Learning from the experts: From expert systems to machine-learned diagnosis models

Murali Ravuri, Anitha Kannan, Geoffrey J. Tso, Xavier Amatriain
Proceedings of the 3rd Machine Learning for Healthcare Conference, PMLR 85:227-243, 2018.

Abstract

Expert diagnostic support systems have been extensively studied. The practical applications of these systems in real-world scenarios have been somewhat limited due to well-understood shortcomings, such as lack of extensibility. More recently, machine-learned models for medical diagnosis have gained momentum, since they can learn and generalize patterns found in very large datasets like electronic health records. These models also have shortcomings - in particular, there is no easy way to incorporate prior knowledge from existing literature or experts. In this paper, we present a method to merge both approaches by using expert systems as generative models that create simulated data on which models can be learned. We demonstrate that such a learned model not only preserves the original properties of the expert systems but also addresses some of their limitations. Furthermore, we show how this approach can also be used as the starting point to combine expert knowledge with knowledge extracted from other data sources, such as electronic health records.

Cite this Paper

BibTeX
@InProceedings{pmlr-v85-ravuri18a, title = {Learning from the experts: From expert systems to machine-learned diagnosis models}, author = {Ravuri, Murali and Kannan, Anitha and Tso, Geoffrey J. and Amatriain, Xavier}, booktitle = {Proceedings of the 3rd Machine Learning for Healthcare Conference}, pages = {227--243}, year = {2018}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {85}, series = {Proceedings of Machine Learning Research}, month = {17--18 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v85/ravuri18a/ravuri18a.pdf}, url = {https://proceedings.mlr.press/v85/ravuri18a.html}, abstract = {Expert diagnostic support systems have been extensively studied. The practical applications of these systems in real-world scenarios have been somewhat limited due to well-understood shortcomings, such as lack of extensibility. More recently, machine-learned models for medical diagnosis have gained momentum, since they can learn and generalize patterns found in very large datasets like electronic health records. These models also have shortcomings - in particular, there is no easy way to incorporate prior knowledge from existing literature or experts. In this paper, we present a method to merge both approaches by using expert systems as generative models that create simulated data on which models can be learned. We demonstrate that such a learned model not only preserves the original properties of the expert systems but also addresses some of their limitations. Furthermore, we show how this approach can also be used as the starting point to combine expert knowledge with knowledge extracted from other data sources, such as electronic health records.} }
Endnote
%0 Conference Paper %T Learning from the experts: From expert systems to machine-learned diagnosis models %A Murali Ravuri %A Anitha Kannan %A Geoffrey J. Tso %A Xavier Amatriain %B Proceedings of the 3rd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2018 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v85-ravuri18a %I PMLR %P 227--243 %U https://proceedings.mlr.press/v85/ravuri18a.html %V 85 %X Expert diagnostic support systems have been extensively studied. The practical applications of these systems in real-world scenarios have been somewhat limited due to well-understood shortcomings, such as lack of extensibility. More recently, machine-learned models for medical diagnosis have gained momentum, since they can learn and generalize patterns found in very large datasets like electronic health records. These models also have shortcomings - in particular, there is no easy way to incorporate prior knowledge from existing literature or experts. In this paper, we present a method to merge both approaches by using expert systems as generative models that create simulated data on which models can be learned. We demonstrate that such a learned model not only preserves the original properties of the expert systems but also addresses some of their limitations. Furthermore, we show how this approach can also be used as the starting point to combine expert knowledge with knowledge extracted from other data sources, such as electronic health records.
APA
Ravuri, M., Kannan, A., Tso, G.J. & Amatriain, X.. (2018). Learning from the experts: From expert systems to machine-learned diagnosis models. Proceedings of the 3rd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 85:227-243 Available from https://proceedings.mlr.press/v85/ravuri18a.html.

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