Non-Knowledge Based Decision Support System

Abstract

The Medical Decision Support System (MDSS) delineates knowledge and supports optimal therapeutic decisions to assist physicians, clinicians, or other healthcare professionals with knowledge and person-specific information in making clinical decisions using targeted medical knowledge which reduces prescribing errors. MDSS are frequently classified as Knowledge base or learning base. Learning bases derive mapping using machine learning (ML), artificial intelligence (AI), or statistical pattern recognition from knowledge-driven human-engineered mapping of literature based, patient-oriented, and practically based data to recommendations. This study offers a merging decision-making support framework that combines a knowledge-based system with a learning-based approach to give and realms medical aid for decision-making despite of events with missing information, with a powerful solution to the information challenge. This work integrates the idea of the data mining of knowledge bases (KB) with artificial intelligence in this context intended to support a combined module of integrated medical decision (AI) In healthcare choices around drug prescribing and evolving medical conditions, poorly experienced inexperienced medical providers can develop an infrastructure to address issues of highly complexity. The rule-based system has its drawbacks, such that the basis of the knowledge uses an explicit system to administer patent medicines with knowledge of each field. However, where the knowledge base available data is unknown, the machine learning strategies are used to respond to a query. The skeleton is query oriented and can be adapted to be used for various user interfaces such as desktops, web browsers and mobile apps. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.