Neural Network Analysis Of Medical Student Personality, Gender, And .
Neural Network Analysis of Medical StudentPersonality, Gender, and Perspective TakingPresenter: Robert Treat PhDCo-authors: Amy Prunuske PhD, Jeffery D. Fritz PhD, Kristina Kaljo PhD, Craig Hanke PhD
I affirm that all persons involved in the planning/contentdevelopment do not have relevant financial relationships withpharmaceutical companies, biomedical device manufacturers ordistributors, or others whose products or services may be consideredrelated to the subject matter of the educational activity.Declaration of Conflicts of Interest
Background
Background
An artificial neural network (ANN) is a computational system consisting of a circuit ofneurons that solves problems Tries to mimic the structure and function of a biological neural network (BNN)ooNot realistic, but relevant to the structure and function of brainHard to interpret, but helps to explain complexity of brainBackground
Regression analysiso Simple linear or linearized relationshipso Calculations are simple and quickBackground
Neural network analysis (NNA) is a powerful predictive alternative to regressionanalysis when there . . .o are non-linear relationshipso is low statistical power.11. Jordan, Henry, Navarro, Daniel, Stringer, Simon, The formation and use of hierarchical cognitive maps in the brain: A neural network model, Network: Computation in Neural Systems, 2020;31:1-4, d
Some types of Artificial Neural Networks (ANNs) . . .ANNPurposeBNN EquivalentAnalyzeFacial recognitionMedical image analysisHuman visionAnalyzetemporal dataSpeech recognitionHandwriting recognitionHuman speechConvolutional spatial dataRecurrentApplicationsTypes of ANNs
Correct YesIncorrect No Analyze Image for Empathy Pixels in an Image used in Input LayerForward Propagation (Information) ----- ----- Backward Propagation (Error Correction)Neural Network Structure
Neuroimaging studies of empathy and NNA have been reported,3,4 and this is typically the typesof images used in convolutional neural networks. Fan3 and Timmers4 reported different regions of the brain could be identified forooPerspective taking (cognitive aspect of empathy)Empathic concern (affective aspect of empathy)3. Fan Y, Duncan NW, de Greck M, Northoff G. Is There a Core Neural Network in Empathy? An fMRI Based Quantitative Meta-Analysis. Neuroscience Biobehavioral Review. 2011 Jan;35(3):903-11. doi:10.1016/j.neubiorev.2010.10.009. Epub 2010 Oct 23. PMID: 20974173.4. Timmers I, Park AL, Fischer MD, Kronman CA, Heathcote LC, Hernandez JM and Simons LE (2018) Is Empathy for Pain Unique in Its Neural Correlates? A Meta-Analysis of Neuroimaging Studies of Empathy.Frontiers in Behavioral Neuroscience 12:289. doi: 10.3389/fnbeh.2018.002891. Jordan, Henry, Navarro, Daniel, Stringer, Simon, The formation and use of hierarchical cognitive maps in the brain: A neural network model, Network: Computation in Neural Systems,2020;31:1-4, 37-141, doi:10.1080/0954898X.2020.1798531.2. Yao, Fuguang. Deep learning analysis of human behaviour recognition based on convolutional neural network analysis. Behaviour & Information Technology. 2020;No PaginationSpecified. doi:10.1080/0144929X.2020.1716390.3. Fuochi, Giulia, Voci, Alberto. A deeper look at the relationship between dispositional mindfulness and empathy: Meditation experience as a moderator and dereification processes asmediators. Personality and Individual Differences. 2020;165. doi:10.1016/j.paid.2020.110122.Neuroimaging
The literature is sparse when using NNA to examine the impact of medicalstudent personality and gender on perspective taking.2 Purpose: To analyze the impact of medical student personality and genderon perspective taking using neural network analysis.2. Fuochi, Giulia, Voci, Alberto. A deeper look at the relationship between dispositional mindfulness and empathy: Meditation experience as a moderator and dereification processes as mediators. Personalityand Individual Differences. 2020;165. doi:10.1016/j.paid.2020.110122.1. Jordan, Henry, Navarro, Daniel, Stringer, Simon, The formation and use of hierarchical cognitive maps in the brain: A neural network model, Network: Computation in Neural Systems,2020;31:1-4, 37-141, doi:10.1080/0954898X.2020.1798531.2. Yao, Fuguang. Deep learning analysis of human behaviour recognition based on convolutional neural network analysis. Behaviour & Information Technology. 2020;No PaginationSpecified. doi:10.1080/0144929X.2020.1716390.3. Fuochi, Giulia, Voci, Alberto. A deeper look at the relationship between dispositional mindfulness and empathy: Meditation experience as a moderator and dereification processes asmediators. Personality and Individual Differences. 2020;165. doi:10.1016/j.paid.2020.110122.Background and Purpose
