Improving Clinical Decision Making with a Two-Stage Recommender System: A Case Study on MIMIC-III Dataset
Bibliographic record
Abstract
Abstract Clinical decision-making is a challenging and time-consuming task that involves integrating a vast amount of patient data, including medical history, test results, and notes from clinicians. To assist this process, clinical recommender systems have been developed to provide personalized recommendations to healthcare practitioners. However, creating effective clinical recommender systems is complex due to the diversity and intricacy of clinical data and the need for customized recommendations. In this paper, we propose a two-stage recommender framework for clinical decision-making basedon the publicly available MIMIC dataset of electronic health records. The first stage of the framework employs a deep neural networkbased model to retrieve a set of candidate items, such as diagnosis, medication, and prescriptions, from the patient’s electronic health records. The model is trained to extract relevant information from clinical notes using a pre-trained language model. The second stage of the framework utilizes a deep learning model to rank and recommend the most pertinent items to healthcare providers. The model considers the patient’s medical history and the context of the current visit to offer personalized recommendations. To evaluate the proposed model, we compared it to various baseline models using multiple evaluation metrics. The findings indicate that the proposed model achieved a precision of 89% and a macro-average F1 score of approximately 84%, indicating its potential to improve clinical decision-making and reduce information overload for healthcare providers. The paper also discusses challenges, such as data availability, privacy, and bias, and suggests areas for future research in this field.
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 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".