scholarly journals Artificial neural network approach for acute poisoning mortality prediction in emergency departments

2021 ◽  
Vol 8 (3) ◽  
pp. 229-236
Author(s):  
Seon Yeong Park ◽  
Kisung Kim ◽  
Seon Hee Woo ◽  
Jung Taek Park ◽  
Sikyoung Jeong ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Acute Poisoning ◽  
Mortality Prediction ◽  
Ann Model ◽  
Operating Characteristic Curve

Objective The number of deaths due to acute poisoning (AP) is on the increase. It is crucial to predict AP patient mortality to identify those requiring intensive care for providing appropriate patient care as well as preserving medical resources. The aim of this study is to predict the risk of in-hospital mortality associated with AP using an artificial neural network (ANN) model.Methods In this multicenter retrospective study, ANN and logistic regression models were constructed using the clinical and laboratory data of 1,304 patients seeking emergency treatment for AP. The ANN model was first trained on 912/1,304 (70%) randomly selected patients and then tested on the remaining 392/1,304 (30%). Receiver operating characteristic curve analysis was used to evaluate the mortality prediction of the two models.Results Age, endotracheal intubation status, and intensive care unit admission were significant predictors of mortality in patients with AP in the multivariate logistic regression model. The ANN model indicated age, Glasgow Coma Scale, intensive care unit admission, and endotracheal intubation status were critical factors among the 12 independent variables related to in-hospital mortality. The area under the receiver operating characteristic curve for mortality prediction was significantly higher in the ANN model compared to the logistic regression model.Conclusion This study establishes that the ANN model could be a valuable tool for predicting the risk of death following AP. Thus, it may facilitate effective patient triage and improve the outcomes.

scholarly journals Semiquantitative Cough Strength Score for Predicting Reintubation After Planned Extubation

2015 ◽  
Vol 24 (6) ◽  
pp. e86-e90 ◽  
Author(s):  
Jun Duan ◽  
Lintong Zhou ◽  
Meiling Xiao ◽  
Jinhua Liu ◽  
Xiangmei Yang
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Spontaneous Breathing ◽  
Operating Characteristic ◽  
Peak Flow ◽  
Characteristic Curve ◽  
Spontaneous Breathing Trial ◽  
Extubation Outcome ◽  
Cough Strength ◽  
Operating Characteristic Curve

Background Semiquantitative cough strength score (SCSS, graded 0–5) and cough peak flow (CPF) have been used to predict extubation outcome in patients in whom extubation is planned; however, the correlation of the 2 assessments is unclear. Methods In the intensive care unit of a university-affiliated hospital, 186 patients who were ready for extubation after a successful spontaneous breathing trial were enrolled in the study. Both SCSS and CPF were assessed before extubation. Reintubation was recorded 72 hours after extubation. Results Reintubation rate was 15.1% within 72 hours after planned extubation. Patients in whom extubation was successful had higher SCSSs than did reintubated patients (mean [SD], 3.2 [1.6] vs 2.2 [1.6], P = .002) and CPF (74.3 [40.0] vs 51.7 [29.4] L/min, P = .005). The SCSS showed a positive correlation with CPF (r = 0.69, P < .001). Mean CPFs were 38.36 L/min, 39.51 L/min, 44.67 L/min, 57.54 L/min, 78.96 L/min, and 113.69 L/min in patients with SCSSs of 0, 1, 2, 3, 4, and 5, respectively. The discriminatory power for reintubation, evidenced by area under the receiver operating characteristic curve, was similar: 0.677 for SCSS and 0.678 for CPF (P = .97). As SCSS increased (from 0 to 1 to 2 to 3 to 4 to 5), the reintubation rate decreased (from 29.4% to 25.0% to 19.4% to 16.1% to 13.2% to 4.1%). Conclusions SCSS was convenient to measure at the bedside. It was positively correlated with CPF and had the same accuracy for predicting reintubation after planned extubation.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

Predictors of thermal increase in magnetic resonance–guided focused ultrasound treatment for essential tremor: histogram analysis of skull density ratio values for 1024 elements

2021 ◽  
pp. 1-6
Author(s):  
Ken Iijima ◽  
Hajime Yokota ◽  
Toshio Yamaguchi ◽  
Masayuki Nakano ◽  
Takahiro Ouchi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Operating Characteristic ◽  
Focused Ultrasound ◽  
Characteristic Curve ◽  
Density Ratio ◽  
Predictive Ability ◽  
Curve Analysis ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Operating Characteristic Curve

