Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks Article Swipe
Steven Walczak
,
Emad Mikhail
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.4018/978-1-6684-6299-7.ch003
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.4018/978-1-6684-6299-7.ch003
This chapter explores valuating the efficacy of using artificial neural networks (ANNs) for predicting the estimated blood loss (EBL) and also transfusion requirements of myomectomy patients. All 146 myomectomy surgeries performed over a 6-year period from a single site are captured. Records were removed for various reasons, leaving 96 cases. Backpropagation and radial basis function ANN models were developed to predict EBL and perioperative transfusion needs along with a regression model. The single hidden layer backpropagation ANN models performed the best for both prediction problems. EBL was predicted on average within 127.33 ml of measured blood loss, and transfusions were predicted with 71.4% sensitivity and 85.4% specificity. A combined ANN ensemble model using the output of the EBL ANN as an input variable to the transfusion prediction ANN was developed and resulted in 100% sensitivity and 62.9% specificity. The preoperative identification of large EBL or transfusion need can assist caregivers in better planning for possible post-operative morbidity and mortality.
Related Topics
Concepts
Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.4018/978-1-6684-6299-7.ch003
- OA Status
- hybrid
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281783422
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4281783422Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.4018/978-1-6684-6299-7.ch003Digital Object Identifier
- Title
-
Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural NetworksWork title
- Type
-
book-chapterOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-06Full publication date if available
- Authors
-
Steven Walczak, Emad MikhailList of authors in order
- Landing page
-
https://doi.org/10.4018/978-1-6684-6299-7.ch003Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.4018/978-1-6684-6299-7.ch003Direct OA link when available
- Concepts
-
Artificial neural network, Blood loss, Backpropagation, Perioperative, Medicine, Blood transfusion, Surgery, Artificial intelligence, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
66Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4281783422 |
|---|---|
| doi | https://doi.org/10.4018/978-1-6684-6299-7.ch003 |
| ids.doi | https://doi.org/10.4018/978-1-6684-6299-7.ch003 |
| ids.openalex | https://openalex.org/W4281783422 |
| fwci | 0.0 |
| type | book-chapter |
| title | Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 46 |
| biblio.first_page | 30 |
| topics[0].id | https://openalex.org/T10916 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9771000146865845 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2746 |
| topics[0].subfield.display_name | Surgery |
| topics[0].display_name | Surgical Simulation and Training |
| topics[1].id | https://openalex.org/T11636 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9646000266075134 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2718 |
| topics[1].subfield.display_name | Health Informatics |
| topics[1].display_name | Artificial Intelligence in Healthcare and Education |
| topics[2].id | https://openalex.org/T11930 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9514999985694885 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2705 |
| topics[2].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[2].display_name | Cardiac, Anesthesia and Surgical Outcomes |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C50644808 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6566709280014038 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[0].display_name | Artificial neural network |
| concepts[1].id | https://openalex.org/C2991743468 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6380152106285095 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q166019 |
| concepts[1].display_name | Blood loss |
| concepts[2].id | https://openalex.org/C155032097 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5891866683959961 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q798503 |
| concepts[2].display_name | Backpropagation |
| concepts[3].id | https://openalex.org/C31174226 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5674336552619934 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q64855140 |
| concepts[3].display_name | Perioperative |
| concepts[4].id | https://openalex.org/C71924100 |
| concepts[4].level | 0 |
