DSAI-08 MACHINE LEARNING-BASED PREDICTION OF INPATIENT CARE COSTS FOR PATIENTS WITH BRAIN METASTASES: A CLAIMS-BASED ANALYSIS
Bibliographic record
Abstract
Abstract INTRODUCTION Brain metastases (BMs) are increasingly a cause of significant cancer-related morbidity, mortality, and economic burden. Predictive analytics providing BM-related risk-adjusted benefits and inpatient care costs (IPCC) are limited. This study sought to evaluate IPCCs and associated reimbursements for patients with BMs and perform predictive modelling through supervised machine learning (sML) using a contemporary, commercial US claims database. METHODS This retrospective study, reported following STROBE and TRIPOD+AI reporting guidelines, included adult patients (aged 18-65 years) in the United States (US) with BMs included in the MarketScan Commercial Claims and Encounters Database who received inpatient care during January 2016 to December 2021. Eligible patients were identified using ICD-10 codes. IPCC measures were adjusted using Medical Care component of 2023 Consumer Price Index. The primary study outcome was total IPCC, represented by adjusted gross total payments. After log-transformation performed due to data skew, sML was carried out using Random Forest algorithm with 1000 iterations with a 70:30 training-test data split, followed by residual analysis and evaluation of predictive performance using root-mean-squared error (RMSE), out-of-bag error (OOBE), and calibration curve. Multivariable linear regression was carried out for sensitivity analyses. RESULTS Total 25117 unique inpatient admissions across 13477 BM patients were included, with median (IQR) age of 57 years (51-61). Gross median (IQR) CPI-medical-care-adjusted values per admission were: total payments $28,119.51 (16847.45-55111.67); hospital payments $24,678.74 (14601.66-47864.84); and payments to principal physician $884.80 (441.07-2434.15), being 3.6% (IQR 1.9-6.8) of total payment. Median inpatient length-of-stay was 4 days (IQR 2-8). ML model had an OOBE of 0.61 and, using test data, RMSE of 0.85, indicating robust performance. No major deviations were found on residual analysis. CONCLUSIONS Contemporary IPCCs for BM patients in US are substantial but driven minimally by payments to principal physician. sML may be utilized to successfully predict IPCC for BM patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".