scholarly journals An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment

2021 ◽  
Vol 9 ◽  
Author(s):  
Sushruta Mishra ◽  
Hrudaya Kumar Tripathy ◽  
Hiren Kumar Thakkar ◽  
Deepak Garg ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Decision Tree ◽  
Predictive Model ◽  
Psychological Health ◽  
Psychological Disorders ◽  
Psychological Disorder ◽  
Health Improvement ◽  
Great Level ◽  
Contributing Factors ◽  
Data Set ◽  
Multi Level

Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk recognition with an accurate explainable interface. Standard questionnaires are utilized as data set and a new approach called a Q-Prioritization is employed to drop insignificant questions from the data set. Moreover, a novel balanced decision tree method based on repetitive oversampling is applied for the training and testing of the model. Predictive nature along with its contributing factors are interpreted with three techniques such as permuted feature importance, contrastive explanation, and counterfactual method, which together form a reasoning engine. The prediction outcome generated an impressive performance with an aggregated accuracy of 98.25%. The mean precision, recall, and F-score metric recorded were 0.98, 0.977, and 0.979, respectively. Also, it was noted that without applying Q-Prioritization, the accuracy significantly drops to 90.25%. The error rate observed with our model was only 0.026. The proposed multi-level psychological disorder predictive model can successfully serve as an assistive deployment for medical experts in the effective treatment of mental health.

Selection of children with ultra-severe traumatic brain injury for neurosurgical intervention

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Decision Tree ◽  
Prediction Model ◽  
Severe Traumatic Brain Injury ◽  
Pupillary Response ◽  
Data Set ◽  
Robust Model ◽  
Gcs Score ◽  
Combined Data

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters

2021 ◽  
pp. 036119812110098
Author(s):  
Miao Yu ◽  
Jinxing Shen ◽  
Changxi Ma
Keyword(s):  
High Speed ◽  
Injury Severity ◽  
Unobserved Heterogeneity ◽  
Research Effort ◽  
Random Parameter ◽  
Policy Implications ◽  
Contributing Factors ◽  
Severe Injuries ◽  
Data Set ◽  
Severity Levels

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

scholarly journals Exploring the Social Networks’ Use in the Health-Care Industry: A Multi-Level Analysis

2021 ◽  
Vol 18 (14) ◽  
pp. 7295
Author(s):  
Tommasina Pianese ◽  
Patrizia Belfiore
Keyword(s):  
Health Care ◽  
Social Networks ◽  
Health Care Providers ◽  
Health Care Industry ◽  
Future Research ◽  
Great Level ◽  
Care Providers ◽  
Care Industry ◽  
Multi Level ◽  
Video And Audio

The application of social networks in the health domain has become increasingly prevalent. They are web-based technologies which bring together a group of people and health-care providers having in common health-related interests, who share text, image, video and audio contents and interact with each other. This explains the increasing amount of attention paid to this topic by researchers who have investigated a variety of issues dealing with the specific applications in the health-care industry. The aim of this study is to systematize this fragmented body of literature, and provide a comprehensive and multi-level overview of the studies that has been carried out to date on social network uses in healthcare, taking into account the great level of diversity that characterizes this industry. To this end, we conduct a scoping review enabling to identify the major research streams, whose aggregate knowledge are discussed according to three levels of analysis that reflect the viewpoints of the major actors using social networks for health-care purposes, i.e., governments, health-care providers (including health-care organizations and professionals) and social networks’ users (including ill patients and general public). We conclude by proposing directions for future research.

Abstiegserfahrungen in der Kommune und der Aufstieg Unabhängiger Wählergemeinschaften. Neue Befunde aus einem Mehrebenen-Kommunalwahlpanel

2021 ◽  
Vol 52 (1) ◽  
pp. 59-77
Author(s):  
Christina-Marie Juen ◽  
Markus Tepe ◽  
Michael Jankowski
Keyword(s):  
Panel Data ◽  
Voter Turnout ◽  
Party System ◽  
Local Politics ◽  
Tax Revenue ◽  
Data Set ◽  
Political Fragmentation ◽  
Electoral Success ◽  
Party Positions ◽  
Multi Level

In Germany, Independent Local Lists (UWG) have become an integral part of local politics in recent decades . Despite their growing political importance, the reasons for their electoral rise have hardly been researched . Recent studies argue that Independent Local Lists pursue anti-party positions, which makes them attractive to voters who are dissatisfied with the party system . Assuming that a decline of confidence in established parties corresponds with the experience of local deprivation, this contribution uses a multi-level panel data set to investigate how socio-economic (emigration, aging, declining tax revenue) and political­cultural (turnout, fragmentation) deprivation processes affect the electoral success of Inde­pendent Local Lists . The empirical findings suggest that Independent Local Lists are more successful in municipalities where voter turnout has fallen and political fragmentation has increased .

scholarly journals PCA based Regression Decision Tree Classification for Somatic Mutations.

