Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission Prediction Article Swipe
Grace Yang
,
Ming Cao
,
Lavender Yao Jiang
,
Xujin Chris Liu
,
Alexander T. M. Cheung
,
Hannah Weiss
,
David B. Kurland
,
Kyunghyun Cho
,
Eric K. Oermann
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.07047
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.07047
Traditional evaluation metrics for classification in natural language processing such as accuracy and area under the curve fail to differentiate between models with different predictive behaviors despite their similar performance metrics. We introduce sensitivity score, a metric that scrutinizes models' behaviors at the vocabulary level to provide insights into disparities in their decision-making logic. We assess the sensitivity score on a set of representative words in the test set using two classifiers trained for hospital readmission classification with similar performance statistics. Our experiments compare the decision-making logic of clinicians and classifiers based on rank correlations of sensitivity scores. The results indicate that the language model's sensitivity score aligns better with the professionals than the xgboost classifier on tf-idf embeddings, which suggests that xgboost uses some spurious features. Overall, this metric offers a novel perspective on assessing models' robustness by quantifying their discrepancy with professional opinions. Our code is available on GitHub (https://github.com/nyuolab/Model_Sensitivity).
Related Topics
Concepts
Computer science
Artificial intelligence
Classifier (UML)
Vocabulary
Machine learning
Test set
Robustness (evolution)
Spurious relationship
Metric (unit)
Sensitivity (control systems)
F1 score
Rank (graph theory)
Natural language processing
Mathematics
Electronic engineering
Combinatorics
Gene
Economics
Operations management
Chemistry
Biochemistry
Engineering
Philosophy
Linguistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.07047
- https://arxiv.org/pdf/2211.07047
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309289584
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4309289584Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2211.07047Digital Object Identifier
- Title
-
Language Model Classifier Aligns Better with Physician Word Sensitivity than XGBoost on Readmission PredictionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-13Full publication date if available
- Authors
-
Grace Yang, Ming Cao, Lavender Yao Jiang, Xujin Chris Liu, Alexander T. M. Cheung, Hannah Weiss, David B. Kurland, Kyunghyun Cho, Eric K. OermannList of authors in order
- Landing page
-
https://arxiv.org/abs/2211.07047Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2211.07047Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2211.07047Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Classifier (UML), Vocabulary, Machine learning, Test set, Robustness (evolution), Spurious relationship, Metric (unit), Sensitivity (control systems), F1 score, Rank (graph theory), Natural language processing, Mathematics, Electronic engineering, Combinatorics, Gene, Economics, Operations management, Chemistry, Biochemistry, Engineering, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.at | 41 |
| abstract_inverted_index.by | 138 |
| abstract_inverted_index.in | 5, 50, 65 |
| abstract_inverted_index.is | 147 |
| abstract_inverted_index.of | 62, 87, 95 |
| abstract_inverted_index.on | 59, 92, 116, 134, 149 |
| abstract_inverted_index.to | 18, 45 |
| abstract_inverted_index.Our | 81, 145 |
| abstract_inverted_index.The | 98 |
| abstract_inverted_index.and | 12, 89 |
| abstract_inverted_index.for | 3, 73 |
| abstract_inverted_index.set | 61, 68 |
| abstract_inverted_index.the | 15, 42, 56, 66, 84, 102, 110, 113 |
| abstract_inverted_index.two | 70 |
| abstract_inverted_index.area | 13 |
| abstract_inverted_index.code | 146 |
| abstract_inverted_index.fail | 17 |
