scholarly journals Characterizing Artificial Intelligence Applications in Cancer Research: A Latent Dirichlet Allocation Analysis

10.2196/14401 ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. e14401 ◽  
Author(s):  
Bach Xuan Tran ◽  
Carl A Latkin ◽  
Noha Sharafeldin ◽  
Katherina Nguyen ◽  
Giang Thu Vu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Research ◽  
Cancer Care ◽  
Latent Dirichlet Allocation ◽  
Similarity Index ◽  
Cancer Control ◽  
Clinical Practices ◽  
Patient Reported ◽  
Dirichlet Allocation

Background Artificial intelligence (AI)–based therapeutics, devices, and systems are vital innovations in cancer control; particularly, they allow for diagnosis, screening, precise estimation of survival, informing therapy selection, and scaling up treatment services in a timely manner. Objective The aim of this study was to analyze the global trends, patterns, and development of interdisciplinary landscapes in AI and cancer research. Methods An exploratory factor analysis was conducted to identify research domains emerging from abstract contents. The Jaccard similarity index was utilized to identify the most frequently co-occurring terms. Latent Dirichlet Allocation was used for classifying papers into corresponding topics. Results From 1991 to 2018, the number of studies examining the application of AI in cancer care has grown to 3555 papers covering therapeutics, capacities, and factors associated with outcomes. Topics with the highest volume of publications include (1) machine learning, (2) comparative effectiveness evaluation of AI-assisted medical therapies, and (3) AI-based prediction. Noticeably, this classification has revealed topics examining the incremental effectiveness of AI applications, the quality of life, and functioning of patients receiving these innovations. The growing research productivity and expansion of multidisciplinary approaches are largely driven by machine learning, artificial neural networks, and AI in various clinical practices. Conclusions The research landscapes show that the development of AI in cancer care is focused on not only improving prediction in cancer screening and AI-assisted therapeutics but also on improving other corresponding areas such as precision and personalized medicine and patient-reported outcomes.

scholarly journals Characterizing Artificial Intelligence Applications in Cancer Research using Latent Dirichlet Allocation (Preprint)

2019 ◽  
Author(s):  
Bach Xuan Tran ◽  
Carl A. Latkin ◽  
Noha Sharafeldin ◽  
Katherina Nguyen ◽  
Giang Thu Vu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Research ◽  
Latent Dirichlet Allocation ◽  
Similarity Index ◽  
Cancer Control ◽  
Clinical Practices ◽  
Global Trends ◽  
Patient Reported ◽  
Dirichlet Allocation

BACKGROUND Artificial Intelligence (AI) - based therapeutics, devices and systems are vital innovations in cancer control. OBJECTIVE This study analyzes the global trends, patterns, and development of interdisciplinary landscapes in AI and cancer research. METHODS Exploratory factor analysis was applied to identify research domains emerging from contents of the abstracts. Jaccard’s similarity index was utilized to identify terms most frequently co-occurring with each other. Latent Dirichlet Allocation was used for classifying papers into corresponding topics. RESULTS The number of studies applying AI to cancer during 1991-2018 has been grown with 3,555 papers covering therapeutics, capacities, and factors associated with outcomes. Topics with the highest volumes of publications include 1) Machine learning, 2) Comparative Effectiveness Evaluation of AI-assisted medical therapies, 3) AI-based Prediction. Noticeably, this classification has revealed topics examining the incremental effectiveness of AI applications, the quality of life and functioning of patients receiving these innovations. The growing research productivity and expansion of multidisciplinary approaches, largely driven by machine learning, artificial neutral network, and artificial intelligence in various clinical practices. CONCLUSIONS The research landscapes show that the development of AI in cancer is focused not only on improving prediction in cancer screening and AI-assisted therapeutics, but also other corresponding areas such as Precision and Personalized Medicine and patient-reported outcomes.

scholarly journals Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study

10.2196/15511 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e15511 ◽  
Author(s):  
Bach Xuan Tran ◽  
Son Nghiem ◽  
Oz Sahin ◽  
Tuan Manh Vu ◽  
Giang Hai Ha ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Precision Medicine ◽  
Data Science ◽  
Latent Dirichlet Allocation ◽  
Developed Countries ◽  
Theory And Practice ◽  
Clinical Practices ◽  
Research Topics ◽  
Dirichlet Allocation

Background Artificial intelligence (AI)–based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. Objective This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. Methods We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. Results The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. Conclusions The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

scholarly journals A Latent Dirichlet Allocation and Fuzzy Clustering Based Machine Learning Model for Text Thesaurus

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Jia Luo ◽  
Dongwen Yu ◽  
Zong Dai
Keyword(s):  
Machine Learning ◽  
Fuzzy Clustering ◽  
Latent Dirichlet Allocation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Text Data ◽  
Huge Data ◽  
Machine Learning Model ◽  
N Gram ◽  
Dirichlet Allocation

It is not quite possible to use manual methods to process the huge amount of structured and semi-structured data. This study aims to solve the problem of processing huge data through machine learning algorithms. We collected the text data of the company’s public opinion through crawlers, and use Latent Dirichlet Allocation (LDA) algorithm to extract the keywords of the text, and uses fuzzy clustering to cluster the keywords to form different topics. The topic keywords will be used as a seed dictionary for new word discovery. In order to verify the efficiency of machine learning in new word discovery, algorithms based on association rules, N-Gram, PMI, andWord2vec were used for comparative testing of new word discovery. The experimental results show that the Word2vec algorithm based on machine learning model has the highest accuracy, recall and F-value indicators.

scholarly journals Modern Probabilistic Model: Filtering Massive Data in E-learning

