Predicting Postoperative Sepsis Risk in Diabetic Urolithiasis Patients: A Multimodal Clinical Data-Driven Machine Learning Model Article Swipe
Duolao Wang
,
Deng Pan
,
Zuheng Wang
,
Zhixiong Su
,
Jianchun Mi
,
Ming-Da Wang
,
Chunmeng Wei
,
Junyi Chen
,
Fubo Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7584146/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-7584146/v1
Objective Diabetic patients are more prone to urinary tract infections due to metabolic abnormalities and impaired immune function, which can progress to urosepsis. This study aims to construct and validate an efficient and accurate predictive model, based on multimodal clinical data combined with machine learning techniques, to early assess the risk of postoperative infectious urosepsis in diabetic patients with urinary stones, thereby providing support for clinical decision-making. Methods 532 patients diagnosed with diabetes who underwent surgical treatment for upper urinary tract stones was included. The patients were randomly divided into training (70%) and validation (30%) cohorts. A total of 164 multimodal clinical parameters were included, and those with statistically significant differences ( P < 0.05) were selected. Feature selection was performed using LASSO regression and the Boruta algorithm. Nine machine learning (ML) algorithms were explored to predict the risk of postoperative infectious urosepsis in diabetic patients with urinary stones. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), learning curve, calibration curve, and decision curve analysis (DCA). The contribution of key predictive factors was visualized using the SHAP method. Results Among the 164 multimodal clinical parameters, 59 were significantly associated with the occurrence of postoperative infectious urosepsis. After feature selection, four key parameters were identified: p_IL_6, p_PCTp_ALB, p_T, and p_HR. Among the nine ML algorithms, logistic regression exhibited the best predictive ability. The AUC in the validation cohort was 0.903. The learning curve indicated good and stable model fitting, while the calibration curve demonstrated a high degree of agreement between predicted and actual probabilities. The decision curve analysis revealed that the model provided significant clinical net benefit within a threshold range of 5% to 90%. Conclusion The model constructed using logistic regression performed excellently in predicting the risk of postoperative infectious urosepsis in diabetic patients with urinary stones and can help clinicians better identify high-risk patients. Further prospective validation in multicenter studies is needed to confirm the model's generalizability.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- preprint
- Landing Page
- https://doi.org/10.21203/rs.3.rs-7584146/v1
- https://www.researchsquare.com/article/rs-7584146/latest.pdf
- OA Status
- gold
- References
- 1
- OpenAlex ID
- https://openalex.org/W4415550949
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415550949Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-7584146/v1Digital Object Identifier
- Title
-
Predicting Postoperative Sepsis Risk in Diabetic Urolithiasis Patients: A Multimodal Clinical Data-Driven Machine Learning ModelWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-24Full publication date if available
- Authors
-
Duolao Wang, Deng Pan, Zuheng Wang, Zhixiong Su, Jianchun Mi, Ming-Da Wang, Chunmeng Wei, Junyi Chen, Fubo WangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-7584146/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-7584146/latest.pdfDirect 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.researchsquare.com/article/rs-7584146/latest.pdfDirect OA link when available
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
1Number of works referenced by this work
Full payload
| id | https://openalex.org/W4415550949 |
|---|---|
| doi | https://doi.org/10.21203/rs.3.rs-7584146/v1 |
| ids.doi | https://doi.org/10.21203/rs.3.rs-7584146/v1 |
| ids.openalex | https://openalex.org/W4415550949 |
| fwci | 0.0 |
| type | preprint |
| title | Predicting Postoperative Sepsis Risk in Diabetic Urolithiasis Patients: A Multimodal Clinical Data-Driven Machine Learning Model |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12664 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.8126999735832214 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2739 |
| topics[0].subfield.display_name | Public Health, Environmental and Occupational Health |
| topics[0].display_name | Clinical practice guidelines implementation |
| topics[1].id | https://openalex.org/T11930 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.7450000047683716 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2705 |
| topics[1].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[1].display_name | Cardiac, Anesthesia and Surgical Outcomes |
