Applications of machine learning to electronic health record data in liver-related disease
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
Electronic Health Records (EHRs) has gained its increasing significance in modern healthcare as its promising prospects in the application of machine learning. The accumulation of vast clinical data holds potential for repurposing in clinical research such as prediction, diagnosis and prognosis. However, the collection and preparation of EHR data present challenges, primarily due to the inherent incompleteness of the data and the associated privacy and security concerns. To address the issue, text-mining tools based on domain-specific lexicons, data sharing and multiple de-identification methods have been suggested. In terms of methodologies, various machine learning models and algorithms used in EHR data analysis are analyzed, including logistic regression, decision trees, random forests, and natural language processing, each with its unique application scenarios in the healthcare domain. Liver related diseases, including HAV, HBV, HCV and especially Liver Cancer, has affected hundreds of millions of people around the world. The incidence and mortality rates for these diseases are still rising continually. With recent advancements of Machine Learning techniques, such as the attention mechanism and BERT-based embedding, which have shown exceptional results in EHR analysis when applying to liver diseases. While EHRs offer a treasure trove of data for clinical research, the challenges associated with their collection, processing, and analysis cannot be ignored. It underscores the need for robust methodologies and tools to harness the full potential of EHRs while ensuring data integrity and patient privacy. In this paper, we will gather and review the existing application in the realm of liver-related diseases.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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