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Mendeley readers
Attention Score in Context
| Title |
Building an automated, machine learning-enabled platform for predicting post-operative complications
|
|---|---|
| Published in |
Physiological Measurement, February 2023
|
| DOI | 10.1088/1361-6579/acb4db |
| Pubmed ID | |
| Authors |
Jeremy A Balch, Matthew M Ruppert, Benjamin Shickel, Tezcan Ozrazgat-Baslanti, Patrick J Tighe, Philip A Efron, Gilbert R Upchurch, Parisa Rashidi, Azra Bihorac, Tyler J Loftus |
| Abstract |
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. 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. 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. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 16% |
| Student > Ph. D. Student | 1 | 5% |
| Student > Bachelor | 1 | 5% |
| Student > Doctoral Student | 1 | 5% |
| Student > Master | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 12 | 63% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 11% |
| Medicine and Dentistry | 2 | 11% |
| Engineering | 2 | 11% |
| Unknown | 13 | 68% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 February 2023.
All research outputs
#14,816,962
of 24,833,726 outputs
Outputs from Physiological Measurement
#1,020
of 1,456 outputs
Outputs of similar age
#195,035
of 461,573 outputs
Outputs of similar age from Physiological Measurement
#3
of 10 outputs
Altmetric has tracked 24,833,726 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,456 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 461,573 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.