scholarly journals Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms

2020 ◽  
Vol 26 (1) ◽  
pp. 20
Author(s):  
Hasan Symum ◽  
José L. Zayas-Castro
Keyword(s):  
Machine Learning ◽  
Chronic Disease ◽  
Length Of Stay ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Prolonged Length

scholarly journals Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Theyazn H.H Aldhyani ◽  
Ali Saleh Alshebami ◽  
Mohammed Y. Alzahrani
Keyword(s):  
Machine Learning ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Upper Approximation ◽  
Healthcare Data ◽  
Ambiguous Objects

Chronic diseases represent a serious threat to public health across the world. It is estimated at about 60% of all deaths worldwide and approximately 43% of the global burden of chronic diseases. Thus, the analysis of the healthcare data has helped health officials, patients, and healthcare communities to perform early detection for those diseases. Extracting the patterns from healthcare data has helped the healthcare communities to obtain complete medical data for the purpose of diagnosis. The objective of the present research work is presented to improve the surveillance detection system for chronic diseases, which is used for the protection of people’s lives. For this purpose, the proposed system has been developed to enhance the detection of chronic disease by using machine learning algorithms. The standard data related to chronic diseases have been collected from various worldwide resources. In healthcare data, special chronic diseases include ambiguous objects of the class. Therefore, the presence of ambiguous objects shows the availability of traits involving two or more classes, which reduces the accuracy of the machine learning algorithms. The novelty of the current research work lies in the assumption that demonstrates the noncrisp Rough K-means (RKM) clustering for figuring out the ambiguity in chronic disease dataset to improve the performance of the system. The RKM algorithm has clustered data into two sets, namely, the upper approximation and lower approximation. The objects belonging to the upper approximation are favourable objects, whereas the ones belonging to the lower approximation are excluded and identified as ambiguous. These ambiguous objects have been excluded to improve the machine learning algorithms. The machine learning algorithms, namely, naïve Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), and random forest tree, are presented and compared. The chronic disease data are obtained from the machine learning repository and Kaggle to test and evaluate the proposed model. The experimental results demonstrate that the proposed system is successfully employed for the diagnosis of chronic diseases. The proposed model achieved the best results with naive Bayes with RKM for the classification of diabetic disease (80.55%), whereas SVM with RKM for the classification of kidney disease achieved 100% and SVM with RKM for the classification of cancer disease achieved 97.53 with respect to accuracy metric. The performance measures, such as accuracy, sensitivity, specificity, precision, and F-score, are employed to evaluate the performance of the proposed system. Furthermore, evaluation and comparison of the proposed system with the existing machine learning algorithms are presented. Finally, the proposed system has enhanced the performance of machine learning algorithms.

scholarly journals Factors and Predictors of Length of stay in Offenders diagnosed with schizophrenia- A Machine-learning-based approach

2020 ◽  
Author(s):  
Johannes Kirchebner ◽  
Moritz Günther ◽  
Martina Sonnweber ◽  
Alice King ◽  
Steffen Lau
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Learning Algorithms ◽  
Evaluation Criteria ◽  
Selection Procedure ◽  
Legal Aspects ◽  
Influential Factors ◽  
Machine Learning Algorithms ◽  
Forward Selection ◽  
Factors Affecting

Abstract Background: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables. Methods: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients’ characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay. Results: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic. Conclusions: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically. Results emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects.

scholarly journals Factors and predictors of length of stay in offenders diagnosed with schizophrenia- A machine-learning-based approach

2020 ◽  
Author(s):  
Johannes Kirchebner ◽  
Moritz Günther ◽  
Martina Sonnweber ◽  
Alice King ◽  
Steffen Lau
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Learning Algorithms ◽  
Evaluation Criteria ◽  
Selection Procedure ◽  
Legal Aspects ◽  
Influential Factors ◽  
Machine Learning Algorithms ◽  
Forward Selection ◽  
Factors Affecting

Abstract Background: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables. Methods: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients’ characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay. Results: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic. Conclusions: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically. Results emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects.

scholarly journals Factors And Predictors Of Length Of Stay In Offenders Diagnosed With Schizophrenia- A Machine-learning-based Approach

2020 ◽  
Author(s):  
Johannes Kirchebner ◽  
Moritz Günther ◽  
Alice King ◽  
Steffen Lau
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Forward Selection ◽  
Factors Affecting ◽  
Forensic Hospital ◽  
Psychiatric Hospitalizations ◽  
Forensic Psychiatric

Abstract Background Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study seeks to examine factors responsible for prolonged detentions of schizophrenic offenders referred to a Swiss forensic hospital using machine learning algorithms more apt to reveal non-linear interdependencies between variables.Methods In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic delinquents were reviewed by using a structured protocol on patients’ characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential predictors for length of stay were preselected. Machine learning algorithms identified the most efficient model for predicting length-of-stay.Results/ Conclusions Ten factors prolonging forensic hospitalization were identified: Six were related to aspects of the index offence (index offence, number of crimes, extend of injury to the victim of the offence), two were related to psychopathology at admission or even prior to that (hallucinations in psychiatric history), one alluded to the course of therapy (self-harming during inpatient treatment), and one referred to biographical aspects (poverty during childhood/ adolescence). Results are discussed in light of earlier reports on the subject.

scholarly journals Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery

2021 ◽  
Vol 10 (18) ◽  
pp. 4074
Author(s):  
Andrew S Zhang ◽  
Ashwin Veeramani ◽  
Matthew S. Quinn ◽  
Daniel Alsoof ◽  
Eren O. Kuris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Spinal Fusion ◽  
Learning Algorithms ◽  
Posterior Spinal Fusion ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Spine Deformity ◽  
Fusion Surgery ◽  
Spinal Fusion Surgery

(1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treatment for Adult Spine Deformity (ASD) will experience a prolonged LOS. (2) Methods: Patients undergoing treatment for ASD with posterior spinal fusion surgery were selected from the American College of Surgeon’s NSQIP dataset. Prolonged LOS was defined as a LOS greater than or equal to 9 days. Data was analyzed with the Logistic Regression, Decision Tree, Random Forest, XGBoost, and Gradient Boosting functions in Python with the Sci-Kit learn package. Prediction accuracy and area under the curve (AUC) were calculated. (3) Results: 1281 posterior patients were analyzed. The five algorithms had prediction accuracies between 68% and 83% for posterior cases (AUC: 0.566–0.821). Multivariable regression indicated that increased Work Relative Value Units (RVU), elevated American Society of Anesthesiologists (ASA) class, and longer operating times were linked to longer LOS. (4) Conclusions: Machine learning algorithms can predict if patients will experience an increased LOS following ASD surgery. Therefore, medical resources can be more appropriately allocated towards patients who are at risk of prolonged LOS.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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