Towards a Directory of Rare Disease Specialists: Identifying Experts from Publication History

Zihan Wang, Michael Brudno, Orion Buske
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:352-360, 2017.

Abstract

Accurate referral to a medical specialist is a challenging part of medical care, especially for patients with rare diseases. Because of the diversity of rare diseases, finding a specialist that has experience with the particular rare disease is important. This burden often falls on the patients and families, but they do not necessarily have the time or scientific expertise to evaluate the medical literature to identify experts. To help patients, families, and general practitioners find specialists in a particular rare disease, we trained machine learning models to predict the expertise of researchers in every rare disease based on their publication record. We compile a dataset of 209,110 disease-author associations from the literature and evaluate the performance of six machine learning methods, classifying known rare disease experts with 79.4% accuracy and predicting 41,129 disease-expert associations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v68-wang17a, title = {Towards a Directory of Rare Disease Specialists: Identifying Experts from Publication History}, author = {Wang, Zihan and Brudno, Michael and Buske, Orion}, booktitle = {Proceedings of the 2nd Machine Learning for Healthcare Conference}, pages = {352--360}, year = {2017}, editor = {Doshi-Velez, Finale and Fackler, Jim and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {68}, series = {Proceedings of Machine Learning Research}, month = {18--19 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v68/wang17a/wang17a.pdf}, url = {https://proceedings.mlr.press/v68/wang17a.html}, abstract = {Accurate referral to a medical specialist is a challenging part of medical care, especially for patients with rare diseases. Because of the diversity of rare diseases, finding a specialist that has experience with the particular rare disease is important. This burden often falls on the patients and families, but they do not necessarily have the time or scientific expertise to evaluate the medical literature to identify experts. To help patients, families, and general practitioners find specialists in a particular rare disease, we trained machine learning models to predict the expertise of researchers in every rare disease based on their publication record. We compile a dataset of 209,110 disease-author associations from the literature and evaluate the performance of six machine learning methods, classifying known rare disease experts with 79.4% accuracy and predicting 41,129 disease-expert associations.} }
Endnote
%0 Conference Paper %T Towards a Directory of Rare Disease Specialists: Identifying Experts from Publication History %A Zihan Wang %A Michael Brudno %A Orion Buske %B Proceedings of the 2nd Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2017 %E Finale Doshi-Velez %E Jim Fackler %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v68-wang17a %I PMLR %P 352--360 %U https://proceedings.mlr.press/v68/wang17a.html %V 68 %X Accurate referral to a medical specialist is a challenging part of medical care, especially for patients with rare diseases. Because of the diversity of rare diseases, finding a specialist that has experience with the particular rare disease is important. This burden often falls on the patients and families, but they do not necessarily have the time or scientific expertise to evaluate the medical literature to identify experts. To help patients, families, and general practitioners find specialists in a particular rare disease, we trained machine learning models to predict the expertise of researchers in every rare disease based on their publication record. We compile a dataset of 209,110 disease-author associations from the literature and evaluate the performance of six machine learning methods, classifying known rare disease experts with 79.4% accuracy and predicting 41,129 disease-expert associations.
APA
Wang, Z., Brudno, M. & Buske, O.. (2017). Towards a Directory of Rare Disease Specialists: Identifying Experts from Publication History. Proceedings of the 2nd Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 68:352-360 Available from https://proceedings.mlr.press/v68/wang17a.html.

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