Computing Missing Values Using Neural Networks in Medical Field

Peterkova, A.; Nemeth, Martin; Böhm, Allan

doi:10.1109/ines.2018.8523857

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…One of the most common problems involved in the collection of medical data is the presence of missing values [9]. In fact, it is a common situation when extracting data and inferring the models in a medical scenario.…”

Section: Related Workmentioning

confidence: 99%

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SMURF: Systematic Methodology for Unveiling Relevant Factors in Retrospective Data on Chronic Disease Treatments

et al. 2019

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Deciding on the continuous treatment of chronic diseases is vital in terms of economy, quality of life, and time. We present a holistic data mining approach that addresses the prediction of the therapeutic response in a panoramic and feedback way while unveiling relevant medical factors. Panoramic prediction makes it possible to decide whether the treatment will be beneficial without using previous knowledge and without involving unnecessary treatments. Feedback prediction can be more accurate prediction since it considers the results of previous stages of the treatment. A novel label encoding called simulated annealing and rounding (SAR) encoding is also proposed to help improve the accuracy of prediction in both approaches. To unveil the medical factors that make the treatment effective for patients, various techniques are applied to the prediction models found through the proposed approaches. Finally, this methodology is applied in the realistic scenario of analyzing electronic medical records of migraineurs under BoNT-A treatment. The results show a significant improvement in accuracy due to the use of SAR encoding, from close to 60% (baseline) to 75% with panoramic prediction, and up to around 90% when using feedback prediction. Furthermore, the following factors have been found to be relevant when predicting the migraine treatment responses: migraine time evolution, unilateral pain, analgesic abuse, headache days, and the retroocular component. According to doctors, these factors are also medically relevant and in alignment with the medical literature.INDEX TERMS Multi-target prediction, classification algorithms, data mining, simulated annealing.

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Section: Related Workmentioning

confidence: 99%

“…Hence, it is essential to study these techniques and select the most appropriate ones to provide an accurate prediction. Nevertheless, dealing with medical data is not an easy task, as some problems such as heterogeneity [8] or simply the lack of values (missing values) [9] typically arise in EMRs.…”

Section: Introductionmentioning

confidence: 99%

SMURF: Systematic Methodology for Unveiling Relevant Factors in Retrospective Data on Chronic Disease Treatments

et al. 2019

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“…Unfortunately, the data analysis requires a cautious pre-analytic phase of meticulous data cleaning and processing which may be particularly challenging in multi-national observational studies and registries ( 10 ). In this paper, we describe in detail our methodology for processing the MIMIC dataset as a part of developing a scoring system for predicting cardiogenic shock (CS) in patients suffering from acute coronary syndrome (ACS) ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Data processing pipeline for cardiogenic shock prediction using machine learning

Jajcay

Bezak

Segev

et al. 2023

Front. Cardiovasc. Med.

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IntroductionRecent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS.MethodsWe mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)—based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction.ResultsWe achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization.ConclusionWe believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.

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“…Temporary trends of experimental data analysis in engineering are utilized by artificial neuron networks (ANN), fuzzy logic, Autoregressive Integrated Moving Average (ARIMA) [ 66 ], and machine-learning methods [ 67 ]. ANN proves its applicability as a universal tool for analysis of various not only in non-technical [ 68 ] but also in technical processes such as: hydro-mechanical [ 69 ], cutting force when grinding [ 70 ], prediction of milling process parameters [ 71 ], surface roughness when turning [ 72 ], and milling [ 73 ]. It was suggested to use ANN to analyze and optimize cutting parameters when turning Ti-6Al-4V, predicting surface roughness and cutting force [ 74 ], and material removal rate [ 75 ].…”

Section: Introductionmentioning

confidence: 99%

Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks

et al. 2022

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Drilling of Carbon Fiber-Reinforced Plastic/Titanium alloy (CFRP/Ti) stacks represents one of the most widely used machining methods for making holes to fasten assemblies in civil aircraft. However, poor machinability of CFRP/Ti stacks in combination with the inhomogeneous behavior of CFRP and Ti alloy face manufacturing and scientific community with a problem of defining significant factors and conditions for ensuring hole quality in the CFRP/Ti alloy stacks. Herein, we investigate the effects of drilling parameters on drilling temperature and hole quality in CFRP/Ti alloy stacks by applying an artificial neuron network (ANN). We varied cutting speed, feed rate, and time delay factors according to the factorial design L9 Taguchi orthogonal array and measured the drilling temperature, hole diameter, and out of roundness by using a thermocouple and coordinate measuring machine methods for ANN analysis. The results show that the drilling temperature was sensitive to the effect of stack material layer, cutting speed, and time delay factors. The hole diameter was mainly affected by feed, stack material layer, and time delay, while out of roundness was influenced by the time delay, stack material layer, and cutting speed. Overall, ANN can be used for the identification of the drilling parameters–hole quality relationship.

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Computing Missing Values Using Neural Networks in Medical Field

Cited by 6 publications

References 8 publications

SMURF: Systematic Methodology for Unveiling Relevant Factors in Retrospective Data on Chronic Disease Treatments

SMURF: Systematic Methodology for Unveiling Relevant Factors in Retrospective Data on Chronic Disease Treatments

Data processing pipeline for cardiogenic shock prediction using machine learning

Application of ANN for Analysis of Hole Accuracy and Drilling Temperature When Drilling CFRP/Ti Alloy Stacks

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