View in EDS HTML Full Text PDF Full Text

A practical and explainable machine learning model based on conventional clinical features for predicting mortality in patients with sepsis.

Saved in:
Bibliographic Details
Title: A practical and explainable machine learning model based on conventional clinical features for predicting mortality in patients with sepsis.
Authors: Huang, Xintong1,2 (AUTHOR), Chen, Yingxu1 (AUTHOR), Li, Tongda1 (AUTHOR), Ge, Lisirui1 (AUTHOR), Yang, Lu3 (AUTHOR), Peng, Tao1,2 (AUTHOR) pt528@swmu.edu.cn, Chen, Guangxiang3 (AUTHOR) cgx23ly2002@163.com, Wang, Maohua1,2 (AUTHOR) wangmaohua@swmu.edu.cn
Source: Anesthesiology & Perioperative Science. Dec2025, Vol. 3 Issue 4, p1-13. 13p.
Database: Academic Search Ultimate
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: asn
DbLabel: Academic Search Ultimate
An: 190291358
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A practical and explainable machine learning model based on conventional clinical features for predicting mortality in patients with sepsis.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Huang%2C+Xintong%22">Huang, Xintong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Yingxu%22">Chen, Yingxu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Tongda%22">Li, Tongda</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ge%2C+Lisirui%22">Ge, Lisirui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Lu%22">Yang, Lu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Peng%2C+Tao%22">Peng, Tao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> pt528@swmu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Guangxiang%22">Chen, Guangxiang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> cgx23ly2002@163.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Maohua%22">Wang, Maohua</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wangmaohua@swmu.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Anesthesiology+%26+Perioperative+Science%22">Anesthesiology & Perioperative Science</searchLink>. Dec2025, Vol. 3 Issue 4, p1-13. 13p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=190291358
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s44254-025-00157-8
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 1
    Titles:
      – TitleFull: A practical and explainable machine learning model based on conventional clinical features for predicting mortality in patients with sepsis.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Huang, Xintong
      – PersonEntity:
          Name:
            NameFull: Chen, Yingxu
      – PersonEntity:
          Name:
            NameFull: Li, Tongda
      – PersonEntity:
          Name:
            NameFull: Ge, Lisirui
      – PersonEntity:
          Name:
            NameFull: Yang, Lu
      – PersonEntity:
          Name:
            NameFull: Peng, Tao
      – PersonEntity:
          Name:
            NameFull: Chen, Guangxiang
      – PersonEntity:
          Name:
            NameFull: Wang, Maohua
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 27318389
          Numbering:
            – Type: volume
              Value: 3
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Anesthesiology & Perioperative Science
              Type: main
ResultId 1