Medical Informatics Research across 20 Years in China: A Structural Topic Modeling-based Analysis of Master’s Theses
Bibliographic record
Abstract
Abstract The establishment of the Discipline Development Consortium for Medical Informatics has ushered in a new phase of medical informatics (MI) research in China. Consequently, Chinese government, healthcare providers, and scholars, have increased their attention on the topic with the aim of improving patient care and healthcare delivery. The purpose of this study was to examine the research progress of medical informatics in China over the past 20 years using Master’s theses. Descriptive analysis was completed to identify the temporal distribution, spatial distribution, institutional distribution, specialty distribution, and advisor distribution, of the theses. A structural topic modeling-based analysis was performed to determine topic prevalence, topic correlation, associations between prolific institutions and topics, and topic trend. Our results reveal that the majority of institutions publishing theses on MI include universities with medical departments, medical universities, engineering universities, and research institutes. Most theses advisors focus on the field of medical informatics, while the sub-fields studied include software engineering, computer science, and biomedical engineering. The themes of theses can be divided into seven categories, including: electronic medical records and hospital informatics, Internet + medicine, and health information management and analysis, while new technologies, such as mHealth, Internet+, cloud computing, and big data, are growing in interest. Medical informatics in China should be established as an independent discipline to enhance research focus and to promote cross-institutional, cross-disciplinary, and cross-national collaboration between authors and institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".