جستجوی مقالات مرتبط با کلیدواژه "text mining" در نشریات گروه "پزشکی"

After a cancer diagnosis, the most important thing is to determine the stage and grade of the cancer. Pathology reports are the main source for cancer staging, but they do not contain all the information needed for the staging. However, the text of these reports is sometimes the only available information. We were interested in knowing whether text mining methods can be used to predict staging only from pathology reports.

A total of 698 pathology reports of breast cancer cases and their TNM staging collected from multiple centers in West Azerbaijan Province, Iran were used for this study. After preparing the semi-structured reports, the texts of the reports were imported into a program written by Python V3. Three machine learning algorithms of Logistic Regression, SVM, and Naïve Bayes and a simple pipeline were used for the purpose of text mining. The performance of the algorithms was evaluated in terms of accuracy, precision, recall, and F1 score.

The Naïve Bayes algorithm achieved excellent results and a value rate of higher than 91% in all evaluation criteria (accuracy, precision, recall and F1 score). This means that the Naïve Bayes algorithm could classify the reports with high efficiency and its predictions were more correct than the other two algorithms. Naïve Bayes also outperformed SVM and Logistic Regression in terms of accuracy, recall and F1 score. In addition, Naïve-Bayes showed faster inference due to its simplicity and lower computational and training time.

We suggest using the proposed design in this study for predicting breast cancer staging, where there is a need but not all necessary information except pathology reports. This method may not be a useful for clinical management of cancer patients, but it can be safely used for epidemiological estimations.

 The results from the analysis of COVID-19 literature by employing text-mining techniques are of particular importance for researchers, policymakers, and planners of medical sciences at the national and international levels, avoiding parallel research and waste of time and budget. The paper explore emerging topics and the trend of scientific words at the national and international levels in the subject area of COVID-19.

 This applied research was conducted by employing the text-mining and its related algorithms and classifying texts. The population consists of all COVID-19 articles indexed in PubMed Central® (PMC). The number of records retrieved was 160,862 items until June 10, 2021. Among these, 3143 national and 157,719 international COVID-19 articles. Python and its related libraries were applied. The most significant words were also identified and reported based on TF-IDF weighting. Emerging topics were identified according to the weighted average growth.

"COVID", "infect", and "cell" were among the most important words used in international COVID-19 articles. In addition, the most important words in the national COVID-19 articles were "patient", "SARS-Cov", and "COVID".

 Among the most important conclusions that can be inferred from the trend of word change used in the COVID-19 literature is that the most significant words in international literature differ significantly from those in national literature, as international research focuses on COVID-19 and the infections caused by it. In contrast, national research focuses on COVID-19 and patients. Another significant result is the annual word-changing national and international literature.

 Due to the increased publication of articles in various scientific fields, analyzing the published topics in specialized journals is important and necessary.

 This research has identified the published topics in global publications in the health information technology (HIT) field.

 This study analyzed articles in the field of HIT using text-mining techniques. For this purpose, 162,994 documents were extracted from PubMed and Scopus databases from 2000 to 2019 using the appropriate search strategy. Text mining techniques and the Latent Dirichlet Allocation (LDA) topic modeling algorithm were used to identify the published topics. Python programming language has also been used to run text-mining algorithms.

 This study categorized the subject of HIT-related published articles into 16 topics, the most important of which were Telemedicine and telehealth, Adoption of HIT, Radiotherapy planning techniques, Medical image analysis, and Evidence-based medicine.

 The results of the trends of subjects of HIT-related published articles represented the thematic extent and the interdisciplinary nature of this field. The publication of various topics in this scientific field has shown a growing trend in recent years.

Type 2 Diabetes Mellitus (T2DM) has emerged as a major threat to global health that fosters life-threatening clinical complications, taking a huge toll on our society. More than 65 million Indians suffer from T2DM, making it one of the leading causes of death. T2DM and associated complications have to be constantly monitored and managed which reduces the overall quality of life and increases socioeconomic burden. Therefore, it is crucial to develop specific treatment and management strategies. In order to achieve this, it is essential to understand the underlying genetic causes and molecular mechanisms.

Integrated gene network and ontology analyses facilitate prioritization of plausible candidate genes for T2DM and also aid in understanding their mechanistic pathways. In this study, T2DM-associated genes were subjected to sequential interaction network and gene set enrichment analysis. High ranking network clusters were derived and their interrelation with pathways was assessed.

About 23 significant candidate genes were prioritized from 615 T2DM-associated genes which were overrepresented in pathways related to insulin resistance, type 2 diabetes, signaling cascades such as insulin receptor signaling pathway, PI3K signaling, IGFR signaling pathway, ERBB signaling pathway, MAPK signaling pathway and their regulatory mechanisms.

Of these, two tyrosine kinase receptor genes-EGFR and IGF1R were identified as common nodes and can be considered to be significant candidate genes in T2DM.

Context:

 Hospitals are large information organizations with the main goal of providing high-quality, integrated, and cost-effective healthcare. This goal is more easily realized through well-designed Hospital Information Systems [HIS].

Evidence Acquisition:

In this narrativereview study, 98 articles were extracted from Science Direct, PubMed, and Google Scholar databasesusing "Hospital Information System"keyword.The articles werepublished between1980 and2018. After examining the quality of the articles in terms of research design and references, 41 articles remained for analysis. Relevant e-books and print books were also examined, and the features and services of HIS were investigated.