OBJECTIVE Sufficient thermal increase capable of generating thermocoagulation is indispensable for an effective clinical outcome in patients undergoing magnetic resonance–guided focused ultrasound (MRgFUS). The skull density ratio (SDR) is one of the most dominant predictors of thermal increase prior to treatment. However, users currently rely only on the average SDR value (SDRmean) as a screening criterion, although some patients with low SDRmean values can achieve sufficient thermal increase. The present study aimed to examine the numerical distribution of SDR values across 1024 elements to identify more precise predictors of thermal increase during MRgFUS. METHODS The authors retrospectively analyzed the correlations between the skull parameters and the maximum temperature achieved during unilateral ventral intermediate nucleus thalamotomy with MRgFUS in a cohort of 55 patients. In addition, the numerical distribution of SDR values was quantified across 1024 elements by using the skewness, kurtosis, entropy, and uniformity of the SDR histogram. Next, the authors evaluated the correlation between the aforementioned indices and a peak temperature > 55°C by using univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis was performed to compare the predictive ability of the indices. The diagnostic performance of significant factors was also assessed. RESULTS The SDR skewness (SDRskewness) was identified as a significant predictor of thermal increase in the univariate and multivariate logistic regression analyses (p < 0.001, p = 0.013). Moreover, the receiver operating characteristic curve analysis indicated that the SDRskewness exhibited a better predictive ability than the SDRmean, with area under the curve values of 0.847 and 0.784, respectively. CONCLUSIONS The SDRskewness is a more accurate predictor of thermal increase than the conventional SDRmean. The authors suggest setting the SDRskewness cutoff value to 0.68. SDRskewness may allow for the inclusion of treatable patients with essential tremor who would have been screened out based on the SDRmean exclusion criterion.

scholarly journals Evaluation of Nottingham Hip Fracture Score, Age-Adjusted Charlson Comorbidity Index and the Physiological and Operative Severity Score for the enumeration of Mortality and morbidity as predictors of mortality in elderly neck of femur fracture patients

2020 ◽  
Vol 8 ◽  
pp. 205031212091826 ◽  
Author(s):  
Michael James Nelson ◽  
Justin Scott ◽  
Palvannan Sivalingam
Keyword(s):  
Hip Fracture ◽  
Charlson Comorbidity Index ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Mortality And Morbidity ◽  
Operative Severity Score ◽  
Significant Difference ◽  
Comorbidity Index ◽  
Operating Characteristic Curve

Background: This study evaluated the use of several risk prediction models in estimating short- and long-term mortality following hip fracture in an Australian population. Methods: Data from 195 patients were retrospectively analysed and applied to three models of interest: the Nottingham Hip Fracture Score, the Age-Adjusted Charlson Comorbidity Index and the Physiological and Operative Severity Score for enUmeration of Mortality and Morbidity. The performance of these models was assessed with receiver operating characteristic curve as well as logistic regression modelling. Results: The median age of participants was 83 years and 69% were women. Ten percent of patients were deceased by 30 days, 25% at 6 months and 31% at 12 months post-operatively. While there was no statistically significant difference between the models, the Age-Adjusted Charlson Comorbidity Index had the largest area under the receiver operating characteristic curve for within 30 day and 12 month mortality, while the Nottingham Hip Fracture Score was largest for 6-month mortality. There was no evidence to suggest that the models were selecting a specific subgroup of our population, therefore, no indication was present to suggest that using multiple models would improve mortality prediction. Conclusions: While there was no statistically significant difference in mortality prediction, the Nottingham Hip Fracture Score is perhaps the best suited clinically, due to its ease of implementation. Larger prospective data collection across a variety of sites and its role in guiding clinical management remains an area of interest.

scholarly journals A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation

2018 ◽  
Vol 26 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Muhammad Faisal ◽  
Andy Scally ◽  
Robin Howes ◽  
Kevin Beatson ◽  
Donald Richardson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Operating Characteristic Curve

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients’ first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital ( n = 24,696) and compared the performance of these models in data from another hospital ( n = 13,477). We used two performance measures – the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well – calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting.

scholarly journals Evaluation of N/LP Ratio as a Predictor of Disease Progression and Mortality in COVID-19 Patients Admitted to the Intensive Care Unit

