Predicting symptom complexity: Using autoregressive integrated moving average (ARIMA) models to create responsive clinic scheduling.
Bibliographic record
Abstract
e13529 Background: Increasing cancer incidence, coupled with a trend in treating patients for longer periods of time, presents challenges in addressing all patients’ symptoms/concerns within the allotted time for ambulatory clinic appointments. Consequently, the ability to forecast and monitor the percentage of cancer patients with different symptom complexity levels is extremely valuable. Symptom complexity is a summary score that weighs the severity of all patient reported symptom scores at one time point. If a clinic could predict how many patients may need more time due to complex symptom management needs, clinic-scheduling templates could be adjusted to include a set number of longer appointments. Methods: Auto Regressive Integrated Moving Average (ARIMA) models were utilized to forecast the percentage of patients with a high symptom complexity level within one cancer clinic in Alberta, Canada. Goodness-of-fit measures such as Bayesian information criterion (BIC) and Ljung-Box test were used to determine optimal form for the ARIMA model. Following model selection, the autocorrelation function (ACF) was performed. These tests together verified that chosen AR, MA and differencing (I) were appropriate. Model performance on the historical data for model fit was summarized by Mean Absolute Error (MAE) and Root Squared Mean Error (RSME). Forecasting accuracy was assessed using mean absolute prediction error by comparing the forecasts with actual clinic data. Results: Of the multiple model structures tested, ARIMA (0, 0, 1) was selected, with the lowest BIC and non-significant Ljung-Box test. We obtained forecasts of the percentage of patients with high symptom complexity levels, with an MAE at 4.0%. To assess forecast accuracy, we calculated the absolute prediction error by comparing the forecasted percentages of patients with high symptom complexity levels to actual clinic visit data and the mean absolute prediction error was 5.9%. Conclusions: This forecasting model has important implications, allowing clinics to adjust scheduling templates to provide a select number of longer timeslots and therefore, be better prepared to meet the symptom management needs of cancer patients who are considered highly complex. This model could be applied to other clinical populations to allow for a tailored scheduling approach based on each clinic’s symptom complexity forecasting.
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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 | 0.008 |
| 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 | 0.000 |
| 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".