scholarly journals Gynecological Surgery and Machine Learning: Complications and Length of Stay Prediction

2021 ◽  
Author(s):  
Oleg Metsker ◽  
Georgy Kopanitsa ◽  
Anton Malushko ◽  
Eduard Komlichenko ◽  
Katerina Bolgova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Research Centre ◽  
Average Score ◽  
Gynecological Surgery ◽  
Female Reproductive System ◽  
Saint Petersburg ◽  
National Medical ◽  
Model Training ◽  
Case Outcome

In this study we are developing predictive models for a length of stay after a gynecological surgery, complications and the length of the surgery using machine learning methods. The study was performed with the data of patients with the diseases of the female reproductive system. The patients were admitted to the Almazov National Medical Research Centre (Saint-Petersburg, Russia) within the period 2010-2020. The study included 8170 electronic medical records of inpatient episodes including 3500 operation protocols. The data included anamnesis of life, anamnesis of disease, laboratory tests, severity, outcome of a surgery, main and comorbid diagnosis, complications, case outcome. The dataset was randomly split into 70% train and 30% test datasets. Validation with the test dataset provided the following prediction metrics for the length of stay after a surgery model. Training score: AUC of ROC: 0.9582230976834093; K-fold CV average score: -8.73; MSE: 5.65; RMSE: 2.83.

Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma

2021 ◽  
Author(s):  
Pedro Vinícius Staziaki ◽  
Di Wu ◽  
Jesse C. Rayan ◽  
Irene Dixe de Oliveira Santo ◽  
Feng Nan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Clinical Parameters ◽  
Icu Admission ◽  
Ct Findings ◽  
Torso Trauma

scholarly journals Machine learning prediction of breast cancer survival using age, sex, length of stay, mode of diagnosis and location of cancer

2021 ◽  
Author(s):  
Hilary I. Okagbue ◽  
Patience I. Adamu ◽  
Pelumi E. Oguntunde ◽  
Emmanuela C. M. Obasi ◽  
Oluwole A. Odetunmibi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Length Of Stay ◽  
Cancer Survival ◽  
Breast Cancer Survival

Studying variables affecting the length of stay in patients with lower limb fractures by means of Machine Learning

2021 ◽  
Author(s):  
Ylenia Colella ◽  
Arianna Scala ◽  
Chiara De Lauri ◽  
Francesco Bruno ◽  
Giuseppe Cesarelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Lower Limb ◽  
Lower Limb Fractures ◽  
Limb Fractures

45 Application of Machine Learning Models to Thermal Burn Patient Outcome Predictions in the Aftermath of a Nuclear Event

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S33-S34
Author(s):  
Morgan A Taylor ◽  
Randy D Kearns ◽  
Jeffrey E Carter ◽  
Mark H Ebell ◽  
Curt A Harris
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Large Scale ◽  
Prediction Models ◽  
Burn Patients ◽  
Thermal Burn ◽  
Logistic Regression Models ◽  
Burn Patient

Abstract Introduction A nuclear disaster would generate an unprecedented volume of thermal burn patients from the explosion and subsequent mass fires (Figure 1). Prediction models characterizing outcomes for these patients may better equip healthcare providers and other responders to manage large scale nuclear events. Logistic regression models have traditionally been employed to develop prediction scores for mortality of all burn patients. However, other healthcare disciplines have increasingly transitioned to machine learning (ML) models, which are automatically generated and continually improved, potentially increasing predictive accuracy. Preliminary research suggests ML models can predict burn patient mortality more accurately than commonly used prediction scores. The purpose of this study is to examine the efficacy of various ML methods in assessing thermal burn patient mortality and length of stay in burn centers. Methods This retrospective study identified patients with fire/flame burn etiologies in the National Burn Repository between the years 2009 – 2018. Patients were randomly partitioned into a 67%/33% split for training and validation. A random forest model (RF) and an artificial neural network (ANN) were then constructed for each outcome, mortality and length of stay. These models were then compared to logistic regression models and previously developed prediction tools with similar outcomes using a combination of classification and regression metrics. Results During the study period, 82,404 burn patients with a thermal etiology were identified in the analysis. The ANN models will likely tend to overfit the data, which can be resolved by ending the model training early or adding additional regularization parameters. Further exploration of the advantages and limitations of these models is forthcoming as metric analyses become available. Conclusions In this proof-of-concept study, we anticipate that at least one ML model will predict the targeted outcomes of thermal burn patient mortality and length of stay as judged by the fidelity with which it matches the logistic regression analysis. These advancements can then help disaster preparedness programs consider resource limitations during catastrophic incidents resulting in burn injuries.

A machine learning-based model for predicting intensive care unit length of stay in patients with cardiac surgery (Preprint)

2021 ◽  
Author(s):  
Nianyue Wu ◽  
Siru Liu ◽  
Haotian Zhang ◽  
Xiaomin Hou ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Length Of Stay ◽  
Confidence Interval ◽  
Medical Information ◽  
Lasso Regression ◽  
Icu Length Of Stay ◽  
Data Source ◽  
The Mean ◽  
Selection Operator

BACKGROUND The intensive care unit (ICU) length of stay is significant to evaluate the effect of cardiac surgical treatment inpatient. OBJECTIVE This research aims to accurately predict the ICU length of stay in patients with cardiac surgery. Methods: We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression. METHODS We used machine learning methods to construct the model, and the medical information mart for intensive care (MIMIC IV) database was used as the data source. A total of 7,567 patients were enrolled and the mean length of stay in the ICU was 3.12 days. A total of 126 predictors were included, and 44 important predictors were screened by least absolute shrinkage and selection operator (Lasso) regression. RESULTS The mean accuracy are 0.603 (95% confidence interval (CI): [0.602-0.604]), 0.687 (95% confidence interval (CI): [0.687-0.688]) and 0.688 (95% confidence interval (CI): [0.687-0.689]) for the logistic regression (LR) with all variables, the gradient boosted decision tree (GBDT) with important variables and the GBDT with all variables respectively. CONCLUSIONS The GBDT model with important predictors partly overestimated patients whose length of stay was less than 3 days and underestimated patients whose length of stay was longer than 3 days. But the better prediction performance of GBDT facilitates early intervention of ICU patients with a long period of hospitalization.

scholarly journals Infectious safety during primary specialized accreditation in the COVID-19 pandemic

2021 ◽  
pp. 14-20
Author(s):  
Evgeny Germanovich Ripp ◽  
A. R. Fattakhov ◽  
T. M. Ripp ◽  
R. A. Postanogov ◽  
N. M. Iminov ◽  
...  
Keyword(s):  
Medical Education ◽  
Medical Research ◽  
Research Centre ◽  
Face To Face ◽  
Technological Processes ◽  
Specialized Accreditation ◽  
National Medical ◽  
Technical Personnel ◽  
The Face ◽  
Simulation Center

This article is devoted to the organization of the work of the Accreditation and Simulation Center of the Institute of Medical Education of the Almazov National Medical Research Centre during the primary specialized accreditation in the COVID-19 pandemic. Organizational solutions, technological processes and routing of accredited (308 people), support and technical personnel (98 people) and employees of the Accreditation and Simulation Center (14 people) and members of accreditation commissions (67 people) are presented to ensure infectious safety and the effectiveness of the face-to-face practice-oriented stage of accreditation.

scholarly journals Using Machine Learning Methods Incorporating Individual Reader Annotations to Classify Paediatric Chest Radiographs in Epidemiological Studies

2021 ◽  
Vol 6 ◽  
pp. 309
Author(s):  
Paul Mwaniki ◽  
Timothy Kamanu ◽  
Samuel Akech ◽  
M. J. C Eijkemans
Keyword(s):  
Machine Learning ◽  
Epidemiological Studies ◽  
Chest Radiographs ◽  
World Health ◽  
Data Sets ◽  
Learning Models ◽  
Middle Income ◽  
Training Models ◽  
Model Training ◽  
Machine Learning Models

Introduction: Epidemiological studies that involve interpretation of chest radiographs (CXRs) suffer from inter-reader and intra-reader variability. Inter-reader and intra-reader variability hinder comparison of results from different studies or centres, which negatively affects efforts to track the burden of chest diseases or evaluate the efficacy of interventions such as vaccines. This study explores machine learning models that could standardize interpretation of CXR across studies and the utility of incorporating individual reader annotations when training models using CXR data sets annotated by multiple readers. Methods: Convolutional neural networks were used to classify CXRs from seven low to middle-income countries into five categories according to the World Health Organization's standardized methodology for interpreting paediatric CXRs. We compared models trained to predict the final/aggregate classification with models trained to predict how each reader would classify an image and then aggregate predictions for all readers using unweighted mean. Results: Incorporating individual reader's annotations during model training improved classification accuracy by 3.4% (multi-class accuracy 61% vs 59%). Model accuracy was higher for children above 12 months of age (68% vs 58%). The accuracy of the models in different countries ranged between 45% and 71%. Conclusions: Machine learning models can annotate CXRs in epidemiological studies reducing inter-reader and intra-reader variability. In addition, incorporating individual reader annotations can improve the performance of machine learning models trained using CXRs annotated by multiple readers.

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