scholarly journals Relationship Between Plaque Score and Cariogram Risk Prediction Parameters among Patients Visiting A Dental Institution: Retrospective Study

2020 ◽  
pp. 1049-1053
Author(s):  
Raj SS ◽  
Keyword(s):  
Retrospective Study ◽  
Risk Prediction ◽  
Plaque Score ◽  
Prediction Parameters

scholarly journals Retrospective Study on the Seasonal Forecast-Based Disease Intervention of the Wheat Blast Outbreaks in Bangladesh

2020 ◽  
Vol 11 ◽  
Author(s):  
Kwang-Hyung Kim ◽  
Eu Ddeum Choi
Keyword(s):  
Retrospective Study ◽  
Risk Prediction ◽  
Early Warning ◽  
Early Warning System ◽  
Disease Risk ◽  
National Level ◽  
Warning System ◽  
Seasonal Forecast ◽  
Seasonal Forecasts ◽  
Wheat Blast

Seasonal disease risk prediction using disease epidemiological models and seasonal forecasts has been actively sought over the last decades, as it has been believed to be a key component in the disease early warning system for the pre-season planning of local or national level disease control. We conducted a retrospective study using the wheat blast outbreaks in Bangladesh, which occurred for the first time in Asia in 2016, to study a what-if scenario that if there was seasonal disease risk prediction at that time, the epidemics could be prevented or reduced through prediction-based interventions. Two factors govern the answer: the seasonal disease risk prediction is accurate enough to use, and there are effective and realistic control measures to be used upon the prediction. In this study, we focused on the former. To simulate the wheat blast risk and wheat yield in the target region, a high-resolution climate reanalysis product and spatiotemporally downscaled seasonal climate forecasts from eight global climate models were used as inputs for both models. The calibrated wheat blast model successfully simulated the spatial pattern of disease epidemics during the 2014–2018 seasons and was subsequently used to generate seasonal wheat blast risk prediction before each winter season starts. The predictability of the resulting predictions was evaluated against observation-based model simulations. The potential value of utilizing the seasonal wheat blast risk prediction was examined by comparing actual yields resulting from the risk-averse (proactive) and risk-disregarding (conservative) decisions. Overall, our results from this retrospective study showed the feasibility of seasonal forecast-based early warning system for the pre-season strategic interventions of forecasted wheat blast in Bangladesh.

scholarly journals The HOSPITAL score and LACE index for predicting the risk of hospital readmission do not show sex discrimination in a retrospective study

2020 ◽  
Author(s):  
Robert Robinson
Keyword(s):  
Decision Making ◽  
Retrospective Study ◽  
Risk Prediction ◽  
Hospital Readmission ◽  
Medical Center ◽  
Hospital Readmissions ◽  
Sex Discrimination ◽  
Medical Patients ◽  
Rapid Improvement ◽  
Prediction Tools

AbstractIntroductionThe perceived absence of human implicit and explicit biases, scalability, and the potential for rapid improvement with algorithmic decision-making systems make compelling arguments for the widespread use of this technology. Unfortunately, real-world performance of some algorithmic decision-making systems demonstrates the reinforcement of discriminatory human biases in a way that is hidden from the human user. This study aims to retrospectively investigate if the widely used HOSPITAL score and LACE index used to predict hospital readmissions exhibit bias on the basis of sex.Materials and MethodsAll adult medical patients discharged from the SIU-School of Medicine (SIU-SOM) Hospitalist service from Memorial Medical Center from January 1, 2015, to January 1, 2017, were studied retrospectively to determine if patient sex had an influence on the ability of the HOSPTIAL score and LACE index to predict the likelihood of any cause hospital readmission within 30 days. Receiver operating characteristic (ROC) curves were constructed comparing risk prediction tool performance by sex by measuring the area under the curve (AUC).ResultsThe analysis includes data for the 1781 discharges for 1410 individual patients that met inclusion criteria. Of these discharges, 456 (27%) were readmitted to the same hospital within 30 days. The overall study population was 47% women, had an average age of 63 years and spent an average of 7.9 days in the hospital. Comparison of the performance of the LACE index in women and men showed no differences between AUCs (0.565 and 0.578, p = 0.613) and an ABROCA of 0.013. Sensitivity (67% and 70%), specificity (46% and 46%), PPV (30% and 31%), NPV (80% and 82%) and accuracy (51% and 52%) for the LACE index are very similar for women and men.Comparison of the performance of the HOSPITAL in women and men showed no differences between AUCs (0.56 and 0.58, p = 0.407) and an ABROCA of 0.008 indicating highly similar performance. Sensitivity (16% and 21%), specificity (96% and 95%), PPV (59% and 57%), NPV (77% and 78%) and accuracy (76% and 76%) for the HOSPITAL score are very similar for women and men.Discussion and ConclusionsThe performance of the HOSPITAL and LACE readmission risk prediction tools appears to have equivalent performance when used for women or men in this small, single-center, retrospective study. Further research is needed to explore the potential of bias and discrimination on risk prediction tools used in healthcare.

scholarly journals Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study (Preprint)

2019 ◽  
Author(s):  
Junetae Kim ◽  
Yu Rang Park ◽  
Jeong Hoon Lee ◽  
Jae-Ho Lee ◽  
Young-Hak Kim ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Intensive Care ◽  
Deep Learning ◽  
Retrospective Study ◽  
Prediction Model ◽  
Real Time ◽  
Risk Prediction ◽  
Time Dependent ◽  
Time Prediction ◽  
Deep Learning Model

BACKGROUND Cardiac arrest is the most serious death-related event in intensive care units (ICUs), but it is not easily predicted because of the complex and time-dependent data characteristics of intensive care patients. Given the complexity and time dependence of ICU data, deep learning–based methods are expected to provide a good foundation for developing risk prediction models based on large clinical records. OBJECTIVE This study aimed to implement a deep learning model that estimates the distribution of cardiac arrest risk probability over time based on clinical data and assesses its potential. METHODS A retrospective study of 759 ICU patients was conducted between January 2013 and July 2015. A character-level gated recurrent unit with a Weibull distribution algorithm was used to develop a real-time prediction model. Fivefold cross-validation testing (training set: 80% and validation set: 20%) determined the consistency of model accuracy. The time-dependent area under the curve (TAUC) was analyzed based on the aggregation of 5 validation sets. RESULTS The TAUCs of the implemented model were 0.963, 0.942, 0.917, 0.875, 0.850, 0.842, and 0.761 before cardiac arrest at 1, 8, 16, 24, 32, 40, and 48 hours, respectively. The sensitivity was between 0.846 and 0.909, and specificity was between 0.923 and 0.946. The distribution of risk between the cardiac arrest group and the non–cardiac arrest group was generally different, and the difference rapidly increased as the time left until cardiac arrest reduced. CONCLUSIONS A deep learning model for forecasting cardiac arrest was implemented and tested by considering the cumulative and fluctuating effects of time-dependent clinical data gathered from a large medical center. This real-time prediction model is expected to improve patient’s care by allowing early intervention in patients at high risk of unexpected cardiac arrests.

Sign in / Sign up

Export Citation Format

Share Document