Towards Fairness in Classifying Medical Conversations into SOAP Sections
As machine learning algorithms are more widely deployed in healthcare, the question of algorithmic fairness becomes more critical to examine. Our work seeks to identify and understand disparities in a deployed model that classifies doctor-patient conversations into sections of a medical SOAP note. W...
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creator | Ferracane, Elisa Konam, Sandeep |
description | As machine learning algorithms are more widely deployed in healthcare, the
question of algorithmic fairness becomes more critical to examine. Our work
seeks to identify and understand disparities in a deployed model that
classifies doctor-patient conversations into sections of a medical SOAP note.
We employ several metrics to measure disparities in the classifier performance,
and find small differences in a portion of the disadvantaged groups. A deeper
analysis of the language in these conversations and further stratifying the
groups suggests these differences are related to and often attributable to the
type of medical appointment (e.g., psychiatric vs. internist). Our findings
stress the importance of understanding the disparities that may exist in the
data itself and how that affects a model's ability to equally distribute
benefits. |
doi_str_mv | 10.48550/arxiv.2012.07749 |
format | Article |
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question of algorithmic fairness becomes more critical to examine. Our work
seeks to identify and understand disparities in a deployed model that
classifies doctor-patient conversations into sections of a medical SOAP note.
We employ several metrics to measure disparities in the classifier performance,
and find small differences in a portion of the disadvantaged groups. A deeper
analysis of the language in these conversations and further stratifying the
groups suggests these differences are related to and often attributable to the
type of medical appointment (e.g., psychiatric vs. internist). Our findings
stress the importance of understanding the disparities that may exist in the
data itself and how that affects a model's ability to equally distribute
benefits.</description><identifier>DOI: 10.48550/arxiv.2012.07749</identifier><language>eng</language><subject>Computer Science - Computation and Language ; Computer Science - Computers and Society</subject><creationdate>2020-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2012.07749$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2012.07749$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Ferracane, Elisa</creatorcontrib><creatorcontrib>Konam, Sandeep</creatorcontrib><title>Towards Fairness in Classifying Medical Conversations into SOAP Sections</title><description>As machine learning algorithms are more widely deployed in healthcare, the
question of algorithmic fairness becomes more critical to examine. Our work
seeks to identify and understand disparities in a deployed model that
classifies doctor-patient conversations into sections of a medical SOAP note.
We employ several metrics to measure disparities in the classifier performance,
and find small differences in a portion of the disadvantaged groups. A deeper
analysis of the language in these conversations and further stratifying the
groups suggests these differences are related to and often attributable to the
type of medical appointment (e.g., psychiatric vs. internist). Our findings
stress the importance of understanding the disparities that may exist in the
data itself and how that affects a model's ability to equally distribute
benefits.</description><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Computers and Society</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8tKAzEYBeBsupDWB3BlXmDGpMmfy7IMrRUqFTr74ScXCYyZkpRq3146ujpwOBz4CHnirJUGgL1g-UnXds34umVaS_tA9v30jcVXusNUcqiVpky7EWtN8ZbyJ30PPjkcaTflaygVL2nK99Floqfj5oOegpurFVlEHGt4_M8l6Xfbvts3h-PrW7c5NKi0baR3UaBhVkZQBhk6H2IEJzgIBdwYzwwwxrUCK4wEYTEoAxw0GLABxZI8_93OlOFc0heW23AnDTNJ_AJw80WX</recordid><startdate>20201202</startdate><enddate>20201202</enddate><creator>Ferracane, Elisa</creator><creator>Konam, Sandeep</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20201202</creationdate><title>Towards Fairness in Classifying Medical Conversations into SOAP Sections</title><author>Ferracane, Elisa ; Konam, Sandeep</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-4dcf3a8094f568a0acdeff5c315365188d0850017659384539ae6851575859ea3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Computers and Society</topic><toplevel>online_resources</toplevel><creatorcontrib>Ferracane, Elisa</creatorcontrib><creatorcontrib>Konam, Sandeep</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ferracane, Elisa</au><au>Konam, Sandeep</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Towards Fairness in Classifying Medical Conversations into SOAP Sections</atitle><date>2020-12-02</date><risdate>2020</risdate><abstract>As machine learning algorithms are more widely deployed in healthcare, the
question of algorithmic fairness becomes more critical to examine. Our work
seeks to identify and understand disparities in a deployed model that
classifies doctor-patient conversations into sections of a medical SOAP note.
We employ several metrics to measure disparities in the classifier performance,
and find small differences in a portion of the disadvantaged groups. A deeper
analysis of the language in these conversations and further stratifying the
groups suggests these differences are related to and often attributable to the
type of medical appointment (e.g., psychiatric vs. internist). Our findings
stress the importance of understanding the disparities that may exist in the
data itself and how that affects a model's ability to equally distribute
benefits.</abstract><doi>10.48550/arxiv.2012.07749</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Computation and Language Computer Science - Computers and Society |
title | Towards Fairness in Classifying Medical Conversations into SOAP Sections |
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