Applications of artificial neural networks in health care organizational decision-making: A scoping review

Shahid, Nida; Rappon, Tim; Berta, Whitney

doi:10.1371/journal.pone.0212356

Cited by 350 publications

(172 citation statements)

References 90 publications

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“…More and more industries are using artificial intelligence to make increasingly complex decisions, and many alternatives are available to them [32]. However, in view of our results, there is a paucity of guidance on selecting the appropriate methods tailored to the health-care industry.…”

Section: Discussionmentioning

confidence: 99%

“…Neural networks can have single or multiple layers, with nodes or neurons interconnected that allows signals to travel through the network. ANNs are typically divided into three layers of neurons, namely: input (receives the information), hidden (extracts patterns and performs the internal processing), and output (presents the final network output) [32,33]. Training is the process to optimize parameters [34].…”

Section: Discussionmentioning

confidence: 99%

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Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado

Villalón²,

Martínez-Martínez³

et al. 2020

Applied Sciences

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Neural networks are increasingly being used in the field of dentistry. The aim of this literature review was to visualize the state of the art of artificial intelligence in dental applications, such as the detection of teeth, caries, filled teeth, crown, prosthesis, dental implants and endodontic treatment. A search was conducted in PubMed, the Institute of Electrical and Electronics Engineers (IEEE) Xplore and arXiv.org. Data extraction was performed independently by two reviewers. Eighteen studies were included. The variable teeth was the most analyzed (n = 9), followed by caries (n = 7). No studies detecting dental implants and filled teeth were found. Only two studies investigated endodontic applications. Panoramic radiographies were the most common image employed (n = 5), followed by periapical images (n = 3). Near-infrared light transillumination images were employed in two studies and bitewing and computed tomography (CT) were employed in one study. The included articles used a wide variety of neuronal networks to detect the described variables. In addition, the database used also had a great heterogeneity in the number of images. A standardized methodology should be used in order to increase the compatibility and robustness between studies because of the heterogeneity in the image database, type, neural architecture and results.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Dental Images Recognition Technology and Applications: A Literature Review

Prados-Privado

Villalón²,

Martínez-Martínez³

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…ML often uses statistical techniques to allow for the computer to ''learn'', or progressively improve performance on a given task, without being explicitly programmed. ML refers to a number of methods and algorithms, and different learning types: supervised, semi-supervised, unsupervised, reinforcement, evolutionary, and deep learning [4,5]. In supervised learning, every input pattern is trained to an associated output pattern and error in computed and desired outputs can be used in improve performance.…”

Section: Ai and Machine Learning Definedmentioning

confidence: 99%

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Davidson

Boland

2020

J Pharmacokinet Pharmacodyn

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The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and SCOPUS. Search terms included pregnancy and AI. Research articles and book chapters were included, while conference papers, editorials and notes were excluded from the review. Included papers focused on pregnancy and AI methods, and pertained to pharmacologic interventions. We identified 376 distinct studies from our queries. A final set of 31 papers were included for the review. Included papers represented a variety of pregnancy concerns and multidisciplinary applications of AI. Few studies relate to pregnancy, AI, and pharmacologics and therefore, we review carefully those studies. External validation of models and techniques described in the studies is limited, impeding on generalizability of the studies. Our review describes how AI has been applied to address maternal health, throughout the pregnancy process: preconception, prenatal, perinatal, and postnatal health concerns. However, there is a lack of research applying AI methods to understand how pharmacologic treatments affect pregnancy. We identify three areas where AI methods could be used to improve our understanding of pharmacological effects of pregnancy, including: (a) obtaining sound and reliable data from clinical records (15 studies), (b) designing optimized animal experiments to validate specific hypotheses (1 study) to (c) implementing decision support systems that inform decision-making (11 studies). The largest literature gap that we identified is with regards to using AI methods to optimize translational studies between animals and humans for pregnancyrelated drug exposures.

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“…32 We also considered using deep neural networks, but given the lack of dimensionality of our data, these models are substantially less accurate and interpretable. [33][34][35] Although we considered several options for building a supervised prediction model, unless otherwise specified, we present the results from a logistic regression prediction model.…”

Section: Data Handling and Machine Learning Approachesmentioning

confidence: 99%

Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach

Rosenfeld

Graham

Jevons

et al. 2020

The Lancet Digital Health

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Background Screening for Barrett's oesophagus relies on endoscopy, which is invasive and few who undergo the procedure are found to have the condition. We aimed to use machine learning techniques to develop and externally validate a simple risk prediction panel to screen individuals for Barrett's oesophagus. MethodsIn this prospective study, machine learning risk prediction in Barrett's oesophagus (MARK-BE), we used data from two case-control studies, BEST2 and BOOST, to compile training and validation datasets. From the BEST2 study, we analysed questionnaires from 1299 patients, of whom 880 (67·7%) had Barrett's oesophagus, including 40 with invasive oesophageal adenocarcinoma, and 419 (32·3%) were controls. We randomly split (6:4) the cohort using a computer algorithm into a training dataset of 776 patients and a testing dataset of 523 patients. We compiled an external validation cohort from the BOOST study, which included 398 patients, comprising 198 patients with Barrett's oesophagus (23 with oesophageal adenocarcinoma) and 200 controls. We identified independently important diagnostic features of Barrett's oesophagus using the machine learning techniques information gain and correlationbased feature selection. We assessed multiple classification tools to create a multivariable risk prediction model. Internal validation of the model using the BEST2 testing dataset was followed by external validation using the BOOST external validation dataset. From these data we created a prediction panel to identify at-risk individuals.Findings The BEST2 study included 40 diagnostic features. Of these, 19 added information gain but after correlationbased feature selection only eight showed independent diagnostic value including age, sex, cigarette smoking, waist circumference, frequency of stomach pain, duration of heartburn and acidic taste, and taking antireflux medication, of which all were associated with increased risk of Barrett's oesophagus, except frequency of stomach pain, with was inversely associated in a case-control population. Logistic regression offered the highest prediction quality with an area under the receiver-operator curve (AUC) of 0·87 (95% CI 0·84-0·90; sensitivity set at 90%; specificity of 68%). In the testing dataset, AUC was 0·86 (0·83-0·89; sensitivity set at 90%; specificity of 65%). In the external validation dataset, the AUC was 0·81 (0·74-0·84; sensitivity set at 90%; specificity of 58%). Interpretation Our diagnostic model offers valid predictions of diagnosis of Barrett's oesophagus in patients withsymptomatic gastro-oesophageal reflux disease, assisting in identifying who should go forward to invasive confirmatory testing. Our predictive panel suggests that overweight men who have been taking antireflux medication for a long time might merit particular consideration for further testing. Our risk prediction panel is quick and simple to administer but will need further calibration and validation in a prospective study in primary care.

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Applications of artificial neural networks in health care organizational decision-making: A scoping review

Cited by 350 publications

References 90 publications

Dental Images Recognition Technology and Applications: A Literature Review

Dental Images Recognition Technology and Applications: A Literature Review

Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence

Development and validation of a risk prediction model to diagnose Barrett's oesophagus (MARK-BE): a case-control machine learning approach

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