2021 ◽  
Author(s):  
Başak Çakır Güney ◽  
Mert Hayıroğlu ◽  
Didar Şenocak ◽  
Vedat Çiçek ◽  
Tufan Çınar ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Hospital Death ◽  
Survival Status ◽  
Inflammatory Index ◽  
Icu Stay

Objective: This research aimed to evaluate whether the neutrophil to lymphocyte and platelet (N/LP) ratio may be used to predict the risk of admission to the intensive care unit (ICU), the need for mechanical ventilation and in-hospital mortality in Coronavirus disease 2019 (COVID-19) cases. Methods: The study was conducted retrospectively on the data of 134 COVID-19 patients who were admitted to the ICU. The N/LP ratio was calculated as follows: neutrophil count x 100 / (lymphocyte count x platelet count). Each member of the research cohort was categorised into 1 of 2 groups based on their survival status (survivor and non-survivor groups). Results: In total, 82 (61%) patients died during the ICU stay. Patients who required mechanical ventilation and died in the ICU stay had significantly higher N/LP ratio than those who did not require it and survived [10 (IQR=4.94-19.38) vs 2.51 (IQR=1.67-5.49), p<0.001] and [11.27 (IQR=4.53-30.02) vs 1.65 (IQR=1-3.24), p<0.001], respectively. The N/LP ratio was linked with the requirement of mechanical ventilation and in-hospital death according to multivariable analysis. In receiver operating characteristic curve analysis, we found that N/LP in predicting admission to the ICU was >4.18 with 61% sensitivity and 62% specificity, it was >5.07 with 74% sensitivity and 73% specificity for the need for mechanical ventilation, and >3.69 with 81% sensitivity and 81% specificity to predict in-hospital death. Conclusion: To our knowledge, this is the first study showing that the N/LP ratio, which is a novel and widely applicable inflammatory index, may be used to predict the risk of ICU admission, mechanical ventilation and in-hospital death in patients with COVID-19 disease.

scholarly journals Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

10.2196/20268 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e20268
Author(s):  
Adrienne Kline ◽  
Theresa Kline ◽  
Zahra Shakeri Hossein Abad ◽  
Joon Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Item Response ◽  
Nearest Neighbors ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Case Based

Background Supervised machine learning (ML) is being featured in the health care literature with study results frequently reported using metrics such as accuracy, sensitivity, specificity, recall, or F1 score. Although each metric provides a different perspective on the performance, they remain to be overall measures for the whole sample, discounting the uniqueness of each case or patient. Intuitively, we know that all cases are not equal, but the present evaluative approaches do not take case difficulty into account. Objective A more case-based, comprehensive approach is warranted to assess supervised ML outcomes and forms the rationale for this study. This study aims to demonstrate how the item response theory (IRT) can be used to stratify the data based on how difficult each case is to classify, independent of the outcome measure of interest (eg, accuracy). This stratification allows the evaluation of ML classifiers to take the form of a distribution rather than a single scalar value. Methods Two large, public intensive care unit data sets, Medical Information Mart for Intensive Care III and electronic intensive care unit, were used to showcase this method in predicting mortality. For each data set, a balanced sample (n=8078 and n=21,940, respectively) and an imbalanced sample (n=12,117 and n=32,910, respectively) were drawn. A 2-parameter logistic model was used to provide scores for each case. Several ML algorithms were used in the demonstration to classify cases based on their health-related features: logistic regression, linear discriminant analysis, K-nearest neighbors, decision tree, naive Bayes, and a neural network. Generalized linear mixed model analyses were used to assess the effects of case difficulty strata, ML algorithm, and the interaction between them in predicting accuracy. Results The results showed significant effects (P<.001) for case difficulty strata, ML algorithm, and their interaction in predicting accuracy and illustrated that all classifiers performed better with easier-to-classify cases and that overall the neural network performed best. Significant interactions suggest that cases that fall in the most arduous strata should be handled by logistic regression, linear discriminant analysis, decision tree, or neural network but not by naive Bayes or K-nearest neighbors. Conventional metrics for ML classification have been reported for methodological comparison. Conclusions This demonstration shows that using the IRT is a viable method for understanding the data that are provided to ML algorithms, independent of outcome measures, and highlights how well classifiers differentiate cases of varying difficulty. This method explains which features are indicative of healthy states and why. It enables end users to tailor the classifier that is appropriate to the difficulty level of the patient for personalized medicine.

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