| concepts[4].score | 0.5483187437057495 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[4].display_name | Medicine |
| concepts[5].id | https://openalex.org/C2780014101 |
| concepts[5].level | 2 |
| concepts[5].score | 0.544962465763092 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q183605 |
| concepts[5].display_name | Blood transfusion |
| concepts[6].id | https://openalex.org/C141071460 |
| concepts[6].level | 1 |
| concepts[6].score | 0.32066527009010315 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q40821 |
| concepts[6].display_name | Surgery |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.2602008581161499 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.24127811193466187 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| keywords[0].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[0].score | 0.6566709280014038 |
| keywords[0].display_name | Artificial neural network |
| keywords[1].id | https://openalex.org/keywords/blood-loss |
| keywords[1].score | 0.6380152106285095 |
| keywords[1].display_name | Blood loss |
| keywords[2].id | https://openalex.org/keywords/backpropagation |
| keywords[2].score | 0.5891866683959961 |
| keywords[2].display_name | Backpropagation |
| keywords[3].id | https://openalex.org/keywords/perioperative |
| keywords[3].score | 0.5674336552619934 |
| keywords[3].display_name | Perioperative |
| keywords[4].id | https://openalex.org/keywords/medicine |
| keywords[4].score | 0.5483187437057495 |
| keywords[4].display_name | Medicine |
| keywords[5].id | https://openalex.org/keywords/blood-transfusion |
| keywords[5].score | 0.544962465763092 |
| keywords[5].display_name | Blood transfusion |
| keywords[6].id | https://openalex.org/keywords/surgery |
| keywords[6].score | 0.32066527009010315 |
| keywords[6].display_name | Surgery |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.2602008581161499 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.24127811193466187 |
| keywords[8].display_name | Computer science |
| language | en |
| locations[0].id | doi:10.4018/978-1-6684-6299-7.ch003 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306463409 |
| locations[0].source.issn | |
| locations[0].source.type | ebook platform |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | IGI Global eBooks |
| locations[0].source.host_organization | https://openalex.org/P4310320424 |
| locations[0].source.host_organization_name | IGI Global |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320424 |
| locations[0].source.host_organization_lineage_names | IGI Global |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | book-chapter |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Research Anthology on Advancements in Women's Health and Reproductive Rights |
| locations[0].landing_page_url | https://doi.org/10.4018/978-1-6684-6299-7.ch003 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5044878328 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0449-6272 |
| authorships[0].author.display_name | Steven Walczak |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I2613432 |
| authorships[0].affiliations[0].raw_affiliation_string | University of South Florida, USA |
| authorships[0].institutions[0].id | https://openalex.org/I2613432 |
| authorships[0].institutions[0].ror | https://ror.org/032db5x82 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I2613432 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of South Florida |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Steven Walczak |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | University of South Florida, USA |
| authorships[1].author.id | https://openalex.org/A5003495906 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-1502-3654 |
| authorships[1].author.display_name | Emad Mikhail |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I2613432 |
| authorships[1].affiliations[0].raw_affiliation_string | University of South Florida, USA |
| authorships[1].institutions[0].id | https://openalex.org/I2613432 |
| authorships[1].institutions[0].ror | https://ror.org/032db5x82 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I2613432 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of South Florida |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Emad Mikhail |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | University of South Florida, USA |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.4018/978-1-6684-6299-7.ch003 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10916 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9771000146865845 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2746 |
| primary_topic.subfield.display_name | Surgery |