| abstract_inverted_index.into | 48 |
| abstract_inverted_index.rank | 93 |
| abstract_inverted_index.some | 124 |
| abstract_inverted_index.such | 9 |
| abstract_inverted_index.test | 67 |
| abstract_inverted_index.than | 112 |
| abstract_inverted_index.that | 37, 101, 121 |
| abstract_inverted_index.this | 128 |
| abstract_inverted_index.uses | 123 |
| abstract_inverted_index.with | 22, 77, 109, 142 |
| abstract_inverted_index.based | 91 |
| abstract_inverted_index.curve | 16 |
| abstract_inverted_index.level | 44 |
| abstract_inverted_index.logic | 86 |
| abstract_inverted_index.novel | 132 |
| abstract_inverted_index.score | 58, 106 |
| abstract_inverted_index.their | 27, 51, 140 |
| abstract_inverted_index.under | 14 |
| abstract_inverted_index.using | 69 |
| abstract_inverted_index.which | 119 |
| abstract_inverted_index.words | 64 |
| abstract_inverted_index.GitHub | 150 |
| abstract_inverted_index.aligns | 107 |
| abstract_inverted_index.assess | 55 |
| abstract_inverted_index.better | 108 |
| abstract_inverted_index.logic. | 53 |
| abstract_inverted_index.metric | 36, 129 |
| abstract_inverted_index.models | 21 |
| abstract_inverted_index.offers | 130 |
| abstract_inverted_index.score, | 34 |
| abstract_inverted_index.tf-idf | 117 |
| abstract_inverted_index.between | 20 |
| abstract_inverted_index.compare | 83 |
| abstract_inverted_index.despite | 26 |
| abstract_inverted_index.metrics | 2 |
| abstract_inverted_index.model's | 104 |
| abstract_inverted_index.models' | 39, 136 |
| abstract_inverted_index.natural | 6 |
| abstract_inverted_index.provide | 46 |
| abstract_inverted_index.results | 99 |
| abstract_inverted_index.scores. | 97 |
| abstract_inverted_index.similar | 28, 78 |
| abstract_inverted_index.trained | 72 |
| abstract_inverted_index.xgboost | 114, 122 |
| abstract_inverted_index.Overall, | 127 |
| abstract_inverted_index.accuracy | 11 |
| abstract_inverted_index.hospital | 74 |
| abstract_inverted_index.indicate | 100 |
| abstract_inverted_index.insights | 47 |
| abstract_inverted_index.language | 7, 103 |
| abstract_inverted_index.metrics. | 30 |
| abstract_inverted_index.spurious | 125 |
| abstract_inverted_index.suggests | 120 |
| abstract_inverted_index.assessing | 135 |
| abstract_inverted_index.available | 148 |
| abstract_inverted_index.behaviors | 25, 40 |
| abstract_inverted_index.different | 23 |
| abstract_inverted_index.features. | 126 |
| abstract_inverted_index.introduce | 32 |
| abstract_inverted_index.opinions. | 144 |
| abstract_inverted_index.classifier | 115 |
| abstract_inverted_index.clinicians | 88 |
| abstract_inverted_index.evaluation | 1 |
| abstract_inverted_index.predictive | 24 |
| abstract_inverted_index.processing | 8 |
| abstract_inverted_index.robustness | 137 |
| abstract_inverted_index.vocabulary | 43 |
| abstract_inverted_index.Traditional | 0 |
| abstract_inverted_index.classifiers | 71, 90 |
| abstract_inverted_index.discrepancy | 141 |
| abstract_inverted_index.disparities | 49 |
| abstract_inverted_index.embeddings, | 118 |
| abstract_inverted_index.experiments | 82 |
| abstract_inverted_index.performance | 29, 79 |
| abstract_inverted_index.perspective | 133 |
| abstract_inverted_index.quantifying | 139 |
| abstract_inverted_index.readmission | 75 |
| abstract_inverted_index.scrutinizes | 38 |
| abstract_inverted_index.sensitivity | 33, 57, 96, 105 |
| abstract_inverted_index.statistics. | 80 |
| abstract_inverted_index.correlations | 94 |
| abstract_inverted_index.professional | 143 |
| abstract_inverted_index.differentiate | 19 |
| abstract_inverted_index.professionals | 111 |
| abstract_inverted_index.classification | 4, 76 |
| abstract_inverted_index.representative | 63 |
| abstract_inverted_index.decision-making | 52, 85 |
| abstract_inverted_index.(https://github.com/nyuolab/Model_Sensitivity). | 151 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |