Scikit-FIBERS: An 'OR'-Rule Discovery Evolutionary Algorithm for Risk Stratification in Right-Censored Survival Analyses Article Swipe
Ryan J. Urbanowicz
,
Harsh Bandhey
,
Malek Kamoun
,
N. M. Fogarty
,
Yi-An Hsieh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3583133.3596393
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3583133.3596393
In 'time-to-event' problems, such as survival analysis in biomedical research, investigators seek to identify the factors that impact/predict not only whether an event occurred (e.g. death), but also when. Further, in the domain of kidney transplantation, there has been recent interest in determining whether higher resolution features, i.e. amino-acid mismatches (AA-MMs) between donors and recipients, can improve risk stratification for transplant failure and identify subsets of AA-MM positions for evaluating risk in future transplants. Motivated by these problems, the FIBERS algorithm was previously developed, which applies a genetic algorithm to learn a population of feature bins (i.e. OR-rules) for predicting right-censored time-to-event survival outcomes. Here, instances are categorized as high-risk if an AA-MM is present for any AA-MM position specified in a given rule, and low-risk if not. In the present study we focus further on the FIBERS algorithm; (1) implementing it as an accessible scikit-learn compatible machine learning package, and (2) applying a variety of simulated right-censored survival datasets to validate scikit-FIBERS efficacy and examine the limitations of its performance to guide ongoing development. We present preliminary results demonstrating the efficacy of scikit-FIBERS, and the limitations of both the algorithm and survival data simulator to guide future work.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3583133.3596393
- https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3583133.3596393&file=p1846-urbanowicz-suppl.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385188965
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- OpenAlex ID
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https://openalex.org/W4385188965Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3583133.3596393Digital Object Identifier
- Title
-
Scikit-FIBERS: An 'OR'-Rule Discovery Evolutionary Algorithm for Risk Stratification in Right-Censored Survival AnalysesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-07-15Full publication date if available
- Authors
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Ryan J. Urbanowicz, Harsh Bandhey, Malek Kamoun, N. M. Fogarty, Yi-An HsiehList of authors in order
- Landing page
-
https://doi.org/10.1145/3583133.3596393Publisher landing page
- PDF URL
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https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3583133.3596393&file=p1846-urbanowicz-suppl.pdfDirect link to full text PDF
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-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3583133.3596393&file=p1846-urbanowicz-suppl.pdfDirect OA link when available
- Concepts
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Computer science, Population, Event (particle physics), Survival analysis, Algorithm, Risk stratification, Transplantation, Machine learning, Artificial intelligence, Statistics, Mathematics, Medicine, Internal medicine, Quantum mechanics, Environmental health, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(i.e. | 96 |
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| abstract_inverted_index.work. | 198 |
| abstract_inverted_index.FIBERS | 79, 137 |
| abstract_inverted_index.domain | 32 |
| abstract_inverted_index.donors | 52 |
| abstract_inverted_index.future | 72, 197 |
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| abstract_inverted_index.subsets | 64 |
| abstract_inverted_index.variety | 154 |
| abstract_inverted_index.whether | 20, 43 |
| abstract_inverted_index.(AA-MMs) | 50 |
| abstract_inverted_index.Further, | 29 |
| abstract_inverted_index.analysis | 6 |
| abstract_inverted_index.applying | 152 |
| abstract_inverted_index.datasets | 159 |
| abstract_inverted_index.efficacy | 163, 181 |
| abstract_inverted_index.identify | 13, 63 |
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| abstract_inverted_index.learning | 148 |
| abstract_inverted_index.low-risk | 125 |
| abstract_inverted_index.occurred | 23 |
| abstract_inverted_index.package, | 149 |
| abstract_inverted_index.position | 118 |
| abstract_inverted_index.survival | 5, 102, 158, 192 |
| abstract_inverted_index.validate | 161 |
| abstract_inverted_index.Motivated | 74 |
| abstract_inverted_index.OR-rules) | 97 |
| abstract_inverted_index.algorithm | 80, 88, 190 |
| abstract_inverted_index.features, | 46 |
| abstract_inverted_index.high-risk | 109 |
| abstract_inverted_index.instances | 105 |
| abstract_inverted_index.outcomes. | 103 |
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| abstract_inverted_index.problems, | 2, 77 |
| abstract_inverted_index.research, | 9 |
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| abstract_inverted_index.simulator | 194 |
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| abstract_inverted_index.demonstrating | 179 |
| abstract_inverted_index.investigators | 10 |
| abstract_inverted_index.scikit-FIBERS | 162 |
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| abstract_inverted_index.scikit-FIBERS, | 183 |
| abstract_inverted_index.stratification | 58 |
| abstract_inverted_index.'time-to-event' | 1 |
| abstract_inverted_index.transplantation, | 35 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6100000143051147 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.63847823 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |