Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction
The task identifying changes and irregularities in medical insurance claim pay-ments is a difficult process of which the traditional practice involves queryinghistorical claims databases and flagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknow...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
AGH University of Science and Technology Press
2013-01-01
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| Series: | Computer Science |
| Online Access: | http://journals.agh.edu.pl/csci/article/download/277/176 |
| _version_ | 1818503932881666048 |
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| author | Che Ngufor Janusz Wojtusiak |
| author_facet | Che Ngufor Janusz Wojtusiak |
| author_sort | Che Ngufor |
| collection | DOAJ |
| description | The task identifying changes and irregularities in medical insurance claim pay-ments is a difficult process of which the traditional practice involves queryinghistorical claims databases and flagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknown andmay change over time, abnormal payments often pass undetected; only to bediscovered when the payment period has passed.This paper presents the problem of on-line unsupervised learning from datastreams when the distribution that generates the data changes or drifts overtime. Automated algorithms for detecting drifting concepts in a probabilitydistribution of the data are presented. The idea behind the presented driftdetection methods is to transform the distribution of the data within a slidingwindow into a more convenient distribution. Then, a test statistics p-value ata given significance level can be used to infer the drift rate, adjust the windowsize and decide on the status of the drift. The detected concepts drifts areused to label the data, for subsequent learning of classification models by asupervised learner. The algorithms were tested on several synthetic and realmedical claims data sets. |
| first_indexed | 2024-12-10T21:30:25Z |
| format | Article |
| id | doaj.art-173cfdcfab1b4607b515975f8e65977b |
| institution | Directory Open Access Journal |
| issn | 1508-2806 |
| language | English |
| last_indexed | 2024-12-10T21:30:25Z |
| publishDate | 2013-01-01 |
| publisher | AGH University of Science and Technology Press |
| record_format | Article |
| series | Computer Science |
| spelling | doaj.art-173cfdcfab1b4607b515975f8e65977b2022-12-22T01:32:49ZengAGH University of Science and Technology PressComputer Science1508-28062013-01-0114219110.7494/csci.2013.14.2.191Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims PredictionChe NguforJanusz WojtusiakThe task identifying changes and irregularities in medical insurance claim pay-ments is a difficult process of which the traditional practice involves queryinghistorical claims databases and flagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknown andmay change over time, abnormal payments often pass undetected; only to bediscovered when the payment period has passed.This paper presents the problem of on-line unsupervised learning from datastreams when the distribution that generates the data changes or drifts overtime. Automated algorithms for detecting drifting concepts in a probabilitydistribution of the data are presented. The idea behind the presented driftdetection methods is to transform the distribution of the data within a slidingwindow into a more convenient distribution. Then, a test statistics p-value ata given significance level can be used to infer the drift rate, adjust the windowsize and decide on the status of the drift. The detected concepts drifts areused to label the data, for subsequent learning of classification models by asupervised learner. The algorithms were tested on several synthetic and realmedical claims data sets.http://journals.agh.edu.pl/csci/article/download/277/176 |
| spellingShingle | Che Ngufor Janusz Wojtusiak Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction Computer Science |
| title | Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction |
| title_full | Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction |
| title_fullStr | Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction |
| title_full_unstemmed | Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction |
| title_short | Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction |
| title_sort | unsupervised labeling of data for supervised learning and its application to medical claims prediction |
| url | http://journals.agh.edu.pl/csci/article/download/277/176 |
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