Jong-Min Kim

Jeongjin Lee, Jong-Min Kim

We propose a Buckley James (BJ) Boost Q learning framework for estimating optimal dynamic treatment regimes under right censored survival data, tailored for longitudinal randomized clinical trial settings. The method integrates accelerated failure time models with iterative boosting techniques, including componentwise least squares and regression trees, within a counterfactual Q learning framework. By directly modeling conditional survival time, BJ Boost Q learning avoids the restrictive proportional hazards assumption and enables unbiased estimation of stage specific Q functions. Grounded in potential outcomes, this framework ensures identifiability of the optimal treatment regime under standard causal assumptions. Compared to Cox based Q learning, which relies on hazard modeling and may suffer from bias under misspecification, our approach provides robust and flexible estimation. Simulation studies and analysis of the ACTG175 HIV trial demonstrate that BJ Boost Q learning yields higher accuracy in treatment decision making, especially in multistage settings where bias can accumulate.

Jong-Min Kim, Il Do Ha, Sangjin Kim

This research integrates deep learning, copula functions, and survival analysis to effectively handle highly correlated and right-censored multivariate survival data. It introduces copula-based activation functions (Clayton, Gumbel, and their combinations) to model the nonlinear dependencies inherent in such data. Through simulation studies and analysis of real breast cancer data, our proposed CNN-LSTM with copula-based activation functions for multivariate multi-types of survival responses enhances prediction accuracy by explicitly addressing right-censored data and capturing complex patterns. The model's performance is evaluated using Shewhart control charts, focusing on the average run length (ARL).

Jong-Min Kim, You-Jin Jeong, Han-Jae Chung et al.

Purpose: To acquire the real-time interactive temperature map for aqueous and adipose tissue, the problems of long acquisition and processing time must be addressed. To overcome these major challenges, this paper proposes a cascaded convolutional neural network (CNN) framework and multi-echo gradient echo (meGRE) with a single reference variable flip angle (srVFA). Methods: To optimize the echo times for each method, MR images are acquired using a meGRE sequence; meGRE images with two flip angles (FAs) and meGRE images with a single FA are acquired during the pretreatment and treatment stages, respectively. These images are then processed and reconstructed by a cascaded CNN, which consists of two CNNs. The first CNN (called DeepACCnet) performs HR complex MR image reconstruction from the LR MR image acquired during the treatment stage, which is improved by the HR magnitude MR image acquired during the pretreatment stage. The second CNN (called DeepPROCnet) copes with T1 mapping. Results: Measurements of temperature and T1 changes obtained by meGRE combined with srVFA and cascaded CNNs were achieved in an agarose gel phantom, ex vivo porcine muscle, and ex vivo porcine muscle with fat layers (heating tests), and in vivo human prostate and brain (non-heating tests). In the heating test, the maximum differences between fiber-optic sensor and samples are less than 1 degree Celcius. In all cases, temperature mapping using the cascaded CNN achieved the best results in all cases. The acquisition and processing times for the proposed method are 0.8 s and 32 ms, respectively. Conclusions: Real-time interactive HR MR temperature mapping for simultaneously measuring aqueous and adipose tissue is feasible by combining a cascaded CNN with meGRE and srVFA.