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 Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1993
Author(s):  
Fernando Pérez-Sanz ◽  
Miriam Riquelme-Pérez ◽  
Enrique Martínez-Barba ◽  
Jesús de la Peña-Moral ◽  
Alejandro Salazar Nicolás ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Liver Biopsies ◽  
Convenient Technique ◽  
End Stage ◽  
Macrovesicular Steatosis

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

Computational Method for Identifying Malonylation Sites by Using Random Forest Algorithm

2020 ◽  
Vol 23 (4) ◽  
pp. 304-312
Author(s):  
ShaoPeng Wang ◽  
JiaRui Li ◽  
Xijun Sun ◽  
Yu-Hang Zhang ◽  
Tao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Computational Method ◽  
Feature Subset ◽  
Random Forest Algorithm ◽  
Post Translational Modification

Background: As a newly uncovered post-translational modification on the ε-amino group of lysine residue, protein malonylation was found to be involved in metabolic pathways and certain diseases. Apart from experimental approaches, several computational methods based on machine learning algorithms were recently proposed to predict malonylation sites. However, previous methods failed to address imbalanced data sizes between positive and negative samples. Objective: In this study, we identified the significant features of malonylation sites in a novel computational method which applied machine learning algorithms and balanced data sizes by applying synthetic minority over-sampling technique. Method: Four types of features, namely, amino acid (AA) composition, position-specific scoring matrix (PSSM), AA factor, and disorder were used to encode residues in protein segments. Then, a two-step feature selection procedure including maximum relevance minimum redundancy and incremental feature selection, together with random forest algorithm, was performed on the constructed hybrid feature vector. Results: An optimal classifier was built from the optimal feature subset, which featured an F1-measure of 0.356. Feature analysis was performed on several selected important features. Conclusion: Results showed that certain types of PSSM and disorder features may be closely associated with malonylation of lysine residues. Our study contributes to the development of computational approaches for predicting malonyllysine and provides insights into molecular mechanism of malonylation.

scholarly journals Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Özer Depren ◽  
Mustafa Tevfik Kartal ◽  
Serpil Kılıç Depren
Keyword(s):  
Machine Learning ◽  
Interest Rate ◽  
Learning Algorithms ◽  
Influential Factors ◽  
Machine Learning Algorithms ◽  
Least Square ◽  
Macroeconomic Factors ◽  
Country Level ◽  
Daily Data ◽  
Turkish Lira

AbstractSome countries have announced national benchmark rates, while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of 2021. Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate (TLREF), this study examines the determinants of TLREF. In this context, three global determinants, five country-level macroeconomic determinants, and the COVID-19 pandemic are considered by using daily data between December 28, 2018, and December 31, 2020, by performing machine learning algorithms and Ordinary Least Square. The empirical results show that (1) the most significant determinant is the amount of securities bought by Central Banks; (2) country-level macroeconomic factors have a higher impact whereas global factors are less important, and the pandemic does not have a significant effect; (3) Random Forest is the most accurate prediction model. Taking action by considering the study’s findings can help support economic growth by achieving low-level benchmark rates.

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