Process Mining for Trauma Resuscitation.

Sen Yang, Jingyuan Li, Xiaoyi Tang, Shuhong Chen, Ivan Marsic, Randall S Burd

Abstract read

In one paragraph

Article in The IEEE intelligent informatics bulletin, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sen YangDepartment of Electrical and Computer Engineering at Rutgers University, NJ, USA. sy358@rutgers.edu.

Jingyuan LiDepartment of Electrical and Computer Engineering at Rutgers University, NJ, USA. j12056@rutgers.edu.

Xiaoyi TangDepartment of Electrical and Computer Engineering at Rutgers University, NJ, USA. xt59@rutgers.edu.

Shuhong ChenDepartment of Electrical and Computer Engineering at Rutgers University, NJ, USA. sc1624@rutgers.edu.

Ivan MarsicDepartment of Electrical and Computer Engineering at Rutgers University, NJ, USA. marsic@jove.rutgers.edu.

Randall S BurdChildren's National Medical Center, Washington DC, USA. rburd@childrensnational.org.

Funding

Intention-aware Recommender System for Improving Trauma Resuscitation OutcomesR01LM011834 · NLM · CHILDREN'S RESEARCH INSTITUTE · PI BURD, RANDALL S., MARSIC, IVAN · 2014 to 2023

$4.7M

NLM NIH HHS R01 LM011834

6 · The paper itself

Abstract

We present our process mining system for analyzing the trauma resuscitation process to improve medical team performance and patient outcomes. Our system has four main parts: trauma resuscitation process model discovery, process model enhancement (or repair), process deviation analysis, and process recommendation. We developed novel algorithms to address the technical challenges for each problem. We validated our system on real-world trauma resuscitation data from the Children's National Medical Center (CNMC), a level 1 trauma center. Our results show our system's capability of supporting complex medical processes. Our approaches were also implemented in an interactive visual analytic tool.

Indexed as

Medical Process DiagnosisProcess MiningTrauma Resuscitation

Identifiers

PMID30443472

PMCPMC6233890

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.