Employing evolutionary artificial neural network in risk-adjusted monitoring of surgical performance

Yeganeh, Ali; Shadman, Alireza; Shongwe, Sandile Charles

doi:10.1007/s00521-023-08257-x

Cited by 16 publications

(8 citation statements)

References 54 publications

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“…The tumor size and other clinical records are widely used for a patient’s survival prediction [ 35 ]. Additionally, the authors of [ 36 , 37 ] proposed models to estimate risk factors based on historical and current data. Moreover, the authors in [ 38 ] employed a deep learning method to diagnose Alzheimer’s disease.…”

Section: Related Workmentioning

confidence: 99%

ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction

et al. 2023

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Technology-assisted diagnosis is increasingly important in healthcare systems. Brain tumors are a leading cause of death worldwide, and treatment plans rely heavily on accurate survival predictions. Gliomas, a type of brain tumor, have particularly high mortality rates and can be further classified as low- or high-grade, making survival prediction challenging. Existing literature provides several survival prediction models that use different parameters, such as patient age, gross total resection status, tumor size, or tumor grade. However, accuracy is often lacking in these models. The use of tumor volume instead of size may improve the accuracy of survival prediction. In response to this need, we propose a novel model, the enhanced brain tumor identification and survival time prediction (ETISTP), which computes tumor volume, classifies it into low- or high-grade glioma, and predicts survival time with greater accuracy. The ETISTP model integrates four parameters: patient age, survival days, gross total resection (GTR) status, and tumor volume. Notably, ETISTP is the first model to employ tumor volume for prediction. Furthermore, our model minimizes the computation time by allowing for parallel execution of tumor volume computation and classification. The simulation results demonstrate that ETISTP outperforms prominent survival prediction models.

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Section: Related Workmentioning

confidence: 99%

ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction

et al. 2023

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“…Regarding trend identification, it is not possible to establish a specific trend due to the low total count of current papers (five papers). However, it can be observed that cardiac surgery performance monitoring is the most frequent application [36], [64], [94], [109].…”

Section: E Surgical Performancementioning

confidence: 99%

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

2023

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In recent years, the adoption of statistical process monitoring (SPM) techniques in healthcare has been successful. For instance, biosurveillance and biosignal monitoring have demonstrated direct benefits. As the latest reviews of the literature show, parametric SPM techniques have been implemented to evaluate the quality-of-service hospitals provide, track medical equipment, monitor safety markers, or assess the improvements made by quality projects. However, as shown in this research, world-trending topics in data science that include data-driven approaches integrated with SPM have not been reviewed. To bridge this gap and shed light on new research, a systematic review of scientific databases and a taxonomic literature analysis were performed. For the scientometric analysis, a set of bibliometric indicators were obtained to portray the performance of each subtopic, such as examining growth kinetics, identifying top authors, journals, countries and affiliations, as well as creating network maps of co-authorship and keyword co-occurrence. Additionally, the taxonomic analysis involved grouping proposals by methodological approach. Each approach was explained and discussed to identify the advantages, limitations, and challenges that researchers and practitioners may encounter. SPM researchers and practitioners require more flexibility in data-driven approaches to account for frequency unbalance, complexity, dimensionality problems, and speed. Those working in data-driven and computer-oriented areas can expand their toolbox by incorporating sequential approaches to enhance the power of their classifiers, assess risk, reduce misspecification, and adopt model-oriented mindsets.INDEX TERMS Data-driven, healthcare, scientometric, statistical process monitoring.

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“…Machine learning (ML) systems can adapt from data, detect patterns, and make different choices without the need for human interaction. Machine learning techniques are widely used in medical diagnosis and the monitoring of patients [12][13][14][15][16][17]. This method aids in the recognition of patterns and the computation of visual attributes that are relevant to the diagnosis process.…”

Section: Introductionmentioning

confidence: 99%

Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer

et al. 2023

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Lung cancer is the world’s second-largest cause of cancer mortality. Patients’ lives can be saved if this malignancy is detected early. Doctors, however, encounter difficulties in detecting cancer in computed tomography (CT) images. In recent years, significant research has been devoted to producing automated lung nodule detection methods that can help radiologists. Most of them use only the lung window in their analysis and generally do not consider the mediastinal windows, which, according to recent research, carry important information. In this paper, we propose a simple yet effective algorithm to analyze multi-window CT images for lung nodules. The algorithm works in three steps. First, the CT image is preprocessed to suppress any noise and improve the image quality. Second, the lungs are extracted from the preprocessed image. Based on the histogram analysis of the lung windows, we propose a multi-Otsu-based approach for lung segmentation in lung windows. The case of mediastinal windows is rather difficult due to irregular patterns in the histograms. To this end, we propose a global–local-mean-based thresholding technique for lung detection. In the final step, the nodule candidates are extracted from the segmented lungs using simple intensity-based thresholding. The radius of the extracted objects is computed to separate the nodule from the bronchioles and blood vessels. The proposed algorithm is evaluated on the benchmark LUNA16 dataset and achieves accuracy of over 94% for lung tumor detection, surpassing that of existing similar methods.

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Employing evolutionary artificial neural network in risk-adjusted monitoring of surgical performance

Cited by 16 publications

References 54 publications

ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction

ETISTP: An Enhanced Model for Brain Tumor Identification and Survival Time Prediction

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer

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