| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 241 | 85 | 124 | 23 |
| Medicine | 1178 | 123 | 119 | 20 |
The journal is mainly concerned with subjects like Artificial intelligence, Health care, Machine learning, Natural language processing and Medical emergency. Research on Artificial intelligence addressed in it frequently intersections with the field of Receiver operating characteristic. The journal explores issues in Receiver operating characteristic which can be linked to other research areas like Logistic regression and Retrospective cohort study.
The main emphasis of it is the research on Health care, emphasizing the topic of Health informatics. Studies on Health informatics discussed in the journal link to the field of Data science. Support vector machine is a key component of Machine learning research discussed in it.
Information extraction is the primary subject of Natural language processing works presented in it. Medical emergency study tackled is connected to the field of Clinical decision support system. The Deep learning study featured in it draws parallels with the field of Convolutional neural network.
The journal publications investigate areas of study like Health care, Artificial intelligence, Nursing, Health information technology and Medical emergency. While the most cited papers focused on Health care, they were also able to explore topics like Context (language use), Information technology, CINAHL and Family medicine. While Artificial intelligence is the focus of the most cited articles, it also provides insights into the studies of Medical diagnosis, Medical record, Natural language processing, Electronic health record and Machine learning.
The journal generally zeroes in on subjects such as Artificial intelligence, Health care, Machine learning, Receiver operating characteristic and Deep learning. The concepts on Artificial intelligence presented in it can also apply to other research fields, including Retrospective cohort study and Natural language processing. In addition to Health care research, JMIR medical informatics aims to explore topics under Context (language use), Medical education, Medical emergency, Usability and Interoperability.
The study on Machine learning presented is investigated in conjunction with research in Preprint. Receiver operating characteristic research presented in JMIR medical informatics encompasses a variety of subjects, including Algorithm and Logistic regression. JMIR medical informatics holds forums on Logistic regression that merges themes from other disciplines such as Random forest and Emergency medicine.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in JMIR medical informatics (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in JMIR medical informatics (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 12.79% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 16.89% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.33% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.22% of all publications and 59.56% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Given the rise of Artificial Intelligence, Machine Learning, and Natural Language Processing in fields like healthcare, those who consider exploring related careers have multiple paths available. One area that fields such as these overlap is Health Informatics, which combines healthcare, information technology, and management. Such a career involves tasks like recording electronic health records, interpreting data to boost patient care, aiding in medical decision-making processes, and developing health IT solutions.
With the wealth of research topics detailed in this article, primarily in Health Care and Artificial Intelligence it offers valuable insights for potential career paths. For instance, high school teachers who have specialized in history, could use this kind of medical technology knowledge to guide their students who consider a career in the health sector. For those who are willing to take it a step further, they might even choose to specialize in this area, putting a unique spin on their teaching career. If you are interested in pursuing this path and want to learn more, consider reading this guide on how to become a high school history teacher in Washington.
Moreover, opportunities abound not only in academia but in hospitals, research facilities, government agencies, and health-related software companies. When exploring potential career paths, remember to consider your skills, passion, and how they align with the research topics discovered in this article. The expansion of technology in healthcare opens doors wide for careers in Health Informatics.
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