 in the best performing clinical model in the test set. The combined model based on radiomics and clinical profiles identified a total of 37 features (32 from radiomics and 5 from clinical features), showing an AUC of 0.823 in the best performing combined model test set, which showed the highest performance among the three models. Conclusions: Radiomics model from MRI can help predict CSF Aβ 42 status in MCI patients and potentially triage the patients for the invasive and costly Aβ test.

scholarly journals Disease Manifestations in Mucopolysaccharidoses and Their Impact on Anaesthesia-Related Complications—A Retrospective Analysis of 99 Patients

2021 ◽  
Vol 10 (16) ◽  
pp. 3518
Author(s):  
Luise Sophie Ammer ◽  
Thorsten Dohrmann ◽  
Nicole Maria Muschol ◽  
Annika Lang ◽  
Sandra Rafaela Breyer ◽  
...  
Keyword(s):  
Clinical Manifestations ◽  
Retrospective Chart Review ◽  
Haematopoietic Stem Cell ◽  
Major Surgery ◽  
High Morbidity ◽  
Lasso Regression ◽  
Forecast Performance ◽  
Clinical Model ◽  
Surgical Interventions ◽  
Model Based

Patients with mucopolysaccharidoses (MPS) frequently require anaesthesia for diagnostic or surgical interventions and thereby experience high morbidity. This study aimed to develop a multivariable prediction model for anaesthesia-related complications in MPS. This two-centred study was performed by retrospective chart review of children and adults with MPS undergoing anaesthesia from 2002 until 2018. We retrieved the patients’ demographics, medical history, clinical manifestations, and indication by each anaesthesia. Multivariable mixed-effects logistic regression was calculated for a clinical model based on preoperative predictors preselected by lasso regression and another model based on disease subtypes only. Of the 484 anaesthesia cases in 99 patients, 22.7% experienced at least one adverse event. The clinical model resulted in a better forecast performance than the subtype-model (AICc 460.4 vs. 467.7). The most relevant predictors were hepatosplenomegaly (OR 3.10, CI 1.54–6.26), immobility (OR 3.80, CI 0.98–14.73), and planned major surgery (OR 6.64, CI 2.25–19.55), while disease-specific therapies, i.e., haematopoietic stem cell transplantation (OR 0.45, CI 0.20–1.03), produced a protective effect. Anaesthetic complications can best be predicted by surrogates for advanced disease stages and protective therapeutic factors. Further model validation in different cohorts is needed.

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