Combination of conditional random field with a rule based method in the extraction of PICO elements
Bibliographic record
Abstract
BACKGROUND: Extracting primary care information in terms of Patient/Problem, Intervention, Comparison and Outcome, known as PICO elements, is difficult as the volume of medical information expands and the health semantics is complex to capture it from unstructured information. The combination of the machine learning methods (MLMs) with rule based methods (RBMs) could facilitate and improve the PICO extraction. This paper studies the PICO elements extraction methods. The goal is to combine the MLMs with the RBMs to extract PICO elements in medical papers to facilitate answering clinical questions formulated with the PICO framework. METHODS: First, we analyze the aspects of the MLM model that influence the quality of the PICO elements extraction. Secondly, we combine the MLM approach with the RBMs in order to improve the PICO elements retrieval process. To conduct our experiments, we use a corpus of 1000 abstracts. RESULTS: We obtain an F-score of 80% for P element, 64% for the I element and 92% for the O element. Given the nature of the used training corpus where P and I elements represent respectively only 6.5 and 5.8% of total sentences, the results are competitive with previously published ones. CONCLUSIONS: Our study of the PICO element extraction shows that the task is very challenging. The MLMs tend to have an acceptable precision rate but they have a low recall rate when the corpus is not representative. The RBMs backed up the MLMs to increase the recall rate and consequently the combination of the two methods gave better results.
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.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".