| topics[2].id | https://openalex.org/T11773 |
| topics[2].field.id | https://openalex.org/fields/36 |
| topics[2].field.display_name | Health Professions |
| topics[2].score | 0.7135999798774719 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/3604 |
| topics[2].subfield.display_name | Emergency Medical Services |
| topics[2].display_name | Healthcare Operations and Scheduling Optimization |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| language | |
| locations[0].id | doi:10.21203/rs.3.rs-7584146/v1 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.researchsquare.com/article/rs-7584146/latest.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.21203/rs.3.rs-7584146/v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5033299238 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2788-2464 |
| authorships[0].author.display_name | Duolao Wang |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I160354086 |
| authorships[0].affiliations[0].raw_affiliation_string | Guangxi Medical University |
| authorships[0].institutions[0].id | https://openalex.org/I160354086 |
| authorships[0].institutions[0].ror | https://ror.org/03dveyr97 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I160354086 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Guangxi Medical University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Dianyu Wang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Guangxi Medical University |
| authorships[1].author.id | https://openalex.org/A5030112824 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7061-9744 |
| authorships[1].author.display_name | Deng Pan |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I129708740, https://openalex.org/I4210134617 |
| authorships[1].affiliations[0].raw_affiliation_string | The Second Affiliated Hospital of Fujian Medical University |
| authorships[1].institutions[0].id | https://openalex.org/I129708740 |
| authorships[1].institutions[0].ror | https://ror.org/050s6ns64 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I129708740 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Fujian Medical University |
| authorships[1].institutions[1].id | https://openalex.org/I4210134617 |
| authorships[1].institutions[1].ror | https://ror.org/03wnxd135 |
| authorships[1].institutions[1].type | healthcare |
| authorships[1].institutions[1].lineage | https://openalex.org/I4210134617 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | Second Affiliated Hospital of Fujian Medical University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Dongwei Pan |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | The Second Affiliated Hospital of Fujian Medical University |
| authorships[2].author.id | https://openalex.org/A5005685844 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Zuheng Wang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I160354086 |
| authorships[2].affiliations[0].raw_affiliation_string | Guangxi Medical University |
| authorships[2].institutions[0].id | https://openalex.org/I160354086 |
| authorships[2].institutions[0].ror | https://ror.org/03dveyr97 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I160354086 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Guangxi Medical University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zuheng Wang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Guangxi Medical University |
| authorships[3].author.id | https://openalex.org/A5101840905 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3497-2073 |
| authorships[3].author.display_name | Zhixiong Su |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I160354086 |
| authorships[3].affiliations[0].raw_affiliation_string | Guangxi Medical University |
| authorships[3].institutions[0].id | https://openalex.org/I160354086 |
| authorships[3].institutions[0].ror | https://ror.org/03dveyr97 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I160354086 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Guangxi Medical University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Zequn Su |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Guangxi Medical University |
| authorships[4].author.id | https://openalex.org/A5091890459 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-9585-9015 |
| authorships[4].author.display_name | Jianchun Mi |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210090327 |
| authorships[4].affiliations[0].raw_affiliation_string | Liuzhou Maternity and Child Healthcare Hospital |
| authorships[4].institutions[0].id | https://openalex.org/I4210090327 |
| authorships[4].institutions[0].ror | https://ror.org/00fbwv278 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210090327 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Liuzhou Maternal and Child Health Hospital |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Junhao Mi |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Liuzhou Maternity and Child Healthcare Hospital |
