Wavelet Reconstruction Networks for Marked Point Processes

Jeremy Weiss
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:95-106, 2021.

Abstract

Timestamped sequences of events, pervasive in domains with data logs, e.g., health records, are often modeled as point processes or rate functions over time. Leading classical methods for risk scores such as Cox and Hawkes processes use such data but make strong assumptions about the shape and form of multivariate influences, resulting in time-to-event distributions irreflective of many real world processes. Methods in point processes and recurrent neural networks capably model rate functions but their complexity may make interpretation, use and reuse challenging. Our work develops a high-performing and interrogable yet simple model. We introduce wavelet reconstruction networks, a multivariate point process with a sparse wavelet reconstruction kernel to model rate functions from marked, timestamped data. We show these simple models achieve improved performance when applied to forecasting complications and care visits in patients with diabetes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v146-weiss21a, title = {Wavelet Reconstruction Networks for Marked Point Processes}, author = {Weiss, Jeremy}, booktitle = {Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021}, pages = {95--106}, year = {2021}, editor = {Greiner, Russell and Kumar, Neeraj and Gerds, Thomas Alexander and van der Schaar, Mihaela}, volume = {146}, series = {Proceedings of Machine Learning Research}, month = {22--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v146/weiss21a/weiss21a.pdf}, url = {https://proceedings.mlr.press/v146/weiss21a.html}, abstract = {Timestamped sequences of events, pervasive in domains with data logs, e.g., health records, are often modeled as point processes or rate functions over time. Leading classical methods for risk scores such as Cox and Hawkes processes use such data but make strong assumptions about the shape and form of multivariate influences, resulting in time-to-event distributions irreflective of many real world processes. Methods in point processes and recurrent neural networks capably model rate functions but their complexity may make interpretation, use and reuse challenging. Our work develops a high-performing and interrogable yet simple model. We introduce wavelet reconstruction networks, a multivariate point process with a sparse wavelet reconstruction kernel to model rate functions from marked, timestamped data. We show these simple models achieve improved performance when applied to forecasting complications and care visits in patients with diabetes.} }
Endnote
%0 Conference Paper %T Wavelet Reconstruction Networks for Marked Point Processes %A Jeremy Weiss %B Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021 %C Proceedings of Machine Learning Research %D 2021 %E Russell Greiner %E Neeraj Kumar %E Thomas Alexander Gerds %E Mihaela van der Schaar %F pmlr-v146-weiss21a %I PMLR %P 95--106 %U https://proceedings.mlr.press/v146/weiss21a.html %V 146 %X Timestamped sequences of events, pervasive in domains with data logs, e.g., health records, are often modeled as point processes or rate functions over time. Leading classical methods for risk scores such as Cox and Hawkes processes use such data but make strong assumptions about the shape and form of multivariate influences, resulting in time-to-event distributions irreflective of many real world processes. Methods in point processes and recurrent neural networks capably model rate functions but their complexity may make interpretation, use and reuse challenging. Our work develops a high-performing and interrogable yet simple model. We introduce wavelet reconstruction networks, a multivariate point process with a sparse wavelet reconstruction kernel to model rate functions from marked, timestamped data. We show these simple models achieve improved performance when applied to forecasting complications and care visits in patients with diabetes.
APA
Weiss, J.. (2021). Wavelet Reconstruction Networks for Marked Point Processes. Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, in Proceedings of Machine Learning Research 146:95-106 Available from https://proceedings.mlr.press/v146/weiss21a.html.

Related Material