@article{chohenshm22,
	abstract = {There is significant interest in deploying machine learning algorithms for
diagnostic radiology, as modern learning techniques have made it possible to
detect abnormalities in medical images within minutes. While machine-assisted
diagnoses cannot yet reliably replace human reviews of images by a radiologist,
they could inform prioritization rules for determining the order by which to
review patient cases so that patients with time-sensitive conditions could
benefit from early intervention.

 We study this scenario by formulating it as a learning-augmented online
scheduling problem. We are given information about each arriving patient's
urgency level in advance, but these predictions are inevitably error-prone. In
this formulation, we face the challenges of decision making under imperfect
information, and of responding dynamically to prediction error as we observe
better data in real-time. We propose a simple online policy and show that this
policy is in fact the best possible in certain stylized settings. We also
demonstrate that our policy achieves the two desiderata of online algorithms
with predictions: consistency (performance improvement with prediction
accuracy) and robustness (protection against the worst case). We complement our
theoretical findings with empirical evaluations of the policy under settings
that more accurately reflect clinical scenarios in the real world.},
	author = {Woo-Hyung Cho and Shane G. Henderson and David B. Shmoys},
	date-added = {2022-12-21 05:38:38 -0500},
	date-modified = {2022-12-21 05:40:56 -0500},
	journal = {Submitted},
	title = {Scheduling with predictions},
	url_paper = {http://arxiv.org/abs/2212.10433},
	year = {2022}}

{"_id":"4Q99riA7F3FQY8YPs","bibbaseid":"cho-henderson-shmoys-schedulingwithpredictions-2022","author_short":["Cho, W.","Henderson, S. G.","Shmoys, D. B."],"bibdata":{"bibtype":"article","type":"article","abstract":"There is significant interest in deploying machine learning algorithms for diagnostic radiology, as modern learning techniques have made it possible to detect abnormalities in medical images within minutes. While machine-assisted diagnoses cannot yet reliably replace human reviews of images by a radiologist, they could inform prioritization rules for determining the order by which to review patient cases so that patients with time-sensitive conditions could benefit from early intervention. We study this scenario by formulating it as a learning-augmented online scheduling problem. We are given information about each arriving patient's urgency level in advance, but these predictions are inevitably error-prone. In this formulation, we face the challenges of decision making under imperfect information, and of responding dynamically to prediction error as we observe better data in real-time. We propose a simple online policy and show that this policy is in fact the best possible in certain stylized settings. We also demonstrate that our policy achieves the two desiderata of online algorithms with predictions: consistency (performance improvement with prediction accuracy) and robustness (protection against the worst case). We complement our theoretical findings with empirical evaluations of the policy under settings that more accurately reflect clinical scenarios in the real world.","author":[{"firstnames":["Woo-Hyung"],"propositions":[],"lastnames":["Cho"],"suffixes":[]},{"firstnames":["Shane","G."],"propositions":[],"lastnames":["Henderson"],"suffixes":[]},{"firstnames":["David","B."],"propositions":[],"lastnames":["Shmoys"],"suffixes":[]}],"date-added":"2022-12-21 05:38:38 -0500","date-modified":"2022-12-21 05:40:56 -0500","journal":"Submitted","title":"Scheduling with predictions","url_paper":"http://arxiv.org/abs/2212.10433","year":"2022","bibtex":"@article{chohenshm22,\n\tabstract = {There is significant interest in deploying machine learning algorithms for\ndiagnostic radiology, as modern learning techniques have made it possible to\ndetect abnormalities in medical images within minutes. While machine-assisted\ndiagnoses cannot yet reliably replace human reviews of images by a radiologist,\nthey could inform prioritization rules for determining the order by which to\nreview patient cases so that patients with time-sensitive conditions could\nbenefit from early intervention.\n\n We study this scenario by formulating it as a learning-augmented online\nscheduling problem. We are given information about each arriving patient's\nurgency level in advance, but these predictions are inevitably error-prone. In\nthis formulation, we face the challenges of decision making under imperfect\ninformation, and of responding dynamically to prediction error as we observe\nbetter data in real-time. We propose a simple online policy and show that this\npolicy is in fact the best possible in certain stylized settings. We also\ndemonstrate that our policy achieves the two desiderata of online algorithms\nwith predictions: consistency (performance improvement with prediction\naccuracy) and robustness (protection against the worst case). We complement our\ntheoretical findings with empirical evaluations of the policy under settings\nthat more accurately reflect clinical scenarios in the real world.},\n\tauthor = {Woo-Hyung Cho and Shane G. Henderson and David B. Shmoys},\n\tdate-added = {2022-12-21 05:38:38 -0500},\n\tdate-modified = {2022-12-21 05:40:56 -0500},\n\tjournal = {Submitted},\n\ttitle = {Scheduling with predictions},\n\turl_paper = {http://arxiv.org/abs/2212.10433},\n\tyear = {2022}}\n\n","author_short":["Cho, W.","Henderson, S. G.","Shmoys, D. B."],"key":"chohenshm22","id":"chohenshm22","bibbaseid":"cho-henderson-shmoys-schedulingwithpredictions-2022","role":"author","urls":{" paper":"http://arxiv.org/abs/2212.10433"},"metadata":{"authorlinks":{}},"downloads":6},"bibtype":"article","biburl":"https://people.orie.cornell.edu/shane/ShanePubs.bib","dataSources":["ZCuKDjctePZJeeaBw"],"keywords":[],"search_terms":["scheduling","predictions","cho","henderson","shmoys"],"title":"Scheduling with predictions","year":2022,"downloads":6}