An intelligent machine learning model for real-time early detection of undesirable cancer events: AIM2REDUCE.
Bibliographic record
Abstract
1557 Background: Cancer and its treatment cause undesirable cancer events (UCEs). Automated warning systems could reduce the frequency and severity of UCEs by alerting the healthcare team and allocating preventative interventions. Most previous studies predicted single UCEs at the initiation of treatment. In AIM2REDUCE, we developed and evaluated a general-purpose system for predicting UCEs during outpatient systemic anti-cancer therapy. Methods: Each time a patient receives treatment, AIM2REDUCE applies machine learning to the preceding data in the electronic medical record (EMR) to predict future UCEs. We identified patients treated for aerodigestive cancers from the EMR at Princess Margaret Cancer Centre, who were randomly split into development, validation, and test cohorts. Features included cancer diagnosis, treatment sessions with doses, laboratory tests, and patient-reported symptoms. UCEs are listed in the Table. We trained LASSO regression and random forests models in the training cohort and tuned hyperparameters in the validation cohort using Bayesian optimization. We evaluated performance across discrimination, calibration, and net benefit in the test cohort. Results: The cohort included 5,760 patients who received 175,565 treatment sessions, with 13,612,746 unique data points across 102 features. Of these patients, 2,352 (40.8%) were female, the median age was 64.0 years (interquartile range 14.0), the most common diagnoses were lung cancer (2,071, 36.0%) and pancreatic cancer (926, 16.1%), and the most common treatment regimens were weekly gemcitabine (433, 7.5%) and maintenance pemetrexed (417, 7.2%). The Table shows the performance of AIM2REDUCE. Conclusions: We demonstrate that longitudinal machine learning systems trained using EMR data can accurately predict a wide range of UCEs. Based on these results, automated warning systems should be implemented and evaluated in real-time clinical practice. [Table: see text]
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".