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1095-1102
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Somatic Mutations ◽  
Feature Selection Technique ◽  
Data Set ◽  
Cancer Data ◽  
Classification Feature ◽  
Decision Tree Classification ◽  
Proposed Model ◽  
Feature Selection Techniques

The analization of cancer data and normal data for the predication of somatic mu-tation occurrences in the data set plays an important role and several challenges persist in detectingsomatic mutations which leads to complexity of handling large volumes of data in classifi-cation with good accuracy. In many situations the dataset may consist of redundant and less significant features and there is a need to remove insignificant features in order to improve the performance of classification. Feature selection techniques are useful for dimensionality reduction purpose. PCA is one type of feature selection technique to identify significant attributes and is adopted in this paper. A novel technique, PCA based regression decision tree is proposed for classification of somatic mutations data in this paper.The performance analysis of this clas-sification process for the detection of somatic mutation is compared with existing algorithms and satisfactory results are obtained with the proposed model.

scholarly journals An Algorithm for Notifiable Disease Modeling and Prediction Using Artificial Intelligence Techniques: A Case of Kenya.

2021 ◽  
Author(s):  
Nicodemus Nzoka Maingi ◽  
Ismail Ateya Lukandu ◽  
Matilu MWAU
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Disease Modeling ◽  
Information Gain ◽  
Disease Outbreak ◽  
Notifiable Disease ◽  
Data Set ◽  
Great Opportunity ◽  
Binary Decision ◽  
Binary Decision Tree

Abstract BackgroundThe disease outbreak management operations of most countries (notably Kenya) present numerous novel ideas of how to best make use of notifiable disease data to effect proactive interventions. Notifiable disease data is reported, aggregated and variously consumed. Over the years, there has been a deluge of notifiable disease data and the challenge for notifiable disease data management entities has been how to objectively and dynamically aggregate such data in a manner such as to enable the efficient consumption to inform relevant mitigation measures. Various models have been explored, tried and tested with varying results; some purely mathematical and statistical, others quasi-mathematical cum software model-driven.MethodsOne of the tools that has been explored is Artificial Intelligence (AI). AI is a technique that enables computers to intelligently perform and mimic actions and tasks usually reserved for human experts. AI presents a great opportunity for redefining how the data is more meaningfully processed and packaged. This research explores AI’s Machine Learning (ML) theory as a differentiator in the crunching of notifiable disease data and adding perspective. An algorithm has been designed to test different notifiable disease outbreak data cases, a shift to managing disease outbreaks via the symptoms they generally manifest. Each notifiable disease is broken down into a set of symptoms, dubbed symptom burden variables, and consequently categorized into eight clusters: Bodily, Gastro-Intestinal, Muscular, Nasal, Pain, Respiratory, Skin, and finally, Other Symptom Clusters. ML’s decision tree theory has been utilized in the determination of the entropies and information gains of each symptom cluster based on select test data sets.ResultsOnce the entropies and information gains have been determined, the information gain variables are then ranked in descending order; from the variables with the highest information gains to those with the lowest, thereby giving a clear-cut criteria of how the variables are ordered. The ranked variables are then utilized in the construction of a binary decision tree, which graphically and structurally represents the variables. Should any variables have a tie in the information gain rankings, such are given equal importance in the construction of the binary decision-tree. From the presented data, the computed information gains are ordered as; Gastro-Intestinal, Bodily, Pain, Skin, Respiratory, Others. Muscular, and finally Nasal Symptoms respectively. The corresponding binary decision tree is then constructed.ConclusionsThe algorithm successfully singles out the disease burden variable(s) that are most critical as the point of diagnostic focus to enable the relevant authorities take the necessary, informed interventions. This algorithm provides a good basis for a country’s localized diagnostic activities driven by data from the reported notifiable disease cases. The algorithm presents a dynamic mechanism that can be used to analyze and aggregate any notifiable disease data set, meaning that the algorithm is not fixated or locked on any particular data set.

