Construct an Approximation Decision Model of Medical Record by Neural Networks - The Ophthalmology Department as an Example

2009 ◽  
pp. 467-480 ◽  
Author(s):  
Yaw-Jen Lin ◽  
Chin-Dr Fan ◽  
Cheng-Chin Huang ◽  
Cheng-Yu Fan
Keyword(s):  
Neural Networks ◽  
Medical Record ◽  
Decision Model

CONSTRUCT AN APPROXIMATION DECISION MODEL OF MEDICAL RECORD BY NEURAL NETWORKS FOR CLINICAL DIAGNOSIS GUIDELINE — THE OPHTHALMOLOGY DEPARTMENT AS AN EXAMPLE

2009 ◽  
Vol 21 (03) ◽  
pp. 201-213
Author(s):  
Jeng-Pang Wang ◽  
Jen-Chien Chien ◽  
Chin-Dr Fan ◽  
Cheng-Chin Huang ◽  
Cheng-Yu Fan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Medical Record ◽  
Clinical Diagnosis ◽  
Decision Model ◽  
Back Propagation ◽  
Diagnosis And Treatment ◽  
Back Propagation Algorithm ◽  
Clinical Practices ◽  
Emergency Calls ◽  
Clinical Emergency

A doctor often has to independently make decisions for the therapy or treatment of patients, such as during clinical emergency calls, some of the most challenging tasks for a doctor. Only if a doctor has highly sophisticated "professional knowledge" and rich "diagnosis and treatment experiences", can he or she be competent for these tasks. Therefore, the inheritance of experience from clinical emergency calls is an important connection in the formative education of doctors. The most traditional formative education of doctors is to have students simulate the diagnosis and treatment modes of their teachers, or carry handbooks containing clinical summaries to perform a diagnosis with them as guidelines. However, this temporary approach of finding answers in books is not only time-consuming, but also detrimental to the confidence of the patients in their doctors. Our research involves the aforementioned clinical practices: clinical emergency calls and doctor formative education. We use ophthalmology as an example and utilize the back-propagation algorithm of the artificial neural networks, to construct an approximate decision model of medical record to guide clinical diagnosis. The doctor can input information such as chief complaint, minor complaints, diagnosis, etc. into the decision model. Correct ophthalmologic approximation medical records are created by referencing diagnosis and treatment to improve the quality of medical treatment and medical care.

scholarly journals Semiotic Aggregation in Deep Learning

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1365
Author(s):  
Bogdan Muşat ◽  
Răzvan Andonie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Decision Model ◽  
Deep Neural Networks ◽  
Neural Model ◽  
Network Layers ◽  
Saliency Maps ◽  
Spatial Entropy ◽  
Insight Into

Convolutional neural networks utilize a hierarchy of neural network layers. The statistical aspects of information concentration in successive layers can bring an insight into the feature abstraction process. We analyze the saliency maps of these layers from the perspective of semiotics, also known as the study of signs and sign-using behavior. In computational semiotics, this aggregation operation (known as superization) is accompanied by a decrease of spatial entropy: signs are aggregated into supersign. Using spatial entropy, we compute the information content of the saliency maps and study the superization processes which take place between successive layers of the network. In our experiments, we visualize the superization process and show how the obtained knowledge can be used to explain the neural decision model. In addition, we attempt to optimize the architecture of the neural model employing a semiotic greedy technique. To the extent of our knowledge, this is the first application of computational semiotics in the analysis and interpretation of deep neural networks.

Stochastic Learning of Strategic Equilibria for Auctions

1999 ◽  
Vol 11 (5) ◽  
pp. 1199-1209
Author(s):  
Samy Bengio ◽  
Yoshua Bengio ◽  
Jacques Robert ◽  
Gilles Bélanger
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Decision Model ◽  
Learning Algorithms ◽  
Space Versus ◽  
Action Space ◽  
Moving Target ◽  
Stochastic Learning ◽  
Strategic Equilibria

This article presents a new application of stochastic adaptive learning algorithms to the computation of strategic equilibria in auctions. The proposed approach addresses the problems of tracking a moving target and balancing exploration (of action space) versus exploitation (of better modeled regions of action space). Neural networks are used to represent a stochastic decision model for each bidder. Experiments confirm the correctness and usefulness of the approach.

More Than Just a Medical Record

ASHA Leader ◽  
2013 ◽  
Vol 18 (8) ◽  
pp. 12-12 ◽  
Keyword(s):  
Medical Record

More Than Just a Medical Record

Coding for Evaluation and Treatment

2008 ◽  
Vol 18 (1) ◽  
pp. 9-20 ◽  
Author(s):  
Mark Kander ◽  
Steve White
Keyword(s):  
Medical Record ◽  
Treatment Session ◽  
Accurate Diagnosis ◽  
Third Party ◽  
Speech Language Pathologists ◽  
Adequate Information ◽  
Diagnosis Codes ◽  
Coding Rules ◽  
Procedure Codes ◽  
The Web

Abstract This article explains the development and use of ICD-9-CM diagnosis codes, CPT procedure codes, and HCPCS supply/device codes. Examples of appropriate coding combinations, and Coding rules adopted by most third party payers are given. Additionally, references for complete code lists on the Web and a list of voice-related CPT code edits are included. The reader is given adequate information to report an evaluation or treatment session with accurate diagnosis, procedure, and supply/device codes. Speech-language pathologists can accurately code services when given adequate resources and rules and are encouraged to insert relevant codes in the medical record rather than depend on billing personnel to accurately provide this information. Consultation is available from the Division 3 Reimbursement Committee members and from [email protected] .

Sign in / Sign up

Export Citation Format

Share Document