| primary_topic.display_name | Surgical Simulation and Training |
| related_works | https://openalex.org/W2894173309, https://openalex.org/W4387932263, https://openalex.org/W2157746493, https://openalex.org/W2371065793, https://openalex.org/W1977222966, https://openalex.org/W2357579020, https://openalex.org/W2807843051, https://openalex.org/W24224509, https://openalex.org/W2913933879, https://openalex.org/W2351906597 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.4018/978-1-6684-6299-7.ch003 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306463409 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | ebook platform |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | IGI Global eBooks |
| best_oa_location.source.host_organization | https://openalex.org/P4310320424 |
| best_oa_location.source.host_organization_name | IGI Global |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320424 |
| best_oa_location.source.host_organization_lineage_names | IGI Global |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | book-chapter |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Research Anthology on Advancements in Women's Health and Reproductive Rights |
| best_oa_location.landing_page_url | https://doi.org/10.4018/978-1-6684-6299-7.ch003 |
| primary_location.id | doi:10.4018/978-1-6684-6299-7.ch003 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306463409 |
| primary_location.source.issn | |
| primary_location.source.type | ebook platform |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IGI Global eBooks |
| primary_location.source.host_organization | https://openalex.org/P4310320424 |
| primary_location.source.host_organization_name | IGI Global |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320424 |
| primary_location.source.host_organization_lineage_names | IGI Global |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | book-chapter |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Research Anthology on Advancements in Women's Health and Reproductive Rights |
| primary_location.landing_page_url | https://doi.org/10.4018/978-1-6684-6299-7.ch003 |
| publication_date | 2022-05-06 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W198948752, https://openalex.org/W2045485498, https://openalex.org/W2347075554, https://openalex.org/W2763911907, https://openalex.org/W2323929895, https://openalex.org/W2096808849, https://openalex.org/W2108981885, https://openalex.org/W4248110985, https://openalex.org/W1972669243, https://openalex.org/W2014391695, https://openalex.org/W2666784499, https://openalex.org/W2103587877, https://openalex.org/W73156971, https://openalex.org/W4296886862, https://openalex.org/W1989149221, https://openalex.org/W1981976602, https://openalex.org/W2275505746, https://openalex.org/W2149541197, https://openalex.org/W1975330545, https://openalex.org/W2624381351, https://openalex.org/W2154789382, https://openalex.org/W2063762895, https://openalex.org/W2034865452, https://openalex.org/W2138914466, https://openalex.org/W2115971566, https://openalex.org/W2002160322, https://openalex.org/W155311401, https://openalex.org/W2016881343, https://openalex.org/W4238798302, https://openalex.org/W2588238245, https://openalex.org/W1979849615, https://openalex.org/W2080712219, https://openalex.org/W1993162856, https://openalex.org/W2018804570, https://openalex.org/W2002622807, https://openalex.org/W1986294961, https://openalex.org/W2111547563, https://openalex.org/W2128124510, https://openalex.org/W170997941, https://openalex.org/W2158201944, https://openalex.org/W2101771110, https://openalex.org/W2171277043, https://openalex.org/W2333069786, https://openalex.org/W2937687211, https://openalex.org/W2047837261, https://openalex.org/W2038886391, https://openalex.org/W2568988948, https://openalex.org/W166951565, https://openalex.org/W2036073298, https://openalex.org/W106890075, https://openalex.org/W2040989045, https://openalex.org/W2346062110, https://openalex.org/W2743488430, https://openalex.org/W2121394390, https://openalex.org/W2037157836, https://openalex.org/W2898655910, https://openalex.org/W1985475871, https://openalex.org/W2095110349, https://openalex.org/W28412257, https://openalex.org/W2145587861, https://openalex.org/W2019695309, https://openalex.org/W2753968605, https://openalex.org/W2082814894, https://openalex.org/W2089507735, https://openalex.org/W2100128988, https://openalex.org/W1998329793 |
| referenced_works_count | 66 |
| abstract_inverted_index.A | 107 |
| abstract_inverted_index.a | 32, 36, 68 |
| abstract_inverted_index.96 | 48 |
| abstract_inverted_index.an | 120 |
| abstract_inverted_index.as | 119 |