scholarly journals Could a Unified Theory of Cognition and Emotion Further the Transdiagnostic Perspective? A Critical Analysis Using Interacting Cognitive Subsystems as a Case Example

2016 ◽  
Vol a4 (3) ◽  
pp. 377-399 ◽  
Author(s):  
Felicity A Cowdrey ◽  
Claire Lomax ◽  
James D Gregory ◽  
Philip J Barnard
Keyword(s):  
Psychological Disorders ◽  
Psychological Disorder ◽  
Human Cognition ◽  
Attention And Memory ◽  
Unified Theory ◽  
Cognition And Emotion ◽  
Reciprocal Interactions ◽  
Unified Theories ◽  
Novel Therapeutic Strategies ◽  
Interacting Cognitive Subsystems

There is evidence that common processes underlie psychological disorders transdiagnostically. A challenge for the transdiagnostic movement is accounting for such processes theoretically. Theories of psychological disorders are traditionally restricted in scope, often explaining specific aspects of a disorder. The alternative to such ‘micro-theories’ is developing frameworks which explain general human cognition, so called ‘macro-theories’, and applying these systematically to clinical phenomena. Interacting Cognitive Subsystems (ICS) [Teasdale, J.D., & Barnard, P.J. (1993). Affect, cognition and change: Re-modelling depressive thought, Lawrence Erlbaum Associates, Hove] is a macro-theory which aims to explain aspects of information processing. The aim of this review is to examine whether ICS provides a useful platform for understanding common processes which maintain psychological disorders. The core principles of ICS are explained and theoretical papers adopting ICS to explain a particular psychological disorder or symptom are considered. Dysfunctional schematic mental models, reciprocal interactions between emotional and intellectual beliefs, as well as attention and memory processes, are identified as being important to the maintenance of psychological disorders. Concrete examples of how such variables can be translated into novel therapeutic strategies are given. The review concludes that unified theories of cognition and emotion have the potential to drive forward developments in transdiagnostic thinking, research and treatment.

scholarly journals Contrasting determinants for the introduction and establishment success of exotic birds in Taiwan using decision trees models

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3092 ◽  
Author(s):  
Shih-Hsiung Liang ◽  
Bruno Andreas Walther ◽  
Bao-Sen Shieh
Keyword(s):  
Decision Tree ◽  
Species Traits ◽  
Gradient Boosting ◽  
Small Data ◽  
Invasion Process ◽  
Data Set ◽  
Establishment Success ◽  
Exotic Birds ◽  
Tree Models ◽  
Nominal Variables

Background Biological invasions have become a major threat to biodiversity, and identifying determinants underlying success at different stages of the invasion process is essential for both prevention management and testing ecological theories. To investigate variables associated with different stages of the invasion process in a local region such as Taiwan, potential problems using traditional parametric analyses include too many variables of different data types (nominal, ordinal, and interval) and a relatively small data set with too many missing values. Methods We therefore used five decision tree models instead and compared their performance. Our dataset contains 283 exotic bird species which were transported to Taiwan; of these 283 species, 95 species escaped to the field successfully (introduction success); of these 95 introduced species, 36 species reproduced in the field of Taiwan successfully (establishment success). For each species, we collected 22 variables associated with human selectivity and species traits which may determine success during the introduction stage and establishment stage. For each decision tree model, we performed three variable treatments: (I) including all 22 variables, (II) excluding nominal variables, and (III) excluding nominal variables and replacing ordinal values with binary ones. Five performance measures were used to compare models, namely, area under the receiver operating characteristic curve (AUROC), specificity, precision, recall, and accuracy. Results The gradient boosting models performed best overall among the five decision tree models for both introduction and establishment success and across variable treatments. The most important variables for predicting introduction success were the bird family, the number of invaded countries, and variables associated with environmental adaptation, whereas the most important variables for predicting establishment success were the number of invaded countries and variables associated with reproduction. Discussion Our final optimal models achieved relatively high performance values, and we discuss differences in performance with regard to sample size and variable treatments. Our results showed that, for both the establishment model and introduction model, the number of invaded countries was the most important or second most important determinant, respectively. Therefore, we suggest that future success for introduction and establishment of exotic birds may be gauged by simply looking at previous success in invading other countries. Finally, we found that species traits related to reproduction were more important in establishment models than in introduction models; importantly, these determinants were not averaged but either minimum or maximum values of species traits. Therefore, we suggest that in addition to averaged values, reproductive potential represented by minimum and maximum values of species traits should be considered in invasion studies.

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