TY - GEN
T1 - The stochastic score classification problem
AU - Gkenosis, Dimitrios
AU - Grammel, Nathaniel
AU - Hellerstein, Lisa
AU - Kletenik, Devorah
N1 - Publisher Copyright:
© Dimitrios Gkenosis, Nathaniel Grammel, Lisa Hellerstein, and Devorah Kletenik.
PY - 2018/8/1
Y1 - 2018/8/1
N2 - 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 pi of being positive, and it costs ci 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 non-adaptive versions of this problem, and pose a number of open questions.
AB - 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 pi of being positive, and it costs ci 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 non-adaptive versions of this problem, and pose a number of open questions.
KW - Adaptivity
KW - Approximation algorithms
KW - Sequential testing
KW - Stochastic probing
KW - Symmetric boolean functions
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U2 - 10.4230/LIPIcs.ESA.2018.36
DO - 10.4230/LIPIcs.ESA.2018.36
M3 - Conference contribution
AN - SCOPUS:85052512208
SN - 9783959770811
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 26th European Symposium on Algorithms, ESA 2018
A2 - Bast, Hannah
A2 - Herman, Grzegorz
A2 - Azar, Yossi
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
T2 - 26th European Symposium on Algorithms, ESA 2018
Y2 - 20 August 2018 through 22 August 2018
ER -