Data mining and machine learning in cancer survival research: An overview and future recommendations

Kaur, Ishleen; Doja, M. N.; Ahmad, Tanvir

doi:10.1016/j.jbi.2022.104026

Cited by 31 publications

(18 citation statements)

References 82 publications

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“…This makes it impossible to assess its accuracy using different estimated probabilities. In the future, data mining and machine learning based on big data will improve the ability to predict diseases and gradually change the medical decision-making process ( 48 ). However, over-fitting in the process of machine learning or deep learning limits its clinical applicability, and the relationship between risk factors and outcomes is difficult for patients to understand.…”

Section: Discussionmentioning

confidence: 99%

From past to future: Bibliometric analysis of global research productivity on nomogram (2000–2021)

Wang

Song

et al. 2022

Front. Public Health

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BackgroundNomogram, a visual clinical predictive model, provides a scientific basis for clinical decision making. Herein, we investigated 20 years of nomogram research responses, focusing on current and future trends and analytical challenges.MethodsWe mined data of scientific literature from the Core Collection of Web of Science, searching for the original articles with title “Nomogram*/Parton Table*/Parton Nomogram*”, published within January 1st, 2000 to December 30th, 2021. Data records were validated using HistCite Version and analyzed with a transformable statistical method, the Bibliometrix 3.0 package of R Studio.ResultsIn total, 4,176 original articles written by 19,158 authors were included from 915 sources. Annually, Nomogram publications are continually produced, which have rapidly grown since 2018. China published the most articles; however, its total citations ranked second after the United States. Both total citations and average article citations in the United States rank first globally, and a high degree of cooperation exists between countries. Frontiers in Oncology published the most papers (238); this number has grown rapidly since 2019. Journal of Urology had the highest H-index, with an average increase in publications over the past 20 years. Most research topics were tumor-related, among which tumor risk prediction and prognostic evaluation were the main contents. Research on prognostic assessment is more published and advanced, while risk prediction and diagnosis have good developmental prospects. Furthermore, nomogram of the urinary system has been highly developed. Following advancements in nomogram modeling, it has recently been applied to non-oncological subjects.ConclusionThis bibliometric analysis provides a comprehensive overview of the current nomogram status, which could enable better understanding of its development over the years, and provide global researchers a comprehensive analysis and structured information to help identify hot spots and gaps in future research.

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Section: Discussionmentioning

confidence: 99%

From past to future: Bibliometric analysis of global research productivity on nomogram (2000–2021)

Wang

Song

et al. 2022

Front. Public Health

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“…Kaur, Doja ve Ahmad [27], kanser hastaları üzerine yapmış olduğu çalışmada veri madenciliği ve makine öğrenme yöntemleri ile kanser hastalarının sağ kalım sürelerinin hesaplanmasına odaklanılmıştır. ABD merkezli SEER veri seti kullanılmıştır.…”

Section: Sağlık Sektöründe Veri Madenciliği Uygulama öRnekleriunclassified

“…Uygulanan yöntemlerin karışıklık matrisi ile doğruluğu test edilmiş ve yöntemlerin benzer sonuçlar verdiği görülmüştür. Meme kanseri hastaların yaşam süreleri tahmin edilmiştir [27].…”

Section: Sağlık Sektöründe Veri Madenciliği Uygulama öRnekleriunclassified

Sağlıkta veri kalitesi ve veri madenciliği uygulamaları

KOÇAK

ERGÜN

2023

Disiplinlerarası Yenilik Araştırmaları Dergisi

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Veri günümüzde çok sık karşılaşılan bir terimdir. Verinin doğru kullanımı doğru değerlendirmeyi sağlar. Bu da kaynakların verimli kullanımını, verilen hizmetin kalitesinin artmasını sağlamaktadır. Verinin en çok toplandığı alanların başında sağlık sektörü gelmektedir. Sağlık hizmet sunumunun maddi ve manevi yükü ağırdır. Bu hizmetin en iyi şekilde verilmesi, kaynakların doğru kullanılması ile yakın ilişkilidir. Sağlık verilerinden anlamlı sonuçların çıkarılarak hekimlere, hemşirelere ve sağlık yöneticileri gibi sağlık sektörü çalışanlarına yön gösterecek bilgilerin sağlanması sağlık verilerinin büyüklüğü düşünüldüğünde ancak veri madenciliği metotları ile mümkündür. Sağlık sektörünün insan hayatını direkt etkileyen bir doğası olması sebebi ile sağlıkta kullanılan verilerin kalitesinin en üst düzeyde olması beklenmektedir. Bu çalışmada veri kalitesini ve veri madenciliğini bütüncül olarak ele almıştır. Uygulama örnekleri aracılığıyla veri madenciliği ile sağlık sektöründe ne tür çalışmalar yapılabileceğine dair genel bir bakış açısı sağlanmıştır.

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“…Because deep learning makes it possible for machines to accurately simulate some aspects of human society and is useful for many complex recognition patterns, it has greatly contributed to the development of related fields such as artificial intelligence. In the last 30 years or so, research on this topic has attracted the close attention of many scholars, and significant progress has been made in areas such as natural language processing [ 20 ], image processing [ 21 ], data mining [ 22 ], and machine translation [ 23 ]. Figure 2 shows the difference in principle between deep learning and several other machine learning techniques.…”

Section: Related Theorymentioning

confidence: 99%

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

Feng

2022

Computational Intelligence and Neuroscience

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Currently, under the impact of the COVID-19, college students are facing increasingly elevated employment pressure and higher education pressure. This can easily cause a huge psychological burden on them, causing affective cognition problems such as anxiety and depression. In the long run, this is not conducive to students’ physical and mental health, nor is it conducive to the healthy development of the school and even the whole society. Therefore, it is imperative to build a novel adaptive affective cognition analysis model for college students. In particular, in the context of smart cities and smart China, many universities have opened the smart campus mode, which provides a huge data resource for our research. Due to problems of the low real-time evaluation and single data source in traditional questionnaire evaluation methods, evaluation errors are prone to occur, which in turn interferes with subsequent treatment. Therefore, for the purpose of alleviating the above deficiencies and improving the efficiency and accuracy of the affective cognition analysis model of college students, this paper studies the adaptive affective cognition analysis method of college students on basis of deep learning. First, because students’ psychological problems are often not sudden, on the contrary, most of these abnormalities will leave traces in their daily activities. Therefore, this paper constructs a multisource dataset with the access control data, network data, and learning data collected from the smart campus platform to describe the affective cognition status of students. Second, the multisource dataset is divided into two categories: image and text, and the CNN model is introduced to mine the psychological characteristics of college students, so as to provide a reference for the subsequent affective cognition state assessment. Finally, simulation tests are developed to confirm the viability of the technique suggested in this research. The experiments demonstrate that the accuracy of the assessment model is significantly increased because it can fully reflect the heterogeneity and comprehensiveness of the data. This also highlights that the new method has a wide range of potential applications in the modern campus setting and is also helpful in fostering the accuracy and depth of college students’ work on their affective cognition.

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Data mining and machine learning in cancer survival research: An overview and future recommendations

Cited by 31 publications

References 82 publications

From past to future: Bibliometric analysis of global research productivity on nomogram (2000–2021)

From past to future: Bibliometric analysis of global research productivity on nomogram (2000–2021)

Sağlıkta veri kalitesi ve veri madenciliği uygulamaları

A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features

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