TY - GEN

T1 - The stochastic score classification problem

AU - Gkenosis, Dimitrios

AU - Grammel, Nathaniel

AU - Hellerstein, Lisa

AU - Kletenik, Devorah

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

UR - http://www.scopus.com/inward/record.url?scp=85052512208&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052512208&partnerID=8YFLogxK

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 -