Artificial intelligence (AI) is the study of computer systems capable of performing
tasks that traditionally require human intelligence, and machine learning (ML) is
one mechanism through which an AI system can be developed by creating algorithms that
modify themselves in response to patterns and make inferences when applied to new
data [
1
,
2
,
3
,
4
]. AI has already proven itself to be useful in several fields of medicine, including
radiology, neurosurgery, dermatology, and ophthalmology, in which AI has either matched,
or, in some cases, exceeded the ability of physicians [
5
,
6
,
7
,
- Chen M.C.
- Ball R.L.
- Yang L.
- Moradzadeh N.
- Chapman B.E.
- Larson D.B.
- et al.
Deep learning to classify radiology free-text reports.
Radiology. 2017; 171115https://doi.org/10.1148/radiol.2017171115
8
,
9
,
10
]. Rapidly interpreting clinical data to classify patients and predict outcomes is
paramount to emergency department (ED) operations, with direct impacts on cost, efficiency,
and quality of care. As such, there exists significant potential for improvement in
ED operations through the application of AI.Keywords
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References
- Machine learning: Trends, perspectives, and prospects.Science. 2015; 80: 255-260https://doi.org/10.1126/science.aaa8415
- Predicting the future — big data, machine learning, and clinical medicine.N Engl J Med. 2016; 375: 1216-1219https://doi.org/10.1056/NEJMp1606181
- Kaufmann M. Machine Learning, a Multistrategy Approach. 1994
- Principles of Artificial Intelligence.Tioga Pub. Co, Palo Alto, CA1980: 476
- Natural and artificial intelligence in neurosurgery: a systemic review.Neurosurgery. 2017; 0: 1-12https://doi.org/10.1093/neuros/nyx384
- Machine learning in medicine.Circulation. 2015; 132: 1920-1930https://doi.org/10.1161/CIRCULATIONAHA.115.001593
- Deep learning to classify radiology free-text reports.Radiology. 2017; 171115https://doi.org/10.1148/radiol.2017171115
- Evaluation of automated teleretinal screening program for diabetic retinopathy.JAMA Ophthalmol. 2016; 134: 204https://doi.org/10.1001/jamaophthalmol.2015.5083
- Dermatologist-level classification of skin cancer with deep neural networks.Nature. 2017; 542: 115-118https://doi.org/10.1038/nature21056
- Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms.Expert Syst Appl. 2014; 41: 1476-1482https://doi.org/10.1016/J.ESWA.2013.08.044
- Emergency severity index, version 4: implementation handbook.Emerg Med. 2005; : 1-5
- More patients are triaged using the emergency severity index than any other triage acuity system in the United States.Acad Emerg Med. 2012; 19: 106-109https://doi.org/10.1111/j.1553-2712.2011.01240.x
Rui P, Kang K. National Hospital Ambulatory Medical Care Survey: 2014 Emergency Department Summary Tables.
- Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index.Ann Emerg Med. 2017; https://doi.org/10.1016/j.annemergmed.2017.08.005
- Patient classification algorithm at urgency care area of a hospital based on.J Med Inform Technol. 2013; 22: 87-94
- Risk scoring for prediction of acute cardiac complications from imbalanced clinical data.IEEE J Biomed Health Inform. 2014; 18: 1894-1902https://doi.org/10.1109/JBHI.2014.2303481
- Machine learning for predicting sepsis in-hospital mortality: an important start.Acad Emerg Med. 2016; 23: 1307https://doi.org/10.1111/acem.13009
- The TIMI risk score for unstable angina/non–ST elevation MI.JAMA. 2000; 284: 835https://doi.org/10.1001/jama.284.7.835
- Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach.Acad Emerg Med. 2016; 23: 269-278https://doi.org/10.1111/acem.12876
- Automated critical test findings identification and online notification system using artificial intelligence in imaging.Radiology. 2017; 0: 1-9https://doi.org/10.1148/radiol.2017162664
- Computer-aided assessment of head computed tomography (CT) studies in patients with suspected traumatic brain injury.J Neurotrauma. 2008; 25: 1163-1172https://doi.org/10.1089/neu.2008.0590
- Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans.Int J Comput Assist Radiol Surg. 2012; 7: 507-516https://doi.org/10.1007/s11548-011-0664-3
- Automated assessment of midline shift in head injury patients.Clin Neurol Neurosurg. 2010; 112: 785-790https://doi.org/10.1016/j.clineuro.2010.06.020
- Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures?.Acta Orthop. 2017; 3674: 1-6https://doi.org/10.1080/17453674.2017.1344459
- ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.Eur Spine J. 2017; 26: 1374-1383https://doi.org/10.1007/s00586-017-4956-3
- Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.J Am Med Inform Assoc. 2013; 20: e212-20https://doi.org/10.1136/amiajnl-2013-001962
- (Jack). Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.Surgery. 2011; 149: 87-93https://doi.org/10.1016/j.surg.2010.03.023
- Application of artificial neural networks to clinical medicine.Lancet. 1995; 346: 1135-1138https://doi.org/10.1016/S0140-6736(95)91804-3
- Natural language processing and its future in medicine.Acad Med. 1999; 74: 890-895https://doi.org/10.1097/00001888-199908000-00012
- Comparison of machine learning classifiers for influenza detection from emergency department free-text reports.J Biomed Inform. 2015; 58: 60-69https://doi.org/10.1016/j.jbi.2015.08.019
- Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning.PLoS One. 2017; 12e0174708https://doi.org/10.1371/journal.pone.0174708
Article Info
Publication History
Published online: January 04, 2018
Accepted:
January 3,
2018
Received:
January 3,
2018
Identification
Copyright
© 2018 Elsevier Inc. All rights reserved.