Sample: 2017/18, 205 of 500 M1/M2 medical students (106 M/99 F) Instrumentsoo50-Item Five Factor Personality Inventory5,6 (5-pt scale)28-Item Interpersonal Reactivity Index7 (5-pt scale) Analysis via IBM SPSS 26.0o Deep Learning with Neural Network Analysis: Multi-layer perceptron 70% Training Data 30% Testing Data Activation Functions: Hyperbolic tangent (hidden layer), Softmax (output layer) Error (Loss) Function: Cross-entropyo Machine Learning: Binary logistic regression5. Lewis R. Goldberg. (1999). Mervielde, I.; Deary, I.; De Fruyt, F.; Ostendorf, F. (eds.). "A Broad-bandwidth, Public Domain, Personality Inventory Measuring the Lower-level Facets of Several Five-factorModels." Personality Psychology in Europe, 7:14-17.6. Costa, Paul T.; McCrae, Robert R. (1985). "The NEO Personality Inventory Manual". Odessa, FL: Psychological Assessment Resources.7. Davis MH. A Multidimensional Approach to Individual Differences in Empathy. JSAS Catalog of Selected Documents in Psychology, 1980;10:85.1. Jordan, Henry, Navarro, Daniel, Stringer, Simon, The formation and use of hierarchical cognitive maps in the brain: A neural network model, Network: Computation in Neural Systems,2020;31:1-4, 37-141, doi:10.1080/0954898X.2020.1798531.2. Yao, Fuguang. Deep learning analysis of human behaviour recognition based on convolutional neural network analysis. Behaviour & Information Technology. 2020;No PaginationSpecified. doi:10.1080/0144929X.2020.1716390.3. Fuochi, Giulia, Voci, Alberto. A deeper look at the relationship between dispositional mindfulness and empathy: Meditation experience as a moderator and dereification processes asmediators. Personality and Individual Differences. 2020;165. doi:10.1016/j.paid.2020.110122.Methods
Table 1: Distribution StatisticsNumber of StudentsFig 1: Histogram of Perspective Taking Scores ----- Negative skewMean26.8SD4.7Percentile 2524.0Median27.0Percentile 7530.0Normalityp .001*Skew Ratio-3.3Kurtosis Ratio1.5 Non-normality due tonegative skew ratio Use median as cut-pointto dichotomize scoresMedian 27.01. Jordan, Henry, Navarro, Daniel, Stringer, Simon, The formation and use of hierarchical cognitive maps in the brain: A neural network model, Network: Computation in Neural Systems,2020;31:1-4, 37-141, doi:10.1080/0954898X.2020.1798531.2. Yao, Fuguang. Deep learning analysis of human behaviour recognition based on convolutional neural network analysis. Behaviour & Information Technology. 2020;No PaginationSpecified. doi:10.1080/0144929X.2020.1716390.3. Fuochi, Giulia, Voci, Alberto. A deeper look at the relationship between dispositional mindfulness and empathy: Meditation experience as a moderator and dereification processes asmediators. Personality and Individual Differences. 2020;165. doi:10.1016/j.paid.2020.110122.Results
PerspectiveTaking35Personality FactorCorrelation ess0.3*Extraversion0.3*Neuroticism-0.3*r 0.528710203040AgreeablenessFig 2: Scatterplot of Perspective Takingon Agreeableness (Personality Factor)50* All Pearson correlations (r) of personality factors withperspective taking are statistically significant (p 0.050)Table 2: Pearson Correlations of PerspectiveTaking and Personality Factors1. Jordan, Henry, Navarro, Daniel, Stringer, Simon, The formation and use of hierarchical cognitive maps in the brain: A neural network model, Network: Computation in Neural Systems,2020;31:1-4, 37-141, doi:10.1080/0954898X.2020.1798531.2. Yao, Fuguang. Deep learning analysis of human behaviour recognition based on convolutional neural network analysis. Behaviour & Information Technology. 2020;No PaginationSpecified. doi:10.1080/0144929X.2020.1716390.3. Fuochi, Giulia, Voci, Alberto. A deeper look at the relationship between dispositional mindfulness and empathy: Meditation experience as a moderator and dereification processes asmediators. Personality and Individual Differences. 2020;165. doi:10.1016/j.paid.2020.110122.Results
Increasing Predictor StrengthConscientiousness (.45)Agreeableness (.40)Agreeableness (.37)Extraversion (.26)Neuroticism (.08)Perspective TakingExtraversion (.06)Openness (.04)FemaleOpenness (.22)Neuroticism (.08)* Significant predictors from LogisticRegression in red font onlyConscientiousness 1Table 3: Percent Correct of NNATraining and Testing ModelsMaleFig 3: NNA Prediction Model of Perspective Taking from Personality Factors (Importance Coefficients) Conscientiousness: Top predictor of perspective taking for females, bottom for males. Extraversion and Openness: Better predictors for males than females. Agreeableness: Only predictor identified with NNA and regression for both gender.1. Jordan, Henry, Navarro, Daniel, Stringer, Simon, The formation and use of hierarchical cognitive maps in the brain: A neural network model, Network: Computation in Neural Systems,2020;31:1-4, 37-141, doi:10.1080/0954898X.2020.1798531.2. Yao, Fuguang. Deep learning analysis of human behaviour recognition based on convolutional neural network analysis. Behaviour & Information Technology. 2020;No PaginationSpecified. doi:10.1080/0144929X.2020.1716390.3. Fuochi, Giulia, Voci, Alberto. A deeper look at the relationship between dispositional mindfulness and empathy: Meditation experience as a moderator and dereification processes asmediators. Personality and Individual Differences. 2020;165. doi:10.1016/j.paid.2020.110122.Results
Female medical student’s perspective taking benefitted fromgreater conscientiousness due in part to taking obligations toothers seriously. Male medical student’s extraversion and openness increasedperspective taking suggesting that enjoying human interactions andbeing enthusiastic, assertive, and gregarious helps empathize withothers.1. Jordan, Henry, Navarro, Daniel, Stringer, Simon, The formation and use of hierarchical cognitive maps in the brain: A neural network model, Network: Computation in Neural Systems,2020;31:1-4, 37-141, doi:10.1080/0954898X.2020.1798531.2. Yao, Fuguang. Deep learning analysis of human behaviour recognition based on convolutional neural network analysis. Behaviour & Information Technology. 2020;No PaginationSpecified. doi:10.1080/0144929X.2020.1716390.3. Fuochi, Giulia, Voci, Alberto. A deeper look at the relationship between dispositional mindfulness and empathy: Meditation experience as a moderator and dereification processes asmediators. Personality and Individual Differences. 2020;165. doi:10.1016/j.paid.2020.110122.Conclusions
If you only remember one thing from this session it could be that . . .Neural network analysis was a better predictiveanalysis over logistic regression for medical student’sperspective taking from personality and gender.Thank You!
Analysis via IBM SPSS 26.0 o Deep Learning with Neural Network Analysis: Multi -layer perceptron 70% Training Data 30% Testing Data Activation Functions: Hyperbolic tangent (hidden layer), Softmax (output layer) . Stringer, Simon, The formation and use of hierarchical cognitive maps in the brain: A neural network model, Network .
neural networks and substantial trials of experiments to design e ective neural network structures. Thus we believe that the design of neural network structure needs a uni ed guidance. This paper serves as a preliminary trial towards this goal. 1.1. Related Work There has been extensive work on the neural network structure design. Generic algorithm (Scha er et al.,1992;Lam et al.,2003) based .
Different neural network structures can be constructed by using different types of neurons and by connecting them differently. B. Concept of a Neural Network Model Let n and m represent the number of input and output neurons of a neural network. Let x be an n-vector containing the external inputs to the neural network, y be an m-vector
A growing success of Artificial Neural Networks in the research field of Autonomous Driving, such as the ALVINN (Autonomous Land Vehicle in a Neural . From CMU, the ALVINN [6] (autonomous land vehicle in a neural . fluidity of neural networks permits 3.2.a portion of the neural network to be transplanted through Transfer Learning [12], and .
Neural Network Programming with Java Unleash the power of neural networks by implementing professional Java code Fábio M. Soares Alan M.F. Souza BIRMINGHAM - MUMBAI . Building a neural network for weather prediction 109 Empirical design of neural networks 112 Choosing training and test datasets 112
neural networks. Figure 1 Neural Network as Function Approximator In the next section we will present the multilayer perceptron neural network, and will demonstrate how it can be used as a function approximator. 2. Multilayer Perceptron Architecture 2.1 Neuron Model The multilayer perceptron neural network is built up of simple components.
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural net-works. Information that flows through the network affects the structure of the ANN because a neural network changes - or learns, in a sense - based on that input and output. Pre pro-cessing Fig. 2 Neural network
application of neural networks is to test the trained neural network. Testing the artificial neural network is very important in order to make sure the trained network can generalize well and produce desired outputs when new data is presented to it. There are several techniques used to test the performance of a trained network, a few of which are
Performance comparison of adaptive shrinkage convolution neural network and conven-tional convolutional network. Model AUC ACC F1-Score 3-layer convolutional neural network 97.26% 92.57% 94.76% 6-layer convolutional neural network 98.74% 95.15% 95.61% 3-layer adaptive shrinkage convolution neural network 99.23% 95.28% 96.29% 4.5.2.
