Combining simulation and machine learning in healthcare: a literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Healthcare is a complex and intricate system where multiple factors interact to affect outcomes. Accordingly, simulation is a key tool to help healthcare researchers account for this complexity and explore “what if” scenarios. Similarly, machine learning is gaining popularity in healthcare as it can also account for this complexity and offers the potential to solve problems that are intractable for traditional methods. Given that both methods have conceptually similar objectives (both predict system responses), it begs a series of questions: Can they be used together to solve healthcare challenges and, if so, how can they be incorporated? What benefits and inspiration can such a combination bring to healthcare? This paper reviews the literature to help address these questions. First, the literature is broadly categorized into six types based on how they combined simulation and machine learning. Each type is then discussed and identified research gaps are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it