Automated identification of postoperative complications within an electronic medical record using natural language processing.

By H. Murff, F. FitzHenry, M. Matheny, Nancy Gentry, Kristen Kotter, K. Crimin, Robert S. Dittus, A. Rosen, P. Elkin, Steven H. Brown and 1 more. Journal of the American Medical Association (JAMA), 2011

476 citations recorded by the source corpus.

Subfields: Epidemiology, Health Informatics, Health Information Management

View Paper: https://www.semanticscholar.org/paper/b88c23712c9fbf42094f32ef8e2b3810c2720d3f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Informatics

1. Chandan K. Reddy
2. Daniela Rus
3. Avishek Choudhury
4. Onur Asan
5. Kaveh Memarzadeh
6. Adam Bohr

In Health Information Management

1. Chandan K. Reddy

Citations in the corpus

• Artificial Intelligence in Surgery: Promises and Perils (2018, methodology): “…been utilized for large-scale database analysis of the EMR to detect adverse events and postoperative complications from physician documentation, 17,18 and many EMR systems now incorporate NLP — for , to achieve automated claims coding — into their underlying software architecture to improve…”
• EpiDEA: Extracting Structured Epilepsy and Seizure Information from Patient Discharge Summaries for Cohort Identification (2012, background): “A key challenge is the extensive occurrence of clinical free text in the discharge summaries, which is a well known issue in processing of patient information in many clinical domains [3,4,5,6].”
• Big Data for Health (2015, background): “HI (EHR) 2974 patients Machine Learning (NLP) Patient records and laboratory results [126]”
• Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review (2020, background): “These studies focused on extracting relevant information [74,77,82,84] to predict bleeding risks among critically ill patients [73], postoperative surgical complications [78], mortality risk [83], and other factors such as lab test results and vital signs [77] influencing patient safety outcomes.”
• Predicting Complications in Critical Care Using Heterogeneous Clinical Data (2016, background): “[36] explores the use of text analytics to predict several postoperative complications.”