Prediction of Adverse Events in Stable Non-Variceal Gastrointestinal Bleeding Using Machine Learning Article Swipe
Dong‐Woo Seo
,
Hahn Yi
,
Beomhee Park
,
Youn‐Jung Kim
,
Dae Ho Jung
,
Ilsang Woo
,
Chang Hwan Sohn
,
Byuk Sung Ko
,
Namkug Kim
,
Won Young Kim
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/jcm9082603
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/jcm9082603
Clinical risk-scoring systems are important for identifying patients with upper gastrointestinal bleeding (UGIB) who are at a high risk of hemodynamic instability. We developed an algorithm that predicts adverse events in patients with initially stable non-variceal UGIB using machine learning (ML). Using prospective observational registry, 1439 out of 3363 consecutive patients were enrolled. Primary outcomes included adverse events such as mortality, hypotension, and rebleeding within 7 days. Four machine learning algorithms, namely, logistic regression with regularization (LR), random forest classifier (RF), gradient boosting classifier (GB), and voting classifier (VC), were compared with the Glasgow–Blatchford score (GBS) and Rockall scores. The RF model showed the highest accuracies and significant improvement over conventional methods for predicting mortality (area under the curve: RF 0.917 vs. GBS 0.710), but the performance of the VC model was best in hypotension (VC 0.757 vs. GBS 0.668) and rebleeding within 7 days (VC 0.733 vs. GBS 0.694). Clinically significant variables including blood urea nitrogen, albumin, hemoglobin, platelet, prothrombin time, age, and lactate were identified by the global feature importance analysis. These results suggest that ML models will be useful early predictive tools for identifying high-risk patients with initially stable non-variceal UGIB admitted at an emergency department.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jcm9082603
- https://www.mdpi.com/2077-0383/9/8/2603/pdf?version=1597156146
- OA Status
- gold
- Cited By
- 17
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3048807236
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3048807236Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jcm9082603Digital Object Identifier
- Title
-
Prediction of Adverse Events in Stable Non-Variceal Gastrointestinal Bleeding Using Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-11Full publication date if available
- Authors
-
Dong‐Woo Seo, Hahn Yi, Beomhee Park, Youn‐Jung Kim, Dae Ho Jung, Ilsang Woo, Chang Hwan Sohn, Byuk Sung Ko, Namkug Kim, Won Young KimList of authors in order
- Landing page
-
https://doi.org/10.3390/jcm9082603Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-0383/9/8/2603/pdf?version=1597156146Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2077-0383/9/8/2603/pdf?version=1597156146Direct OA link when available
- Concepts
-
Medicine, Logistic regression, Internal medicine, Adverse effect, Emergency department, Area under the curve, Upper gastrointestinal bleeding, Prospective cohort study, Receiver operating characteristic, Machine learning, Gastroenterology, Surgery, Endoscopy, Computer science, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3, 2023: 4, 2022: 2, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.classifier | 79, 83, 87 |
| abstract_inverted_index.identified | 166 |
| abstract_inverted_index.importance | 171 |
| abstract_inverted_index.mortality, | 60 |
| abstract_inverted_index.predicting | 113 |
| abstract_inverted_index.predictive | 183 |
| abstract_inverted_index.rebleeding | 63, 141 |
| abstract_inverted_index.regression | 73 |
| abstract_inverted_index.algorithms, | 70 |
| abstract_inverted_index.consecutive | 49 |
| abstract_inverted_index.department. | 198 |
| abstract_inverted_index.hemodynamic | 20 |
| abstract_inverted_index.hemoglobin, | 158 |
| abstract_inverted_index.hypotension | 134 |
| abstract_inverted_index.identifying | 6, 186 |
| abstract_inverted_index.improvement | 108 |
| abstract_inverted_index.performance | 126 |
| abstract_inverted_index.prospective | 42 |
| abstract_inverted_index.prothrombin | 160 |
| abstract_inverted_index.significant | 107, 151 |
| abstract_inverted_index.conventional | 110 |
| abstract_inverted_index.hypotension, | 61 |
| abstract_inverted_index.instability. | 21 |
| abstract_inverted_index.non-variceal | 35, 192 |
| abstract_inverted_index.risk-scoring | 1 |
| abstract_inverted_index.observational | 43 |
| abstract_inverted_index.regularization | 75 |
| abstract_inverted_index.gastrointestinal | 10 |
| abstract_inverted_index.Glasgow–Blatchford | 93 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5100728863, https://openalex.org/A5004946653 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I116631150, https://openalex.org/I2801680663, https://openalex.org/I40542001 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8500000238418579 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.85408498 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |