Building an automated, machine learning-enabled platform for predicting post-operative complications

被引：2

作者：

Balch, Jeremy A. ^{[1
,2
]}

Rupert, Matthew M. ^{[1
,3
]}

Shickel, Benjamin ^{[1
,3
]}

Ozrazgat-Baslanti, Tezcan ^{[1
,3
]}

Tighe, Patrick J. ^{[4
]}

Efron, Philip A. ^{[2
]}

Upchurch, Gilbert R. ^{[2
]}

Rashidi, Parisa ^{[1
,5
]}

Bihorac, Azra ^{[1
,3
]}

Loftus, Tyler J. ^{[1
,2
]}

机构：

[1] Univ Florida, Intelligent Crit Care Ctr, Gainesville, FL 32611 USA

[2] Univ Florida, Dept Surg, Gainesville, FL 32611 USA

[3] Univ Florida, Dept Med, Gainesville, FL USA

[4] Univ Florida, Dept Anesthesiol, Gainesville, FL USA

[5] Univ Florida, Dept Biomed Engn, Gainesville, FL USA

来源：

PHYSIOLOGICAL MEASUREMENT | 2023年 / 44卷 / 02期

基金：

美国国家科学基金会;

关键词：

clinical decision support; artificial intelligence; machine learning; surgery; post operative complications; VALIDATION; EMERGENCY;

D O I：

10.1088/1361-6579/acb4db

中图分类号：

Q6 [生物物理学];

学科分类号：

071011 ;

摘要：

Objective. In 2019, the University of Florida College of Medicine launched the MySurgeryRisk algorithm to predict eight major post-operative complications using automatically extracted data from the electronic health record. Approach. This project was developed in parallel with our Intelligent Critical Care Center and represents a culmination of efforts to build an efficient and accurate model for data processing and predictive analytics. Main Results and Significance. This paper discusses how our model was constructed and improved upon. We highlight the consolidation of the database, processing of fixed and time-series physiologic measurements, development and training of predictive models, and expansion of those models into different aspects of patient assessment and treatment. We end by discussing future directions of the model.

引用

页数：9

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