Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia

Andriulli, Angelo; Grossi, Enzo; Buscema, Massimo; Festa, V.; Intraligi, N.M; Dominici, P; Cerutti, Renata; Perri, Francesco

doi:10.1016/s1590-8658(03)00057-4

Cited by 25 publications

(11 citation statements)

References 27 publications

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“…The present results are similar to those obtained in previous studies in other medicine fields [37]–[42], suggesting that this kind of statistical approach may be better than the traditional one, to understand complex topics like human diseases.…”

Section: Discussionsupporting

confidence: 91%

Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

Eller-Vainicher

Chiodini

Santi³

et al. 2011

PLoS ONE

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BackgroundIt is known that bone mineral density (BMD) predicts the fracture's risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0.MethodologyWe compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively.ConclusionsANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF.

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Section: Discussionsupporting

confidence: 91%

Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

Eller-Vainicher

Chiodini

Santi³

et al. 2011

PLoS ONE

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“…Artificial adaptive systems can analyze real world data very efficiently. The internal validity of their assessment is provided by uniquely severe validation protocols, seldom used in classical statistics [Vomweg et al, 2003;Andriulli et al, 2003;Mecocci et al, 2002].…”

Section: Enhancing Internal Validity Of Observational Studiesmentioning

confidence: 99%

“…A multicentre study was carried out to test the potential usefulness of ANNs in predicting which patients with Helicobacter pylori (H.pylori) infection would benefit from eradication therapy [Andriulli et al, 2003]. Several models of ANN were tested.…”

Section: Dyspeptic Syndromementioning

confidence: 99%

Artificial intelligence and outcome research

Grossi

Buscema

2006

Drug Development Research

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The coupling of computer science and theoretical bases like non‐linear dynamics and chaos, quite new for medicine theory, allows the creation of “intelligent” agents (Artificial Adaptive Systems [AAS]) able to adapt themselves dynamically to problems of high complexity. ASS are able to reproduce the dynamical interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on an individual basis and not as average trends. These tools can offer specific advantages within the outcome research arena, helping to answer some open issues like enhancing the internal validity of observational studies, transferring evidence derived from clinical research to a single patient level, and performing “virtual” clinical trials as a guide for more efficient clinical development. A remarkable contribution to this individual approach comes from Fuzzy Logic, according to which there are no sharp limits between opposite things, like wealth and disease. This approach allows for partially escaping from the probability theory trap in situations where it is fundamental to express a judgement based on a single case and favour a novel humanism directed to the management of the patient as an individual subject. Some examples of original applications in the authors' experience are described. Drug Dev. Res. 67:227–244, 2006. © 2006 Wiley‐Liss, Inc.

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“…The most efficient and common architecture used in anANN is the feed forward ANN (Andriulli et al 2003; Das et al 2003; ZareNezhad and Aminian 2010). The inputs multiplied by the weights should be passed to a binary function according to the digital characteristic of the spikes, which are the elementary units of neural signal transmission.…”

Section: Introductionmentioning

confidence: 99%

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department

et al. 2013

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Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient’s emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician’s burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department.

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Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia

Cited by 25 publications

References 27 publications

Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

Artificial intelligence and outcome research

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department

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