2021 ◽  
pp. 52-58
Author(s):  
Hachem Harouni Alaoui ◽  
Elkaber Hachem ◽  
Cherif Ziti
Keyword(s):  
Machine Learning ◽  
Probabilistic Model ◽  
Latent Dirichlet Allocation ◽  
Web Pages ◽  
Massive Data ◽  
Learning Session ◽  
Learning Procedure ◽  
E Learning ◽  
Dirichlet Allocation

So muchinformation keeps on being digitized and stored in several forms, web pages, scientific articles, books, etc. so the mission of discovering information has become more and more challenging. The requirement for new IT devices to retrieve and arrange these vastamounts of informationaregrowing step by step. Furthermore, platforms of e-learning are developing to meet the intended needsof students.The aim of this article is to utilize machine learning to determine the appropriate actions that support the learning procedure and the Latent Dirichlet Allocation (LDA) so as to find the topics contained in the connections proposed in a learning session. Ourpurpose is also to introduce a course which moves toward the student's attempts and which reduces the unimportant recommendations (Which aren’t proper to the need of the student grown-up) through the modeling algorithms of the subjects.

scholarly journals Applying Text Mining, Clustering Analysis, and Latent Dirichlet Allocation Techniques for Topic Classification of Environmental Education Journals

2021 ◽  
Vol 13 (19) ◽  
pp. 10856
Author(s):  
I-Cheng Chang ◽  
Tai-Kuei Yu ◽  
Yu-Jie Chang ◽  
Tai-Yi Yu
Keyword(s):  
Artificial Intelligence ◽  
Cluster Analysis ◽  
Text Mining ◽  
Environmental Education ◽  
Hierarchical Clustering ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Word Analysis ◽  
Dirichlet Allocation

Facing the big data wave, this study applied artificial intelligence to cite knowledge and find a feasible process to play a crucial role in supplying innovative value in environmental education. Intelligence agents of artificial intelligence and natural language processing (NLP) are two key areas leading the trend in artificial intelligence; this research adopted NLP to analyze the research topics of environmental education research journals in the Web of Science (WoS) database during 2011–2020 and interpret the categories and characteristics of abstracts for environmental education papers. The corpus data were selected from abstracts and keywords of research journal papers, which were analyzed with text mining, cluster analysis, latent Dirichlet allocation (LDA), and co-word analysis methods. The decisions regarding the classification of feature words were determined and reviewed by domain experts, and the associated TF-IDF weights were calculated for the following cluster analysis, which involved a combination of hierarchical clustering and K-means analysis. The hierarchical clustering and LDA decided the number of required categories as seven, and the K-means cluster analysis classified the overall documents into seven categories. This study utilized co-word analysis to check the suitability of the K-means classification, analyzed the terms with high TF-IDF wights for distinct K-means groups, and examined the terms for different topics with the LDA technique. A comparison of the results demonstrated that most categories that were recognized with K-means and LDA methods were the same and shared similar words; however, two categories had slight differences. The involvement of field experts assisted with the consistency and correctness of the classified topics and documents.

scholarly journals The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis

2019 ◽  
Vol 16 (12) ◽  
pp. 2150 ◽  
Author(s):  
Bach Xuan Tran ◽  
Roger S. McIntyre ◽  
Carl A. Latkin ◽  
Hai Thanh Phan ◽  
Giang Thu Vu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Bibliometric Analysis ◽  
Exploratory Factor Analysis ◽  
Depressive Disorders ◽  
Latent Dirichlet Allocation ◽  
Rapid Development ◽  
The United States ◽  
Research Network ◽  
Dirichlet Allocation

Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.

Cancer patient experience: A pilot survey in Japan.

2014 ◽  
Vol 32 (30_suppl) ◽  
pp. 260-260
Author(s):  
Izumi Kamiya ◽  
Ayako Okuyama ◽  
Kayoko Katayama ◽  
Natsumi Yamashita ◽  
Keizo Akuta ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Experience ◽  
Emotional Support ◽  
Cancer Care ◽  
Medical Staff ◽  
Pain Control ◽  
Cancer Control ◽  
Treatment Preferences ◽  
Inpatient Setting ◽  
Patient Reported

260 Background: Patient-reported experiences of cancer care are an important outcome of cancer control programs. To establish a nation-wide system to monitor progress in cancer control policies, we piloted a patient experience survey to six hospitals in Japan. Methods: We conducted a self-administered questionnaire survey to a total of 1,804 adult cancer patients receiving cancer treatment in six hospitals (three cancer centers, two general hospitals, and one academic institution) from July 2013 to Mar 2014. Patients were asked to answer 94 questions covering eight dimensions of cancer experience: 1) decision-making, 2) care coordination, 3) patient education, 4) pain control, 5) emotional support, 6) family support, 7) access to care, and 8) care continuity. Results: Eighty percent of the patients reported that their treatment preferences were respected in the decision-making process, but a large proportion of patients (60%) also noted that they preferred to have their treatment decisions made for them by their physicians. Many (32%) expressed difficulty in communicating their questions and concerns to their physicians at the time of diagnosis. Only one fifth of patients were informed at the time of diagnosis that they can seek for a second opinion from other providers. Average patient-reported wait time to surgery was 30 days, which was considered to be long by a third of the patients. Eighty percent of patients felt that their care was well-coordinated by a multidisciplinary team, while % also felt that they received adequate emotional support from their medical staff. Relatively small proportion of outpatients (77%) felt that they had access to medical staff when they had medical questions, compared to nearly all patients in an inpatient setting. Only 65% of inpatients and 40% of outpatients felt that they had received best available pain control during their care. Less than half of the patients were able to communicate their preferred place of care after discharge with their healthcare provider. Conclusions: Patient-reported experiences of cancer care are an important outcome measure of cancer policy performance. This pilot study served to reveal some of the important on in future nationwide surveys.

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