Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer
Bibliographic record
Abstract
Importance: Patient-reported symptom burden was recently found to be associated with emergency department use and unplanned hospitalization (ED/Hosp) in patients with head and neck cancer. It was hypothesized that symptom scores could be combined with administrative health data to accurately risk stratify patients. Objective: To develop and validate a machine learning approach to predict future ED/Hosp in patients with head and neck cancer. Design, Setting, and Participants: This was a population-based predictive modeling study of patients in Ontario, Canada, diagnosed with head and neck cancer from January 2007 through March 2018. All outpatient clinical encounters were identified. Edmonton Symptom Assessment System (ESAS) scores and clinical and demographic factors were abstracted. Training and test cohorts were randomly generated in a 4:1 ratio. Various machine learning algorithms were explored, including (1) logistic regression using a least absolute shrinkage and selection operator, (2) random forest, (3) gradient boosting machine, (4) k-nearest neighbors, and (5) an artificial neural network. Data analysis was performed from September 2021 to January 2022. Main Outcomes and Measures: The main outcome was any 14-day ED/Hosp event following symptom assessment. The performance of each model was assessed on the test cohort using the area under the receiver operator characteristic (AUROC) curve and calibration plots. Shapley values were used to identify the variables with greatest contribution to the model. Results: The training cohort consisted of 9409 patients (mean [SD] age, 63.3 [10.9] years) undergoing 59 089 symptom assessments (80%). The remaining 2352 patients (mean [SD] age, 63.3 [11] years) and 14 193 symptom assessments were set aside as the test cohort (20%). Several models had high predictive accuracy, particularly the gradient boosting machine (validation AUROC, 0.80 [95% CI, 0.78-0.81]). A Youden-based cutoff corresponded to a validation sensitivity of 0.77 and specificity of 0.66. Patient-reported symptom scores were consistently identified as being the most predictive features within models. A second model built only with symptom severity data had an AUROC of 0.72 (95% CI, 0.70-0.74). Conclusions and Relevance: In this study, machine learning approaches predicted with a high degree of accuracy ED/Hosp in patients with head and neck cancer. These tools could be used to accurately risk stratify patients and may help direct targeted intervention.
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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 | 0.009 | 0.017 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".