Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection
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
Machine Learning (ML) and Deep Learning (DL) are derivatives of Artificial Intelligence (AI) that have already demonstrated their effectiveness in a variety of domains, including healthcare, where they are now routinely integrated into patients’ daily activities. On the other hand, data heterogeneity has long been a key obstacle in AI, ML and DL. Here, Multimodal Machine Learning (Multimodal ML) has emerged as a method that enables the training of complex ML and DL models that use heterogeneous data in their learning process. In addition, Multimodal ML enables the integration of multiple models in the search for a single, comprehensive solution to a complex problem. In this review, the technical aspects of Multimodal ML are discussed, including a definition of the technology and its technical underpinnings, especially data fusion. It also outlines the differences between this technology and others, such as Ensemble Learning, as well as the various workflows that can be followed in Multimodal ML. In addition, this article examines in depth the use of Multimodal ML in the detection and prediction of Cardiovascular Diseases, highlighting the results obtained so far and the possible starting points for improving its use in the aforementioned field. Finally, a number of the most common problems hindering the development of this technology and potential solutions that could be pursued in future studies are outlined.
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 | 0.001 | 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 | 0.001 | 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