| authorships[5].author.id | https://openalex.org/A5115694967 |
| authorships[5].author.orcid | https://orcid.org/0009-0007-4739-9492 |
| authorships[5].author.display_name | Ming-Da Wang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I129708740, https://openalex.org/I4210134617 |
| authorships[5].affiliations[0].raw_affiliation_string | The Second Affiliated Hospital of Fujian Medical University |
| authorships[5].institutions[0].id | https://openalex.org/I129708740 |
| authorships[5].institutions[0].ror | https://ror.org/050s6ns64 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I129708740 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Fujian Medical University |
| authorships[5].institutions[1].id | https://openalex.org/I4210134617 |
| authorships[5].institutions[1].ror | https://ror.org/03wnxd135 |
| authorships[5].institutions[1].type | healthcare |
| authorships[5].institutions[1].lineage | https://openalex.org/I4210134617 |
| authorships[5].institutions[1].country_code | CN |
| authorships[5].institutions[1].display_name | Second Affiliated Hospital of Fujian Medical University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Mingda Wang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | The Second Affiliated Hospital of Fujian Medical University |
| authorships[6].author.id | https://openalex.org/A5107947824 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Chunmeng Wei |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I160354086 |
| authorships[6].affiliations[0].raw_affiliation_string | Guangxi Medical University |
| authorships[6].institutions[0].id | https://openalex.org/I160354086 |
| authorships[6].institutions[0].ror | https://ror.org/03dveyr97 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I160354086 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Guangxi Medical University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Chunmeng Wei |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Guangxi Medical University |
| authorships[7].author.id | https://openalex.org/A5101456645 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-0388-2120 |
| authorships[7].author.display_name | Junyi Chen |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I129708740, https://openalex.org/I4210134617 |
| authorships[7].affiliations[0].raw_affiliation_string | The Second Affiliated Hospital of Fujian Medical University |
| authorships[7].institutions[0].id | https://openalex.org/I129708740 |
| authorships[7].institutions[0].ror | https://ror.org/050s6ns64 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I129708740 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Fujian Medical University |
| authorships[7].institutions[1].id | https://openalex.org/I4210134617 |
| authorships[7].institutions[1].ror | https://ror.org/03wnxd135 |
| authorships[7].institutions[1].type | healthcare |
| authorships[7].institutions[1].lineage | https://openalex.org/I4210134617 |
| authorships[7].institutions[1].country_code | CN |
| authorships[7].institutions[1].display_name | Second Affiliated Hospital of Fujian Medical University |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Junyi Chen |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | The Second Affiliated Hospital of Fujian Medical University |
| authorships[8].author.id | https://openalex.org/A5039085769 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-2209-9932 |
| authorships[8].author.display_name | Fubo Wang |
| authorships[8].countries | CN |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I160354086 |
| authorships[8].affiliations[0].raw_affiliation_string | Guangxi Medical University |
| authorships[8].institutions[0].id | https://openalex.org/I160354086 |
| authorships[8].institutions[0].ror | https://ror.org/03dveyr97 |
| authorships[8].institutions[0].type | education |
| authorships[8].institutions[0].lineage | https://openalex.org/I160354086 |
| authorships[8].institutions[0].country_code | CN |
| authorships[8].institutions[0].display_name | Guangxi Medical University |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Fubo Wang |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Guangxi Medical University |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.researchsquare.com/article/rs-7584146/latest.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-26T00:00:00 |
| display_name | Predicting Postoperative Sepsis Risk in Diabetic Urolithiasis Patients: A Multimodal Clinical Data-Driven Machine Learning Model |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12664 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.8126999735832214 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2739 |
| primary_topic.subfield.display_name | Public Health, Environmental and Occupational Health |
| primary_topic.display_name | Clinical practice guidelines implementation |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21203/rs.3.rs-7584146/v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.researchsquare.com/article/rs-7584146/latest.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-7584146/v1 |
| primary_location.id | doi:10.21203/rs.3.rs-7584146/v1 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.researchsquare.com/article/rs-7584146/latest.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-7584146/v1 |
| publication_date | 2025-10-24 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4399368396 |
| referenced_works_count | 1 |
| abstract_inverted_index.( | 112 |
| abstract_inverted_index.A | 97 |
| abstract_inverted_index.a | 250, 274 |
| abstract_inverted_index.5% | 278 |
| abstract_inverted_index.59 | 192 |
| abstract_inverted_index.ML | 219 |
| abstract_inverted_index.an | 31 |
| abstract_inverted_index.in | 56, 144, 230, 290, 298, 315 |
| abstract_inverted_index.is | 318 |
| abstract_inverted_index.of | 52, 99, 140, 175, 199, 253, 277, 294 |
| abstract_inverted_index.on | 38 |
| abstract_inverted_index.to | 7, 12, 22, 27, 47, 136, 279, 320 |
| abstract_inverted_index.164 | 100, 188 |
| abstract_inverted_index.532 | 69 |
| abstract_inverted_index.AUC | 229 |
| abstract_inverted_index.The | 85, 173, 228, 236, 260, 282 |
| abstract_inverted_index.and | 15, 29, 33, 93, 106, 125, 168, 214, 241, 257, 304 |
| abstract_inverted_index.are | 4 |
| abstract_inverted_index.can | 20, 305 |
| abstract_inverted_index.due | 11 |
| abstract_inverted_index.for | 65, 78 |
| abstract_inverted_index.key | 176, 207 |
| abstract_inverted_index.net | 271 |
| abstract_inverted_index.the | 50, 126, 138, 155, 158, 182, 187, 197, 217, 224, 231, 246, 266, 292, 322 |
| abstract_inverted_index.was | 83, 120, 152, 179, 234 |
| abstract_inverted_index.who | 74 |
| abstract_inverted_index.< | 114 |
| abstract_inverted_index.(ML) | 132 |
| abstract_inverted_index.90%. | 280 |
| abstract_inverted_index.Nine | 129 |
| abstract_inverted_index.SHAP | 183 |
| abstract_inverted_index.This | 24 |
| abstract_inverted_index.aims | 26 |
| abstract_inverted_index.area | 156 |
| abstract_inverted_index.best | 225 |
| abstract_inverted_index.data | 41 |
| abstract_inverted_index.four | 206 |
| abstract_inverted_index.good | 240 |
| abstract_inverted_index.help | 306 |
| abstract_inverted_index.high | 251 |
| abstract_inverted_index.into | 90 |
| abstract_inverted_index.more | 5 |
| abstract_inverted_index.nine | 218 |
| abstract_inverted_index.p_T, | 213 |
| abstract_inverted_index.risk | 51, 139, 293 |
| abstract_inverted_index.that | 265 |
| abstract_inverted_index.were | 87, 104, 116, 134, 193, 209 |
| abstract_inverted_index.with | 43, 59, 72, 108, 147, 196, 301 |
| abstract_inverted_index.(30%) | 95 |
| abstract_inverted_index.(70%) | 92 |
| abstract_inverted_index.0.05) | 115 |
| abstract_inverted_index.After | 203 |
| abstract_inverted_index.Among | 186, 216 |
| abstract_inverted_index.LASSO | 123 |
| abstract_inverted_index.Model | 150 |
| abstract_inverted_index.based | 37 |
| abstract_inverted_index.curve | 162, 170, 238, 248, 262 |
| abstract_inverted_index.early | 48 |
| abstract_inverted_index.model | 243, 267, 283 |
| abstract_inverted_index.p_HR. | 215 |
| abstract_inverted_index.prone | 6 |
| abstract_inverted_index.range | 276 |
| abstract_inverted_index.study | 25 |
| abstract_inverted_index.those | 107 |
| abstract_inverted_index.total | 98 |
| abstract_inverted_index.tract | 9, 81 |
| abstract_inverted_index.under | 157 |
| abstract_inverted_index.upper | 79 |
| abstract_inverted_index.using | 122, 154, 181, 285 |
| abstract_inverted_index.which | 19 |
| abstract_inverted_index.while | 245 |
| abstract_inverted_index.(AUC), | 163 |
| abstract_inverted_index.(DCA). | 172 |
| abstract_inverted_index.0.903. | 235 |
| abstract_inverted_index.Boruta | 127 |
| abstract_inverted_index.actual | 258 |
| abstract_inverted_index.assess | 49 |
| abstract_inverted_index.better | 308 |
| abstract_inverted_index.cohort | 233 |
| abstract_inverted_index.curve, | 165, 167 |
| abstract_inverted_index.degree | 252 |
| abstract_inverted_index.immune | 17 |
| abstract_inverted_index.model, | 36 |
| abstract_inverted_index.needed | 319 |
| abstract_inverted_index.stable | 242 |
| abstract_inverted_index.stones | 82, 303 |
| abstract_inverted_index.within | 273 |
| abstract_inverted_index.Feature | 118 |
| abstract_inverted_index.Further | 312 |
| abstract_inverted_index.Methods | 68 |
| abstract_inverted_index.Results | 185 |
| abstract_inverted_index.benefit | 272 |
| abstract_inverted_index.between | 255 |
| abstract_inverted_index.confirm | 321 |
| abstract_inverted_index.divided | 89 |
