Journal
JOURNAL OF PERSONALIZED MEDICINE
Volume 12, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/jpm12050742
Keywords
deep learning; dynamical systems; LSTM; metabolic panel; prediction; time series
Funding
- NSFC [NSAF-U1930402]
- DOE [DE-SC0021655]
- NCI via Leidos Biomedical Research, Inc. [21X130F]
- NSF [DMS-1954532, OIA-1655740]
- GEAR award from the SC EPSCoR/IDeA Program
- U.S. Department of Energy (DOE) [DE-SC0021655] Funding Source: U.S. Department of Energy (DOE)
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We developed a patient-specific dynamical system model for tracking and predicting patients' metabolic indices. The model, which consists of stacked LSTM recurrent neural networks and fully connected neural networks, showed high accuracy in short-term predictions.
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-infinity 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.
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