Engineering topics

Deng, Jun

Publications and source records attributed to Deng, Jun.

3 records

DOE OSTI2023

Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis

Abstract Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models

We develop a patient-specific dynamical system model from the time series data of the cancer patient’s metabolic panel taken during the period of cancer treatment and recovery. The model consists of a pair of stacked long short-term memory (LSTM) recurrent neural networks and a fully connected neural network in each unit. It is intended to be used by physicians to trace back and look forward at the patient’s metabolic indices, to identify potential adverse events, and to make short-term predictions. When the model is used in making short-term predictions, the relative error in every index is less than 10% in the L ∞ norm and less than 6.3% in the L 1 norm in the validation process. Once a master model is built, the patient-specific model can be calibrated through transfer learning. As an example, we obtain patient-specific models for four more cancer patients through transfer learning, which all exhibit reduced training time and a comparable level of accuracy. This study demonstrates that this modeling approach is reliable and can deliver clinically acceptable physiological models for tracking and forecasting patients’ metabolic indices.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Fast-Charging and Ultrahigh-Capacity Lithium Metal Anode Enabled by Surface Alloying

Li metal anodes are going through a great revival but they still encounter grand challenges. One often neglected issue is that most reported Li metal anodes are only cyclable under relatively low current density (<5 mA cm -2 ) and small areal capacity (<5 mAh cm -2 ), which essentially limits their high-power applications and results in ineffective Li utilization (<1%). Herein, it is reported that surface alloyed Li metal anodes can enable reversible cycling with ultrafast rate and ultralarge areal capacity. Low-cost Si wafers are used and are chemically etched down to 20-30 mu m membranes. Simply laminating a Si membrane onto Li foil results in the formation of LixSi alloy film fused onto Li metal with mechanical robustness and high Li-ion conductivity. Symmetric cell measurements show that the surface alloyed Li anode has excellent cycling stability, even under high current density up to 25 mA cm -2 and unprecedented areal capacity up to 100 mAh cm -2 . Furthermore, the surface alloyed Li anode is paired with amorphous MoS 3 cathode and achieves remarkable full-cell performance.

36 MATERIALS SCIENCE↗