This package contains the software programs designed for the NHMC-AR model. It includes a learning algorithm, a prediction function and a state inference algorithm.

This package has been implemented by Fatoumata Dama, PhD student (2019-2022), Nantes University, France.

Fatoumata Dama was supported by a PhD scholarship granted by the French Ministery for Higher Education, Research and Innovation. She worked under the supervision of Christine Sinoquet, Associate Professor, PhD supervisor, LS2N / UMR CNRS 6004 (Digital Science Institute of Nantes), Nantes University, France.

• Python 3.6
• Numpy
• Scipy
• Scikit-learn
• Pickle5
• Futures
• Numba 0.45
• Tick

FC : heart frequency (HF)

PAS : systolic blood pressure (SBP)

PAM : average blood pressure (ABP)

PAD : diastolic blood pressure (DBP)

In this case, the contextual variables only take into account the latest occurrences of events, over all categories of events.

python3 -O mcnh-ar-C1_training.py train_data_dir nb_time_series model_output_dir ar_order nb_states

In this case, the contextual variables are extracted using the Hawkes point process framework. Thus, these variables take into account the whole history of past events.

python3 -O mcnh-ar-C2_training.py train_data_dir nb_time_series model_output_dir ar_order nb_states features_file

• train_data_dir: "anesthesia_data" directory
• nb_time_series: the number of training instances (between 1 and 500)
• model_output_dir: the name of the directory in which model output is saved within a serialized file
• ar_order: the order of the auto-regressive process (>= 0)
• nb_states: the number of states to be considered (>= 2)
• features_file: the file that contains the contextual variables C2 extracted based on the Hawkes process (to be loaded from directory "Point-process-models/tick-Hawkes-process/model_outputs/expKernel/5-event-types")