Evaluating Medical Decision Making Heuristics and Other Business Heuristics with Neural Networks

Velanovich

2020

PLoS ONE

Self Cite

Background Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict transfusions for all inpatient operations. Methods Over 1.6 million surgical cases over a two year period from the NSQIP-PUF database are used. Data from 2014 (750937 records) are used for model development and data from 2015 (885502 records) are used for model validation. ANN and regression models are developed to predict perioperative transfusions for surgical patients. Results Various ANN models and logistic regression, using four variable sets, are compared. The best performing ANN models with respect to both sensitivity and area under the receiver operator characteristic curve outperformed all of the regression models (p < .001) and achieved a performance of 70-80% specificity with a corresponding 75-62% sensitivity. Conclusion ANNs can predict >75% of the patients who will require transfusion and 70% of those who will not. Increasing specificity to 80% still enables a sensitivity of almost 67%. The unique contribution of this research is the utilization of a single ANN model to predict transfusions across a broad range of surgical procedures.

Section: Discussionmentioning

confidence: 99%

Prediction of perioperative transfusions using an artificial neural network

Velanovich

2020

PLoS ONE

Self Cite

“…The proposed methodology emphasizes two significant issues with ANN development: improving models through noise reduction and improving the interpretation of results (to overcome the black-box nature of ANNs). A side benefit of the methods advocated for triangulation prior to the ANN is that any reduction in the independent variable set will reduce the overall costs of the model [13,35].…”

Section: Methodology For Ann Triangulationmentioning

confidence: 99%

“…The output value is compared to the known value from the historic training example and if an error above the error threshold exists, then the values of the weighted connections are adjusted to better approximate the observed output value. This type of learning is nonparametric and makes no assumptions about population distributions or the behavior of the error term [35].…”

Section: Input Layermentioning

confidence: 99%

“…As already mentioned in the previous section, comparison of ANN classification or forecasting results to standard statistical methods following a model selection approach [35,39] is a common method used by researchers to attempt to demonstrate both the validity of using an ANN model and also to demonstrate a methodological improvement gained from the use of the ANN modeling paradigm. However, the focus of the research reported in this paper is on nonconcurrent method triangulation and as such this section will focus on previous research that has utilized statistical and other methods in a nonconcurrent manner with ANNs.…”

Section: Previous Work With Triangulating Nnsmentioning

confidence: 99%

“…From the PLS-SEM model, it appears that either the engagement (E) construct variables or the organizational support (OS) variables or perhaps the combination of these two constructs will produce the best performing ANN model to predict an individual's CSE. As with most ANN research, various hidden node architectures are constructed for the three possible input vectors (E, OS, E, and OS combined) [35,39]. Each ANN starts with a quantity of hidden nodes equal to half the number of input nodes and this value is incremented by one and the ANN retrained until the performance starts to decay, indicating overlearning.…”

Section: Triangulation To Improve Ann Performancementioning

confidence: 99%

See 2 more Smart Citations

Methodological Triangulation Using Neural Networks for Business Research

Advances in Artificial Neural Systems

2012

Self Cite

Artificial neural network (ANN) modeling methods are becoming more widely used as both a research and application paradigm across a much wider variety of business, medical, engineering, and social science disciplines. The combination or triangulation of ANN methods with more traditional methods can facilitate the development of high-quality research models and also improve output performance for real world applications. Prior methodological triangulation that utilizes ANNs is reviewed and a new triangulation of ANNs with structural equation modeling and cluster analysis for predicting an individual's computer self-efficacy (CSE) is shown to empirically analyze the effect of methodological triangulation, at least for this specific information systems research case. A new construct, engagement, is identified as a necessary component of CSE models and the subsequent triangulated ANN models are able to achieve an 84% CSE group prediction accuracy.

An Evaluation of Artificial Neural Networks in Predicting Pancreatic Cancer Survival

Journal of Gastrointestinal Surgery

Velanovich

2017

ANN models can accurately predict the 7-month survival of pancreatic adenocarcinoma patients, both with and without resection, at a 91% sensitivity and 38% specificity. This implies that ANN models may be useful objective decision tools in complex treatment decisions. This information may be used by patients and surgeons in determining optimal treatment plans that minimize regret and improve the quality of life for these patients.