| abstract_inverted_index.factors | 178 |
| abstract_inverted_index.feature | 204 |
| abstract_inverted_index.machine | 44, 130 |
| abstract_inverted_index.method. | 184 |
| abstract_inverted_index.model's | 323 |
| abstract_inverted_index.p_IL_6, | 211 |
| abstract_inverted_index.predict | 137 |
| abstract_inverted_index.stones, | 61 |
| abstract_inverted_index.stones. | 149 |
| abstract_inverted_index.studies | 317 |
| abstract_inverted_index.support | 64 |
| abstract_inverted_index.thereby | 62 |
| abstract_inverted_index.urinary | 8, 60, 80, 148, 302 |
| abstract_inverted_index.Diabetic | 2 |
| abstract_inverted_index.ability. | 227 |
| abstract_inverted_index.accurate | 34 |
| abstract_inverted_index.analysis | 171, 263 |
| abstract_inverted_index.clinical | 40, 66, 102, 190, 270 |
| abstract_inverted_index.cohorts. | 96 |
| abstract_inverted_index.combined | 42 |
| abstract_inverted_index.decision | 169, 261 |
| abstract_inverted_index.diabetes | 73 |
| abstract_inverted_index.diabetic | 57, 145, 299 |
| abstract_inverted_index.explored | 135 |
| abstract_inverted_index.fitting, | 244 |
| abstract_inverted_index.identify | 309 |
| abstract_inverted_index.impaired | 16 |
| abstract_inverted_index.learning | 45, 131, 164, 237 |
| abstract_inverted_index.logistic | 221, 286 |
| abstract_inverted_index.patients | 3, 58, 70, 86, 146, 300 |
| abstract_inverted_index.progress | 21 |
| abstract_inverted_index.provided | 268 |
| abstract_inverted_index.randomly | 88 |
| abstract_inverted_index.receiver | 159 |
| abstract_inverted_index.revealed | 264 |
| abstract_inverted_index.surgical | 76 |
| abstract_inverted_index.training | 91 |
| abstract_inverted_index.validate | 30 |
| abstract_inverted_index.Objective | 1 |
| abstract_inverted_index.agreement | 254 |
| abstract_inverted_index.construct | 28 |
| abstract_inverted_index.diagnosed | 71 |
| abstract_inverted_index.efficient | 32 |
| abstract_inverted_index.evaluated | 153 |
| abstract_inverted_index.exhibited | 223 |
| abstract_inverted_index.function, | 18 |
| abstract_inverted_index.high-risk | 310 |
| abstract_inverted_index.included, | 105 |
| abstract_inverted_index.included. | 84 |
| abstract_inverted_index.indicated | 239 |
| abstract_inverted_index.metabolic | 13 |
| abstract_inverted_index.operating | 160 |
| abstract_inverted_index.patients. | 311 |
| abstract_inverted_index.performed | 121, 288 |
| abstract_inverted_index.predicted | 256 |
| abstract_inverted_index.providing | 63 |
| abstract_inverted_index.selected. | 117 |
| abstract_inverted_index.selection | 119 |
| abstract_inverted_index.threshold | 275 |
| abstract_inverted_index.treatment | 77 |
| abstract_inverted_index.underwent | 75 |
| abstract_inverted_index.urosepsis | 55, 143, 297 |
| abstract_inverted_index.Conclusion | 281 |
| abstract_inverted_index.algorithm. | 128 |
| abstract_inverted_index.algorithms | 133 |
| abstract_inverted_index.associated | 195 |
| abstract_inverted_index.clinicians | 307 |
| abstract_inverted_index.infections | 10 |
| abstract_inverted_index.infectious | 54, 142, 201, 296 |
| abstract_inverted_index.multimodal | 39, 101, 189 |
| abstract_inverted_index.occurrence | 198 |
| abstract_inverted_index.parameters | 103, 208 |
| abstract_inverted_index.predicting | 291 |
| abstract_inverted_index.predictive | 35, 177, 226 |
| abstract_inverted_index.regression | 124, 222, 287 |
| abstract_inverted_index.selection, | 205 |
| abstract_inverted_index.urosepsis. | 23, 202 |
| abstract_inverted_index.validation | 94, 232, 314 |
| abstract_inverted_index.visualized | 180 |
| abstract_inverted_index.algorithms, | 220 |
| abstract_inverted_index.calibration | 166, 247 |
| abstract_inverted_index.constructed | 284 |
| abstract_inverted_index.differences | 111 |
| abstract_inverted_index.excellently | 289 |
| abstract_inverted_index.identified: | 210 |
| abstract_inverted_index.multicenter | 316 |
| abstract_inverted_index.p_PCTp_ALB, | 212 |
| abstract_inverted_index.parameters, | 191 |
| abstract_inverted_index.performance | 151 |
| abstract_inverted_index.prospective | 313 |
| abstract_inverted_index.significant | 110, 269 |
| abstract_inverted_index.techniques, | 46 |
| abstract_inverted_index.contribution | 174 |
| abstract_inverted_index.demonstrated | 249 |
| abstract_inverted_index.abnormalities | 14 |
| abstract_inverted_index.postoperative | 53, 141, 200, 295 |
| abstract_inverted_index.significantly | 194 |
| abstract_inverted_index.statistically | 109 |
| abstract_inverted_index.characteristic | 161 |
| abstract_inverted_index.probabilities. | 259 |
| abstract_inverted_index.decision-making. | 67 |
| abstract_inverted_index.generalizability. | 324 |
| abstract_inverted_index.<italic>P</italic> | 113 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.6276149 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |