Deep learning and alternative learning strategies for retrospective real-world clinical data

Chen, David; Liu, Sijia; Kingsbury, Paul R.; Sohn, Sunghwan; Storlie, Curtis B.; Habermann, Elizabeth B.; Naessens, James M.; Larson, David W.; Liu, Hongfang

doi:10.1038/s41746-019-0122-0

Cited by 169 publications

(118 citation statements)

References 33 publications

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“…Furthermore, the conventional classifier algorithms used in the study may require complex processes to provide better results. Deep learning algorithms can be applied directly on raw data to eliminate these disadvantages (Chen et al, ).…”

Section: Discussionmentioning

confidence: 99%

Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Polat

Topalcengiz

Danyluk

2019

Journal of Food Safety

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The purpose of this study was to evaluate the performance of artificial intelligence tools for the prediction of Salmonella presence and absence in agricultural surface waters based on the population of microbiological indicators (total coliform, generic Escherichia coli, and enterococci) and physicochemical attributes of water (air and water temperature, conductivity, ORP, pH, and turbidity). Previously collected data set from six agricultural ponds monitored for two growing seasons were used for analysis. Classification algorithms including artificial neural networks (ANNs), the nearest neighborhood algorithm (kNN), and support vector machines (SVM) were trained and tested with a 539-point data set for optimum prediction accuracy. Classification accuracy performances were validated with data set (400 samples) collected from different agricultural surface water sources. All tested algorithms yielded the highest accuracy around 75 ± 1% for generic E. coli followed by enterococci (65 ± 5%) and total coliform (60 ± 10%). Classifiers calculated 6-15% higher accuracy ranging from 62 to 66% for turbidity than all other tested physicochemical attributes.Based on E. coli populations measured in other water sources, trained algorithms predicted the presence and absence of Salmonella with an accuracy between 58.15 and 59.23%. The classification performance of ANN, kNN, and SVM algorithms are encouraging for the prediction of Salmonella in agricultural surface waters.

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Section: Discussionmentioning

confidence: 99%

Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Polat

Topalcengiz

Danyluk

2019

Journal of Food Safety

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“…In contrast to the recent advancement and current interest in clinically-applied deep learning, there is still no definitive evidence of a model with predictive performance that is similar to a human physician [131]. As of 2020, there is no immediate vision in which AI can fully automate drug research pipelines or independently diagnose and provide subsequent health care procedures making researchers and clinicians obsolete.…”

Section: Resultsmentioning

confidence: 99%

Electronic Medical Records and Machine Learning in Approaches to Drug Development

Shinozaki¹

2020

Artificial Intelligence in Oncology Drug Discovery and Development

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Electronic medical records (EMRs) were primarily introduced as a digital health tool in hospitals to improve patient care, but over the past decade, research works have implemented EMR data in clinical trials and omics studies to increase translational potential in drug development. EMRs could help discover phenotypegenotype associations, enhance clinical trial protocols, automate adverse drug event detection and prevention, and accelerate precision medicine research. Although feasible, data mining in EMRs still faces challenges. Existing machine learning tools may help overcome these bottlenecks in EMR mining to unlock new approaches in drug development. This chapter will explore the role of EMRs in drug development while evaluating the viability and bottlenecks of their uses in data mining. This will include discussions on EMR usage in drug development while highlighting successful outcomes in oncology and exploring ML tools to complement and enhance EMR as a widely accepted drug-research source, a section on current clinical applications of EMRs, and a conclusion to summarize and imagine what a future drug research pipeline from EMR to patient treatment may look like.

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“…There has been a warning that healthcare researchers should not be overly enthralled by the promises of deep learning methods [26]. Therefore, to avoid abusing the deep learning method in our study, we also compared the performance of the CNN model with a conventional statistical learning method for continuous variables, using a multivariate linear regression model.…”

Section: Discussionmentioning

confidence: 99%

Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network: A Study Based on the Scandinavian Obesity Surgery Registry

Cao

Raoof

Montgomery

et al. 2019

JCM

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Severe obesity has been associated with numerous comorbidities and reduced health-related quality of life (HRQoL). Although many studies have reported changes in HRQoL after bariatric surgery, few were long-term prospective studies. We examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg). CNN was used to predict the 5-year HRQoL after bariatric surgery in a training dataset and evaluated in a test dataset. In general, performance of the CNN model (measured as mean squared error, MSE) increased with more convolution layer filters, computation units, and epochs, and decreased with a larger batch size. The CNN model showed an overwhelming advantage in predicting all the HRQoL measures. The MSEs of the CNN model for training data were 8% to 80% smaller than those of the linear regression model. When the models were evaluated using the test data, the CNN model performed better than the linear regression model. However, the issue of overfitting was apparent in the CNN model. We concluded that the performance of the CNN is better than the traditional multivariate linear regression model in predicting long-term HRQoL after bariatric surgery; however, the overfitting issue needs to be mitigated using more features or more patients to train the model.

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Deep learning and alternative learning strategies for retrospective real-world clinical data

Cited by 169 publications

References 33 publications

Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Prediction of Salmonella presence and absence in agricultural surface waters by artificial intelligence approaches

Electronic Medical Records and Machine Learning in Approaches to Drug Development

Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network: A Study Based on the Scandinavian Obesity Surgery Registry

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