Clinical Decision Support System for Unani Medicine Practitioners
This paper presents a new online system designed to help practitioners of Unani Medicine diagnose diseases more effectively. It uses advanced technology to analyze patient symptoms and suggest possible illnesses, making it easier for practitioners to provide care.
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- 1 Following the solution of the little UM data issue, our next objective is to automate the system and make it more user-friendly for forthcoming software developers and knowledge engineers to digitize data and implement their own NLP techniques.
- 2 In our project, we aim to address this gap by developing a comprehensive CDSS tailored to the specific requirements and diagnostic approaches of Unani Medicine.
- 3 A Clinical Decision Support System (CDSS) is an IT-based solution designed to assist healthcare practitioners in making informed clinical decisions.
- 4 CDSS has been built using rule-based, machine learning, and/or AI-based approaches, to analyze patients' data, interpret clinical guidelines, and generate treatment recommendations.
Introduction
12 2.2 CDSS Applications Comparison Matrix . . . . . . . . . . . . . . . . It is still widely used in the subcontinent, particularly in Pakistan and India.
However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices.
The process of diagnosing diseases can be difficult, time-consuming and prone to error.
Research Question
In our project, we aim to address this gap by developing a comprehensive CDSS tailored to the specific requirements and diagnostic approaches of Unani Medicine. Following the solution of the little UM data issue, our next objective is to automate the system and make it more user-friendly for forthcoming software developers and knowledge engineers to digitize data and implement their own NLP techniques.
Methodology
By any means, it is not an exhaustive effort to include each and every disease, symptom, and treatment method related to Unani Medicines. The system leverages a rule-based reasoning engine to suggest remedies based on symptoms and other patient information and uses an ontology-based method to organize knowledge on homeopathic remedies and diseases.
Study Design
The system is made to be simple to use and offers clinicians access to a large database of treatments as well as the option to enter patient data for analysis .
They conducted an analysis of the “medical interview” data to establish an indicator for non-Kampo specialists without technical knowledge to perform suitable traditional medicine.
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Results & Findings
2.1 CDSS Literature Review Matrix . . . . . . . . . . . . . . . . . . . . 16 ix Abbreviations AIML Artifical Intelligence & Machine Learning API Application Programming Interface BERT Bidirectional Encoder Representations from Transformers CDSS Clinical Decision Support System EHR Electronic Healthcare Records GPT Generative Pretrained Transformers ICL In-Context Learning LLMs Large Language Models LMs Lanaguage Models NLP Natural Language.
- 2.1 CDSS Literature Review Matrix . . . . . . . . . . . . . . . . . . . .
- 16 ix Abbreviations AIML Artifical Intelligence & Machine Learning API Application Programming Interface BERT Bidirectional Encoder Representations from Transformers CDSS Clinical Decision Support System EHR Electronic.
- Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages.
- An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes.
- The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database.
Shifting to a Deep Learning Model, data scarcity was addressed through augmentation, though challenges of limited source data and declining quality arose.
Their limitations are Limited generalizability, Lack of validation and Scalability challenges. 2015 Thai Medicine Their focus is on creating a prototype model for a decision-making support tool in Thai traditional medicine.
Practical Applications
The technology was created to give Ayurvedic practitioners access to precise and pertinent patient data so they could make well informed decisions. Additionally, the reliance on data mining techniques may introduce potential biases or limitations in the representation and interpretation of medical information.
If the data is incomplete, inconsistent, or of low quality, it may lead to inaccurate recommendations and unreliable clinical decisions.
Additionally, the system’s reliance on case-based reasoning (CBR) algorithm may limit its ability to handle complex and rare cases that deviate from the existing clinical cases in the dataset.
A Brief History of Unani Medicines
This section provides an overview of Unani Medicines, tracing its origins and evolution, emphasizing its holistic approach and relevance in modern healthcare.
Clinical Decision Support System
This section defines CDSS, explaining its role in assisting healthcare practitioners with informed clinical decisions through the integration of patient data and medical knowledge.
Frequently Asked Questions
In our project, we aim to address this gap by developing a comprehensive CDSS tailored to the specific requirements and diagnostic approaches of Unani Medicine. Following the solution of the little UM data issue, our next objective is to automate the system.
By any means, it is not an exhaustive effort to include each and every disease, symptom, and treatment method related to Unani Medicines. The system leverages a rule-based reasoning engine to suggest remedies based on symptoms and other patient information and uses.
A Clinical Decision Support System (CDSS) is an IT-based solution designed to assist healthcare practitioners in making informed clinical decisions. CDSS has been built using rule-based, machine learning, and/or AI-based approaches, to analyze patients’ data, interpret clinical guidelines, and generate treatment recommendations.
Therefore, there is a pressing need to build a centralized IT-based platform to support different stakeholders like Unani Medicines practitioners, patients, students, and government regulators. The technology was created to give Ayurvedic practitioners access to precise and pertinent patient data so they.
Their limitations are Limited generalizability, Lack of validation and Scalability challenges. 2015 Thai Medicine Their focus is on creating a prototype model for a decision-making support tool in Thai traditional medicine. Shifting to a Deep Learning Model, data scarcity was addressed through.
This paper presents a new online system designed to help practitioners of Unani Medicine diagnose diseases more effectively. It uses advanced technology to analyze patient symptoms and suggest possible illnesses, making it easier for practitioners to provide care.