For HIS, seven features, namelythe coverage of differenttypes of data, integration of subsystems, having an enterprise metamodel, communication with other information systems, coverage of hospital units, adherence to standards, and connectivity to digital instruments were obtained. Moreover, 18 servicesofpatient management, economic management and cost reduction, legal management of data, treatment management, administrative management, presenting information based on policies, clinical decision support, managerial-administrative decision support, educationalsupport, research support, electronic medical record generation, text mining, encoding, documentation quality improvement, medical support, resource utilization management, personnel management, and warehouse management were determined.

To evaluate HIS, it is necessary to determine its features and services. Based on the features and services of HIS, itsevaluation tool has been developedin this study

Nowadays, due to the increased publication of articles in various scientific fields, identifying the publishing trend and emerging keywords in the texts of these articles is essential. Thus, the present study has identified and analyzed the keywords used in published articles on medical librarianship and information.

In the present investigation, an exploratory and descriptive approach has been used to analyze librarianship and information articles published in specialized journals in this field from 1964 to 2019 by applying text mining techniques. The TF-IDF weighting algorithm has been applied to identify the most important keywords used in the articles. Python programming language has also been used to implement text mining algorithms.

The results obtained from the TF-IDF algorithm indicate that the words “library”, “patient”, and “inform” with the weights of 95.087, 65.796, and 63.386, respectively, were the most important keywords in the published articles on medical librarianship and information. Also, the words “Catalog”, “Book” and “Journal” were the most important keywords used in the articles published between the years 1960 and 1970, and the words “Patient”, “Bookstore” and “Intervent” were the most important keyword used in articles on medical librarianship and information published from 2015 to 2020. The words “Blockchain”, “telerehabilit”, “Instagram”, “WeChat”, and “comic” are new keywords observed in articles on medical librarianship and information between 2015 and 2020.

The results of the present study have revealed that the keywords used in articles on medical librarianship and information have not been consistent over time and have undergone an alteration at different periods so that nowadays, this field of science has also changed following the needs of society with the advent and growth of information technologies.

The existence of an intellectual structure for every field is essential for managers and scholars. Intellectual structures provide a comprehensive map of knowledge that can guide researchers and managers to have a better view of their fields. Besides, with high-speed and massive amounts of data and information generation, reading and surveying of all resources are severely tricky. Intellectual maps solve this problem and make a situation for control and monitoring this voluminous and high-speed generated data. Epidemiology is regarded as one of the exciting fields which many researchers focused on it. A study of the structure and criteria of different epidemiological fields has not been done yet. Indeed, there is no serious effort for knowledge discovery of hidden information on epidemiological texts.

In this paper, in order to survey this field, an intellectual structure is provided using co-word analysis. Utilizing co-word analysis discloses relationships and structure among research subjects and topics in a field.

Finally, four main clusters were determined, namely: genetic (with 30.53% of surveyed papers), illness (29.47%), modeling (23.16%), and prevention (16.84%).

According to epidemiology co-word network, epidemiology area has not been studied from enough different areas, especially from novel technologies

Performance is a multi-dimensional and dynamic concept. During the past 2 decades, considerable studies were performed in developing the hospital performance concept. To know literature key concepts on hospital performance, the knowledge visualization based on co-word analysis and social network analysis has been used.
Documents were identified through “PubMed” searching from1945 to 2014 and 2350 papers entered the study after omitting unrelated articles, the duplicates, and articles without abstract. After pre-processing and preparing articles, the key words were extracted and terms were weighted by TF-IDF weighting schema. Support as an interestingness measure, which considers the co-occurrence of the extracted keywords and "hospital performance" phrase was calculated. Keywords having high support with "hospital performance" are selected. Term-term matrix of these selected keywords is calculated and the graph is extracted.
The most high frequency words after “Hospital Performance” were “mortality” and “efficiency”. The major knowledge structure of hospital performance literature during these years shows that the keyword “mortality” had the highest support with hospital performance followed by “quality of care”, “quality improvement”, “discharge”, “length of stay” and “clinical outcome”. The strongest relationship is seen between “electronic medical record” and “readmission rate”.
Some dimensions of hospital performance are more important such as “efficiency”, “effectiveness”, “quality” and “safety” and some indicators are more highlighted such as “mortality”, “length of stay”, “readmission rate” and “patient satisfaction”. In the last decade, some concepts became more significant in hospital performance literature such as “mortality”, “quality of care” and “quality improvement”.

The purpose of this paper is to propose a novel intelligent model for AIDS/HIV data based on expert system and using it for developing an intelligent medical consulting system for AIDS/HIV.

In this descriptive research, 752 frequently asked questions (FAQs) about AIDS/HIV are gathered from numerous websites about this disease. To perform the data mining and extracting the intelligent model, the 6 stages of Crisp method has been completed for FAQs. The 6 stages include: Business understanding, data understanding, data preparation, modelling, evaluation and deployment. C5.0 Tree classification algorithm is used for modelling. Also, rational unified process (RUP) is used to develop the web‑based medical consulting software. Stages of RUP are as follows: Inception, elaboration, construction and transition. The intelligent developed model has been used in the infrastructure of the software and based on client’s inquiry and keywords related FAQs are displayed to the client, according to the rank. FAQs’ ranks are gradually determined considering clients reading it. Based on displayed FAQs, test and entertainment links are also displayed.

The accuracy of the AIDS/HIV intelligent web‑based medical consulting system is estimated to be 78.76%.

AIDS/HIV medical consulting systems have been developed using intelligent infrastructure. Being equipped with an intelligent model, providing consulting services on systematic textual data and providing side services based on client’s activities causes the implemented system to be unique. The research has been approved by Iranian Ministry of Health and Medical Education for being practical.