When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ESA.2018.36
URN: urn:nbn:de:0030-drops-94990
URL: http://drops.dagstuhl.de/opus/volltexte/2018/9499/
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### The Stochastic Score Classification Problem

 pdf-format:

### 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 non-adaptive 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 =	{36:1--36:14},
series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN =	{978-3-95977-081-1},
ISSN =	{1868-8969},
year =	{2018},
volume =	{112},
editor =	{Yossi Azar and Hannah Bast and Grzegorz Herman},
publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9499},
URN =		{urn:nbn:de:0030-drops-94990},
doi =		{10.4230/LIPIcs.ESA.2018.36},
annote =	{Keywords: approximation algorithms, symmetric Boolean functions, stochastic probing, sequential testing, adaptivity}
}
```

 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: 08.08.2018

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