Artificial Neural Networks and Predictive Medicine: a Revolutionary Paradigm Shift

Grossi, Enzo

doi:10.5772/15810

Cited by 17 publications

(13 citation statements)

References 29 publications

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“…It has been argued that ML can offer an indispensable tool for biomedical problems involving complex heterogeneous data when conventional statistical tools fail (Inza et al 2010;Campbell 2014;Grossi 2011). In applications such as gene selection (Hoff et al 2008), screening heart murmurs in children (DeGroff et al 2001), and predicting breast cancer relapse (Faradmal et al 2014), ML-based models were able to map highly non-linear input and output patterns even when mechanistic relationships between model variables could not be determined due to pathologies or complexity.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

et al. 2015

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Experimental datasets in bioengineering are commonly limited in size, thus rendering Machine Learning (ML) impractical for predictive modelling. Novel techniques of multiple runs for model development and surrogate data analysis for model validation are suggested for prediction of biomedical outcomes based on small datasets for classification and regression tasks. The proposed framework was applied to designing a Neural Network model for osteoarthritic bone fracture risk stratification, and a Decision Tree model for prediction of antibody-mediated kidney transplant rejection. Despite the small datasets (35 bone specimens and 80 kidney transplants), the two models achieved high accuracy of 98.3% and 85%, respectively.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

et al. 2015

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“…ANN proved to be very useful in the current analysis as it allowed us to assess the role of potential predictors of CMCT and MEP at month 12 as continuous outcomes, without the possible constraints of parametric models (e.g., normal distribution of the outcome, etc.). To note, ANN had been successfully used in other medical fields [Mecocci, 2002;Grossi, 2011;Azarkhish et al, 2012;etc. ] and, in neurology, in particular [Mecocci, 2002;Shanthi et al, 2009; for a recent overview see Atanassova & Dimitrov, 2011].…”

Section: Discussionmentioning

confidence: 99%

Modelling of Transcranial Magnetic Stimulation in One-Year Follow-Up Study of Patients with Minor Ischaemic Stroke

Atanassova¹,

Chalakova²,

Dimitrov³

2012

Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges

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“…In healthcare systems, an interesting topic is homecare assistance by telemedicine [14][15][16], allowing home assistance through the use of appropriate certified devices connected to the cloud and able to transmit patient measurements from home to an external structure behaving as a control room. In this direction, ANN could predict patient physiological status [17], could be applied for predictive medicine [18], and for the prediction of heart problems [19], suggesting ANN as a good approach to predict patient health status in DSS. In particular, ANN applied in other applications exhibited high performances [20], specifically MLP-ANN provided a good match between experimental and predicted results [21], and good flexibility concerning predictive maintenance and big data analytics [22].…”

Section: Background: Tools and Specifications Useful For Rmp-dss Designmentioning

confidence: 99%

A Study of a Health Resources Management Platform Integrating Neural Networks and DSS Telemedicine for Homecare Assistance

Massaro¹,

Maritati²,

Savino³

et al. 2018

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Abstract:The proposed paper is related to a case of study of an e-health telemedicine system oriented on homecare assistance and suitable for de-hospitalization processes. The proposed platform is able to transfer efficiently the patient analyses from home to a control room of a clinic, thus potentially reducing costs and providing high-quality assistance services. The goal is to propose an innovative resources management platform (RMP) integrating an innovative homecare decision support system (DSS) based on a multilayer perceptron (MLP) artificial neural network (ANN). The study is oriented in predictive diagnostics by proposing an RMP integrating a KNIME (Konstanz Information Miner) MLP-ANN workflow experimented on blood pressure systolic values. The workflow elaborates real data transmitted via the cloud by medical smart sensors and provides a prediction of the patient status. The innovative RMP-DSS is then structured to enable three main control levels. The first one is a real-time alerting condition triggered when real-time values exceed a threshold. The second one concerns preventative action based on the analysis of historical patient data, and the third one involves alerting due to patient status prediction. The proposed study combines the management of processes with DSS outputs, thus optimizing the homecare assistance activities.

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Artificial Neural Networks and Predictive Medicine: a Revolutionary Paradigm Shift

Cited by 17 publications

References 29 publications

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

Machine Learning for Predictive Modelling based on Small Data in Biomedical Engineering

Modelling of Transcranial Magnetic Stimulation in One-Year Follow-Up Study of Patients with Minor Ischaemic Stroke

A Study of a Health Resources Management Platform Integrating Neural Networks and DSS Telemedicine for Homecare Assistance

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