Data Decision and Drug Therapy Based on Non-Small Cell Lung Cancer in a Big Data Medical System in Developing Countries

Wu, Jia; Tan, Yanlin; Chen, Zhigang; Zhao, Ming

doi:10.3390/sym10050152

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…To this end, artificial intelligence (AI)-based assisted medical systems can be built to address such problems. In recent years, with the development of 5G, mobile Internet, and AI-related technologies, many intelligent medical systems based on IoT, smart devices [ 6 , 7 ], big data and AI technologies [ 8 ], and medical sensors [ 9 ], developed to assist in diagnosis and decision-making, have emerged. AI techniques are now being used to some extent in radiation oncology [ 10 ] and cancer diagnosis [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

Zhan

Long

Gou

et al. 2021

Sensors

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In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.

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Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

Zhan

Long

Gou

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Beijing, a small number of high-level hospital medical staff members have to provide medical services to more than 20 million people in Beijing, and many cancer patients in other regions also come here for the extraordinary medical treatment. Medical staff members have been overloaded for a long time [6][7][8][9]. This will inevitably have an impact on the accuracy of the diagnosis, thus leading to serious consequences.…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Medical Decision System for Prostate Cancer Based on Ensemble Method

Zhuang

Tan

2020

Computational and Mathematical Methods in Medicine

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Prostate cancer (PCa) is one of the main diseases that endanger men’s health worldwide. In developing countries, due to the large number of patients and the lack of medical resources, there is a big conflict between doctors and patients. To solve this problem, an auxiliary medical decision system for prostate cancer was constructed. The system used six relevant tumor markers as the input features and employed classical machine learning models (support vector machine and artificial neural network). Stacking method aimed at different ensemble models together was used for the reduction of overfitting. 1,933,535 patient information items had been collected from three first-class hospitals in the past five years to train the model. The result showed that the auxiliary medical system could make use of massive data. Its performance is continuously improved as the amount of data increases. Based on the system and collected data, statistics on the incidence of prostate cancer in the past five years were carried out. In the end, influence of diet habit and genetic inheritance for prostate cancer was analyzed. Results revealed the increasing prevalence of PCa and great negative impact caused by high-fat diet and genetic inheritance.

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“…This model differs from that of De Pillis and Radunskaya [23] by adding a dynamic equation related to stored fat [24]. Other studies have presented models regarding drug therapy which contribute to decision making and early cancer treatment [25]. In 2018, Alharbi and Rambely suggested dynamically that switching back to a healthy lifestyle boosted the immune system in terms of inhibiting or eliminating a moderately abnormal cell [26].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Simulation of the Immune System Response to Inhibit and Eliminate Abnormal Cells

Alharbi

Rambely

2019

Symmetry

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Diet has long been considered a risk factor related to an increased risk of cancer. This challenges us to understand the relationship between the immune system and diet when abnormal cells appear in a tissue. In this paper, we propose and analyze a model from the point of view of a person who follows a healthy diet, i.e., one correlated to the food pyramid, and a person who follows an unhealthy diet. Normal cells and immune cells are used in the design of the model, which aims to describe how the immune system functions when abnormal cells appear in a tissue. The results show that the immune system is able to inhibit and eliminate abnormal cells through the three following stages: the response stage, the interaction stage, and the recovery stage. Specifically, the failure of the immune system to accomplish the interaction stage occurs when a person follows an unhealthy diet. According to the analysis and simulation of our model, we can deduce that dietary pattern has a significant impact on the functioning of the immune system.

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Data Decision and Drug Therapy Based on Non-Small Cell Lung Cancer in a Big Data Medical System in Developing Countries

Cited by 20 publications

References 22 publications

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

Auxiliary Medical Decision System for Prostate Cancer Based on Ensemble Method

A Dynamic Simulation of the Immune System Response to Inhibit and Eliminate Abnormal Cells

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