| Title |
Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model
|
|---|---|
| Published in |
Frontiers of Medicine, April 2018
|
| DOI | 10.1007/s11684-017-0582-z |
| Pubmed ID | |
| Authors |
Won-Mo Jung, In-Soo Park, Ye-Seul Lee, Chang-Eop Kim, Hyangsook Lee, Dae-Hyun Hahm, Hi-Joon Park, Bo-Hyoung Jang, Younbyoung Chae |
| Abstract |
Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints. The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911; recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Philippines | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 22% |
| Professor | 1 | 11% |
| Professor > Associate Professor | 1 | 11% |
| Student > Master | 1 | 11% |
| Unknown | 4 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 2 | 22% |
| Neuroscience | 1 | 11% |
| Computer Science | 1 | 11% |
| Unknown | 5 | 56% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 14 April 2018.
All research outputs
#19,791,176
of 24,321,976 outputs
Outputs from Frontiers of Medicine
#240
of 367 outputs
Outputs of similar age
#259,904
of 332,841 outputs
Outputs of similar age from Frontiers of Medicine
#8
of 10 outputs
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