| abstract_inverted_index.in | 132, 150 |
| abstract_inverted_index.ml | 92 |
| abstract_inverted_index.of | 6, 23, 93, 115, 141 |
| abstract_inverted_index.on | 88 |
| abstract_inverted_index.or | 144 |
| abstract_inverted_index.to | 59, 123 |
| abstract_inverted_index.146 | 27 |
| abstract_inverted_index.ANN | 55, 76, 109, 118, 127 |
| abstract_inverted_index.All | 26 |
| abstract_inverted_index.EBL | 61, 85, 117, 143 |
| abstract_inverted_index.The | 71, 138 |
| abstract_inverted_index.and | 19, 51, 62, 97, 104, 130, 135, 157 |
| abstract_inverted_index.are | 39 |
| abstract_inverted_index.can | 147 |
| abstract_inverted_index.for | 12, 44, 81, 153 |
| abstract_inverted_index.the | 4, 14, 79, 113, 116, 124 |
| abstract_inverted_index.was | 86, 128 |
| abstract_inverted_index.100% | 133 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.also | 20 |
| abstract_inverted_index.best | 80 |
| abstract_inverted_index.both | 82 |
| abstract_inverted_index.from | 35 |
| abstract_inverted_index.loss | 17 |
| abstract_inverted_index.need | 146 |
| abstract_inverted_index.over | 31 |
| abstract_inverted_index.site | 38 |
| abstract_inverted_index.were | 42, 57, 99 |
| abstract_inverted_index.with | 67, 101 |
| abstract_inverted_index.(EBL) | 18 |
| abstract_inverted_index.62.9% | 136 |
| abstract_inverted_index.71.4% | 102 |
| abstract_inverted_index.85.4% | 105 |
| abstract_inverted_index.along | 66 |
| abstract_inverted_index.basis | 53 |
| abstract_inverted_index.blood | 16, 95 |
| abstract_inverted_index.input | 121 |
| abstract_inverted_index.large | 142 |
| abstract_inverted_index.layer | 74 |
| abstract_inverted_index.loss, | 96 |
| abstract_inverted_index.model | 111 |
| abstract_inverted_index.needs | 65 |
| abstract_inverted_index.using | 7, 112 |
| abstract_inverted_index.(ANNs) | 11 |
| abstract_inverted_index.127.33 | 91 |
| abstract_inverted_index.6-year | 33 |
| abstract_inverted_index.assist | 148 |
| abstract_inverted_index.better | 151 |
| abstract_inverted_index.cases. | 49 |
| abstract_inverted_index.hidden | 73 |
| abstract_inverted_index.model. | 70 |
| abstract_inverted_index.models | 56, 77 |
| abstract_inverted_index.neural | 9 |
| abstract_inverted_index.output | 114 |
| abstract_inverted_index.period | 34 |
| abstract_inverted_index.radial | 52 |
| abstract_inverted_index.single | 37, 72 |
| abstract_inverted_index.within | 90 |
| abstract_inverted_index.Records | 41 |
| abstract_inverted_index.average | 89 |
| abstract_inverted_index.chapter | 1 |
| abstract_inverted_index.leaving | 47 |
| abstract_inverted_index.predict | 60 |
| abstract_inverted_index.removed | 43 |
| abstract_inverted_index.various | 45 |
| abstract_inverted_index.combined | 108 |
| abstract_inverted_index.efficacy | 5 |
| abstract_inverted_index.ensemble | 110 |
| abstract_inverted_index.explores | 2 |
| abstract_inverted_index.function | 54 |
| abstract_inverted_index.measured | 94 |
| abstract_inverted_index.networks | 10 |
| abstract_inverted_index.planning | 152 |
| abstract_inverted_index.possible | 154 |
| abstract_inverted_index.reasons, | 46 |
| abstract_inverted_index.resulted | 131 |
| abstract_inverted_index.variable | 122 |
| abstract_inverted_index.captured. | 40 |
| abstract_inverted_index.developed | 58, 129 |
| abstract_inverted_index.estimated | 15 |
| abstract_inverted_index.morbidity | 156 |
| abstract_inverted_index.patients. | 25 |
| abstract_inverted_index.performed | 30, 78 |
| abstract_inverted_index.predicted | 87, 100 |
| abstract_inverted_index.problems. | 84 |
| abstract_inverted_index.surgeries | 29 |
| abstract_inverted_index.valuating | 3 |
| abstract_inverted_index.artificial | 8 |
| abstract_inverted_index.caregivers | 149 |
| abstract_inverted_index.mortality. | 158 |
| abstract_inverted_index.myomectomy | 24, 28 |
| abstract_inverted_index.predicting | 13 |
| abstract_inverted_index.prediction | 83, 126 |
| abstract_inverted_index.regression | 69 |
| abstract_inverted_index.sensitivity | 103, 134 |
| abstract_inverted_index.transfusion | 21, 64, 125, 145 |
| abstract_inverted_index.preoperative | 139 |
| abstract_inverted_index.requirements | 22 |
| abstract_inverted_index.specificity. | 106, 137 |
| abstract_inverted_index.transfusions | 98 |
| abstract_inverted_index.perioperative | 63 |
| abstract_inverted_index.identification | 140 |
| abstract_inverted_index.post-operative | 155 |
| abstract_inverted_index.Backpropagation | 50 |
| abstract_inverted_index.backpropagation | 75 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.8700000047683716 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.21139873 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |