Gkenosis, Dimitrios ;
Grammel, Nathaniel ;
Hellerstein, Lisa ;
Kletenik, Devorah
The Stochastic Score Classification Problem
Abstract
Consider the following Stochastic Score Classification Problem. A doctor is assessing a patient's risk of developing a certain disease, and can perform n tests on the patient. Each test has a binary outcome, positive or negative. A positive result is an indication of risk, and a patient's score is the total number of positive test results. Test results are accurate. The doctor needs to classify the patient into one of B risk classes, depending on the score (e.g., LOW, MEDIUM, and HIGH risk). Each of these classes corresponds to a contiguous range of scores. Test i has probability p_i of being positive, and it costs c_i to perform. To reduce costs, instead of performing all tests, the doctor will perform them sequentially and stop testing when it is possible to determine the patient's risk category. The problem is to determine the order in which the doctor should perform the tests, so as to minimize expected testing cost. We provide approximation algorithms for adaptive and nonadaptive versions of this problem, and pose a number of open questions.
BibTeX  Entry
@InProceedings{gkenosis_et_al:LIPIcs:2018:9499,
author = {Dimitrios Gkenosis and Nathaniel Grammel and Lisa Hellerstein and Devorah Kletenik},
title = {{The Stochastic Score Classification Problem}},
booktitle = {26th Annual European Symposium on Algorithms (ESA 2018)},
pages = {36:136:14},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {9783959770811},
ISSN = {18688969},
year = {2018},
volume = {112},
editor = {Yossi Azar and Hannah Bast and Grzegorz Herman},
publisher = {Schloss DagstuhlLeibnizZentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2018/9499},
URN = {urn:nbn:de:0030drops94990},
doi = {10.4230/LIPIcs.ESA.2018.36},
annote = {Keywords: approximation algorithms, symmetric Boolean functions, stochastic probing, sequential testing, adaptivity}
}
2018
Keywords: 

approximation algorithms, symmetric Boolean functions, stochastic probing, sequential testing, adaptivity 
Seminar: 

26th Annual European Symposium on Algorithms (ESA 2018)

Issue date: 

2018 
Date of publication: 

2018 