Search

Chemotherapy remains a cornerstone of cancer treatment, but it is frequently associated with severe toxicities, with 30–80% of patients experiencing grade 3–4 adverse events that may require dose reduction, treatment delays, or hospitalization. While machine learning models have shown strong potential in predicting chemotherapy-related toxicities using electronic health records, genomic data, and clinical variables, most existing approaches generate only point estimates (e.g., a single risk probability) without quantifying uncertainty, limiting their clinical reliability. Such miscalibrated predictions can lead to overconfident risk underestimation or excessive caution, both of which may negatively impact treatment decisions and patient outcomes. This manuscript proposes a conceptual framework that integrates neural network-based toxicity prediction with adaptive conformal prediction to produce calibrated, patient-specific prediction intervals with formal coverage guarantees. The framework combines a feedforward neural network for risk estimation, a non-conformity score to measure how atypical a patient is relative to the training data, and an adaptive calibration mechanism that updates interval thresholds over time to reflect shifts in patient populations and clinical practice. This design enables narrower intervals for well-represented, predictable cases and wider intervals for atypical or high-uncertainty patients, thereby making prediction reliability explicit. Importantly, the method provides finite-sample coverage guarantees without requiring distributional assumptions, ensuring that true toxicity outcomes fall within the predicted intervals at a user-specified confidence level. By transforming point predictions into uncertainty-aware, clinically interpretable intervals, the framework supports more robust, risk-stratified decision-making in chemotherapy planning and moves toward safer, more trustworthy AI-assisted oncology care.

Intensive care unit (ICU) data consist of high-frequency multivariate time series, including vital signs, laboratory results, and hemodynamic variables, which are crucial for clinical decision-making and predictive modeling. However, these data are frequently incomplete due to monitor interruptions, clinical workflows, and selective measurement, with missing rates ranging from 20% to over 80% depending on the variable. Missingness in ICU time series is often not random, as sicker patients tend to be monitored more frequently, creating a missing not at random (MNAR) mechanism. Conventional imputation methods such as mean filling, interpolation, and multiple imputation assume random missingness and therefore introduce bias and distort clinical signals under MNAR conditions. We propose a variational recurrent neural network (VRNN) with stochastic attention to impute ICU time series under MNAR settings. The framework integrates latent state modeling of physiological dynamics, stochastic attention over observed measurements using Gumbel-Softmax sampling, and a missingness pattern encoder that explicitly models the observation process. An imputation decoder generates probabilistic estimates of missing values conditioned on latent states, attention context, and missingness structure. This framework enables uncertainty-aware and potentially unbiased imputation in ICU time series by jointly modeling physiological dynamics and missingness mechanisms. It combines variational inference and stochastic attention to address systematic bias in conventional approaches, with future work needed to validate performance on real-world ICU datasets.