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A Machine Learning Predictive Model for Sepsis

NCT04771429 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 4500

Last updated 2021-02-25

No results posted yet for this study

Summary

Timely and accurately predicting the occurrence of sepsis and actively intervening in treatment may effectively improve the survival and cure rate of patients with sepsis.

Using machine learning and natural language processing, we want to develop models to 1) identify all children with sepsis admitted to hospital and 2) stratify them to distinguish those who are at high risk of death b) How will you undertake your work? From Shanghai hospitals anf MIMIC III, we will develop a very large dataset of patient admissions for all medical conditions including sepsis from the electronic health record. This data will include both structured data such as age, gender, medications, laboratory values, co-morbidities as well as unstructured data such as discharge summaries and physician notes. Using the dataset, we will train a model through natural language processing and machine learning to be able to identify people admitted with sepsis and identify those patients who will be at high risk of death. We will test the ability of these models to determine our predictive accuracies. We will then test these models at other institutions.

Conditions

Sponsors & Collaborators

  • Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

    lead OTHER

Principal Investigators

  • Xin Sun, MD · Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Eligibility

Min Age
1 Year
Max Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2019-04-01
Primary Completion
2021-03-01
Completion
2021-04-01

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04771429 on ClinicalTrials.gov