2017 AUT Mathematical Sciences Symposium
The Importance of Selecting Appropriate k-fold Cross-validation and Training Algorithms in Improving Postoperative Discharge Decision-making via Artificial Intelligence Luca Parisi1, Marianne Lyne Manaog2 1Auckland
Bioengineering Institute (ABI), University of Auckland
[email protected] 2MedIntellego®, Auckland, New Zealand
Recent advances in statistical, interview-based methods have facilitated postoperative decision-making processes; however, there is currently no objective method to assist in predicting patient-centred outcomes, such as postoperative home discharge. This process is critical as it may impair the management of the hospital resources and may influence the survival of patients after surgery. Previous Artificial Intelligence (AI)-based models were either too inaccurate to be applied clinically (LERS-LEM2: 48% accuracy) or too sophisticated for the relatively low gain in accuracy attained (evolutionary neural logic networks: 72.72% accuracy). As a minimum viable solution to predict where patients in a postoperative recovery area should be sent to next, the clinical potential of the following AI-based classifier was assessed: a multi-layer perceptron (MLP) with ten sigmoid hidden and Softmax output neurons, developed and tested in MATLAB (version 2017b, The MathWorks, Inc.). Body temperature, oxygen saturation and blood pressure measurements, along with the patient’s perceived comfort at discharge were obtained from the University of California-Irvine (UCI) database. 65-20-15 k-fold cross-validation and the scaled conjugate gradient learning algorithm were found to yield the highest classification accuracy: 82.35% (95% CI: 56.57% to 96.20%), improving the highest accuracy from the literature by almost 10%. Having negligible cross-entropy and mean squared errors (1.16 and 0.11 respectively), and high specificity (92.86%, 95% CI: 66.13% to 99.82%), the MLP, parameterised as above, is deemed a minimum viable solution for assisting clinicians in improving decision-making to minimise postoperative complications, improve the management of the hospital resources and ultimately patient outcome.
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2017 AUT Mathematical Sciences Symposium Auckland University of Technology Auckland, New Zealand 23rd – 24th November 2017
Published by: Mathematical Sciences Research Group School of Engineering, Computer and Mathematical Sciences Auckland University of Technology http://www.aut.ac.nz/study-at-aut/study-areas/computer-mathematical-sciences/research-groups/ mathematical-sciences-research-group
Welcome to the 2017 AUT Mathematical Sciences Symposium On behalf of the Mathematical Sciences Research Group within the School of Engineering, Computer and Mathematical Sciences at Auckland University of Technology, we have much pleasure in welcoming you to the 2017 AUT Mathematical Sciences Symposium. This is the fourth such Symposium and it is a continuation of our efforts to develop and promote the research being undertaken within the Department of Mathematical Sciences as part of our recently enlarged School. We are delighted to welcome a number of invited speakers to the Symposium with the aim of exploring collaborative opportunities and potential new areas of research that can be established with our research active staff. The concept of this Symposium was a joint effort of us. We both appreciate the assistance of staff of the Department, in particular Dr Sarah Marshall, Dr Nuttanan Wichitaksorn and Dr Wenjun Zhang, who have each been involved in a variety of activities to ensure the continued success of this series. As New Zealand’s newest university we have recently had the opportunity to employ a number of new academic staff, all of whom have been developing research profiles. The School is putting in place a number of opportunities that will support and assist them in extending and enhancing their activities, with this meeting being one such effort. Our growing postgraduate programme in the Mathematical Sciences at Honours, Masters and Doctoral levels has been enhanced with our Master of Analytics (MAnalytics) degree, now in its third year. The success of this programme, with around thirty students at various stages of completion of the degree is leading to increased project supervision demands on our staff as well as leading to growing links with business and industry. We have established an arrangement with the SAS Institute that sees students in our MAnalytics degree gaining SAS Certification on graduation. The Mathematical Sciences Research Group focuses on two main areas – Analytics and Applied Mathematics. We are very much focused on “research lead teaching” and we have developed a small number of research clusters within these areas to strengthen and support those academic staff working in these areas. Ideally we would like to foster collaborative activities and we thank those of you who have joined us at this meeting and we hope that we can facilitate some future joint research efforts. We have kept the focus narrow so as to make the meeting meaningful and rewarding for those who participate. We hope that you enjoy your time with us and that you find the exercise a useful adjunct to the mathematical and statistical scene within New Zealand. On behalf of the Mathematical Sciences Research Group Jeffrey Hunter Professor of Mathematical Sciences Jiling Cao Professor of Mathematics Co-chairs of the 2017 AUT Mathematical Sciences Symposium
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Symposium Schedule 8:30-9:00
Thursday 23rd November Registration
Friday 24th November
WF Level 7
Welcome
9:00-9:15
WF711
Matt Wand
Ilze Ziedins 9:15-10:00
9:00-9:45
A linear network with feedback and controls
Fast Approximate Inference for Arbitrarily Large Statistical Models via Message Passing
WF711
10:05 - 10:30
WF711
Murray Jorgensen
Hyuck Chung
WF711
WF514
9:50-10:15
10:30 - 11:00
Morning Tea
11:00 - 11:45
Preparing Financial Regulation for Forthcoming Crises
John Butcher
Samin Aref (Bibliometrix)
WF711
WF710
10:15-10:45
Morning Tea
10:45-11:30
Transport Equilibrium Models and Novel Applications
Andrea Raith
Eckhard Platen
WF711
11:50 - 12:15 12:15 - 12:40
WF711
Jose Da Fonseca
Catherine Hassell Sweatman
WF711
WF514
Jeong-Hoon Kim
Amir Rastar
WF711
WF514
12:00-12:25
Lunch
12:40 - 13:45
11:35-12:00
13:45-14:10
Correlated failures in multicomponent systems WF711
14:35-15:00 15:00-15:25
14:10-14:35
Winston Sweatman
Mohsen Pashna
WF711
WF710
Graeme Wake
Luca Parisi (Genetic Alg)
WF711
WF710
WF711
WF710
16:10-16:45
Nuttanan Wichitaksorn
Md. Shahidul Islam
Robin Willink
(Garment Industry) WF710
Marco Reale Parsimonious structures for multivariate time series models WF711
18:30 - 21:00
14:40-15:25 15:25-15:40
15:45-16:10
16:50-17:35
Sarah Marshall
Samin Aref (Binary Prog)
WF711
WF710
Lunch Jason Chen
Luca Parisi (AI)
WF711
WF710
Narrendar RaviChandran
Md. Shahidul Islam
WF711
WF710
(Demand Forecasting)
Which mechanisms shape species' responses to environmental problems like climate change WF711
Wenjun Zhang
WF711
WF710
William Godsoe
Afternoon Tea
15:25-15:45
Valerie Chopovda
WF711
12:25-13:45
Richard Arnold 13:45-14:30
Robin Hankin
Symposium Dinner Four Seasons Restaurant, WH Building Cnr Mayoral Drive and Wellesley Street East
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Farewell WF711
List of Authors and Titles Samin Aref, Andrew J. Mason, Mark C. Wilson Computing the frustration index in signed graphs using binary programming . . . . . . . 10 Samin Aref, David Friggens, Shaun Hendy Analysing Scientific Collaborations of New Zealand Institutions using Scopus Bibliometric Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Richard Arnold, Stefanka Chukova, Yu Hayakawa Correlated failures in multicomponent systems . . . . . . . . . . . . . . . . . . . . . . 11 John Butcher Polish notation and numerical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Jason Chen A Spatiotemporal Modelling Study of a GnRH Neuron . . . . . . . . . . . . . . . . . . 11 Valerie Chopovda, Anton Gulley, Winston Sweatman Using magnets for particle extraction from powder flow . . . . . . . . . . . . . . . . . . 12 Hyuck Chung Acoustic pressure fields of 2D elastic cylindrical shells . . . . . . . . . . . . . . . . . . 12 Jose Da Fonseca, Edem Dawui Semivariance and Semiskew Risk Premiums in Currency Markets . . . . . . . . . . . . 12 William Godsoe Which mechanisms shape species’ responses to environmental problems like climate change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Robin Hankin Generalized Bradley-Terry models for MasterChef Australia . . . . . . . . . . . . . . . 13 Catherine Hassell Sweatman Mathematical model of diabetes and lipid metabolism : predicting the consequences of a low carbohydrate healthy fat diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Md. Shahidul Islam, Jannat Ara Ferdousi Factors Influencing on Growth of Garment Industry . . . . . . . . . . . . . . . . . . . . 14 Md. Shahidul Islam, Mohammad Sazzad Mosharrof Comparison of Various Technique for Demand Forecasting in a Multinational Company . 14 Murray Jorgensen Clustering via Mixture Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Jeong-Hoon Kim SEV, Mellin Transform and Pricing of Financial Derivatives . . . . . . . . . . . . . . . 15 Sarah Marshall, Thomas Archibald Lot-sizing for a Product Recovery System with Quality-dependent Recovery Channels . 15 8
2017 AUT Mathematical Sciences Symposium
Luca Parisi, Marianne Lyne Manaog The Importance of Selecting Appropriate k-fold Cross-validation and Training Algorithms in Improving Postoperative Discharge Decision-making via Artificial Intelligence
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Luca Parisi, Narrendar RaviChandran Genetic Algorithms and Artificial Neural Networks for Optimising User Control in Hand Prosthetic Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Mohsen Pashna, Rubiyah Yusof, Neda Afshar Oil Spill Dispersion Simulation, and Multi Robot System to track and predict the pollution trajectory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Eckhard Platen, Michael Schmutz Preparing Financial Regulation for Forthcoming Crises . . . . . . . . . . . . . . . . . . 17 Andrea Raith Transport Equilibrium Models and Novel Applications . . . . . . . . . . . . . . . . . . 17 Amir Rastar Tracking for convective flow inside the respiratory airways using method of characteristics 18 Narrendar RaviChandran, Luca Parisi Bifurcation Analysis to Improve the Specificity of Biophysical Neuronal Models . . . . 18 Marco Reale Parsimonious structures for multivariate time series models . . . . . . . . . . . . . . . . 19 Winston Sweatman Memorable MISGs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Graeme Wake Asia-Pacific Initiatives for Mathematics-in-Industry . . . . . . . . . . . . . . . . . . . . 19 Matt Wand Fast Approximate Inference for Arbitrarily Large Statistical Models via Message Passing 20 Nuttanan Wichitaksorn, Boris Choy, Richard Gerlach Modeling Non-linear Dependence of Bivariate Seemingly Unrelated Tobit Models through Copulas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Robin Willink The Guide to the Expression of Uncertainty in Measurement: a progressive retrospective
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Wenjun Zhang, Jin. E. Zhang Changing Probability Measures in GARCH Option Pricing Models . . . . . . . . . . . . 21 Ilze Ziedins, Bill Helton, Frank Kelly, Ruth Williams A linear network with feedback and controls . . . . . . . . . . . . . . . . . . . . . . . . 21
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List of Participants First name Samin Richard Boris Christian Murray John Jiling Jason Valerie Renu Hyuck Jose Neda Fred William Robin Catherine Jeffrey Gulshad Md. Shahidul
Surname Aref Arnold Bacic Bläsche Black Butcher Cao Chen Chopovda Choudhary Chung Da Fonseca Farrokhi Afshar Furlan Godsoe Hankin Hassell Sweatman Hunter Imran Islam
Arunkumar Murray Jeong-Hoon Sarah Julian Luca Mohsen
Jayakumar Jorgensen Kim Marshall Mccree Parisi Pashna
Balint
Petro
Eckhard
Platen
Andrea Amir Narrendar Marco Shu
Raith Rastar RaviChandran Reale Su
Winston Brice Alna Graeme Matt Maojun Stuart
Sweatman Valles van der Merwe Wake Wand Wang Weston
Nuttanan Robin Xiangjie Sidra Wenjun Ilze
Wichitaksorn Willink Xue Zafar Zhang Ziedins
Affiliation Department of Computer Science, University of Auckland School of Mathematics and Statistics, Victoria University of Wellington Computer Science, Auckland University of Technology Institute of Natural and Mathematical Sciences, Massey University Department of Mathematical Sciences, Auckland University of Technology Mathematics, University of Auckland Department of Mathematical Sciences , Auckland University of Technology Department of Mathematical Science, Auckland University of Technology Institute of Natural and Mathematical Sciences, Massey University Department of Mathematical Sciences, Auckland University of Technology Department of Mathematical Sciences, Auckland University of Technology Department of Finance, Auckland University of Technology Faculty of Creative Art and Industry, University of Auckland Department of Mathematical Sciences, Auckland University of Technology Bioprotection research centre, Lincoln University Department of Mathematical Sciences, Auckland University of Technology Department of Mathematical Sciences, Auckland University of Technology Department of Mathematical Sciences, Auckland University of Technology Mathematical Sciences, Auckland University of Technology School of Engineering, Computer and Mathematical Science, Auckland University of Technology Department of Mechanical Engineering, Auckland University of Technology Department of Mathematical Sciences, AUT, Auckland University of Technology Department of Mathematics, Yonsei Uiversity Department of Mathematical Sciences, Auckland University of Technology Data analytics, Auckland University of Technology Auckland Bioengineering Institute (ABI), University of Auckland Malaysia Japan International Institute of Technology, Centre for Artificial and Intelligence and Robotics, UTM Visiting from Budapest University of Technology and Economics, Auckland University of Technology School of Mathematical and Physical Sciences, Finance Discipline Group, University of Technology Sydney Department of Engineering Science, University of Auckland Auckland Bioengineering Institute, University of Auckland Department of Mechanical Engineering, University of Auckland School of Mathematics and Statistics, University of Canterbury School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology Institute of Natural and Mathematical Sciences, Massey University Wholesale Analytics, Genesis Department of Mathematical Sciences, Auckland University of Technology Institute of Natural and Mathematical Sciences, Massey University School of Mathematical and Physical Sciences, University of Technology Sydney Department of Mathematical Sciences, Auckland University of Technology Institute for Radio Astronomy and Space Research , Auckland University of Technology Department of Mathematical Sciences, Auckland University of Technology Ministry of Business, Innovation and Employment Department of Statistics, University of Auckland Institute of Natural and Mathematical Sciences, Massey University Department of Mathematical Sciences, Auckland University of Technology Department of Statistics, University of Auckland
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