The Hawkes Process component in TrialSynth, while effective for modeling event occurrences and their temporal dynamics, has certain limitations. One significant limitation is its reliance on the assumption of self-excitation, which may not hold true for all clinical events. In some cases, events may be influenced by external factors or covariates that are not captured by the traditional Hawkes Process framework. This could lead to inaccuracies in the generated event sequences, particularly in complex clinical scenarios where multiple factors influence patient outcomes.
To address these limitations, alternative event modeling approaches could be integrated into TrialSynth. For instance, incorporating Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks could enhance the model's ability to capture long-range dependencies and complex temporal patterns in event sequences. These architectures are well-suited for sequential data and can learn from the entire history of events, potentially improving the fidelity of the generated synthetic data.
Additionally, integrating a hybrid model that combines Hawkes Processes with other probabilistic graphical models, such as Bayesian Networks, could provide a more comprehensive framework for event generation. This would allow TrialSynth to incorporate prior knowledge and external influences on event occurrences, leading to more accurate and realistic synthetic data generation.