A Laboratory Test Expert System for Clinical Diagnosis Support in Primary Health Care

Abstract

Clinical Decision Support Systems have the potential to reduce lack of communication and errors in diagnostic steps in primary health care. Literature reports have showed great advances in clinical decision support systems in the recent years, which have proven its usefulness in improving the quality of care. However, most of these systems are focused on specific areas of diseases. In this way, we propose a rule-based expert system, which supports clinicians in primary health care, providing a list of possible diseases regarding patient's laboratory tests results in order to assist previous diagnosis. Our system also allows storing and retrieving patient's data and the history of patient's analyses, establishing a basis for coordination between the various health care levels. A validation step and speed performance tests were made to check the quality of the system. We conclude that our system could improve clinician accuracy and speed, resulting in more efficiency and better quality of service. Finally, we propose some recommendations for further research.

Filtering of Irrelevant Clashes Detected by BIM Software Using a Hybrid Method of Rule-Based Reasoning and Supervised Machine Learning

“…Clash avoidance begins with the modeling method during the design phase, emphasizing the collaboration and coordination among the design teams to avoid the occurrence of clashes from the beginning. Mehrbod et al [18] established taxonomy to classify the causes of clashes into three categories, namely, process-based, model-based, and physical design [12]. They aimed to understand the causes of design conflicts and the consideration factors for conflict/clash resolution.…”

“…Rule-based systems, also known as rule-based expert systems, have been commonly used in many fields such as medical, engineering, manufacturing, education, etc. since the 1980s and have been proved to be effective in pattern recognition, diagnosis, decision-making, control, planning, and so on due to the transparency of knowledge reasoning and consistency of reasoning results [12]. However, despite their advantages, rule-based systems require a considerable amount of time to acquire the knowledge that is needed for reasoning.…”

“…They found that the domain knowledge is usually dispersed and fragmented, so rule acquisition requires human experts from many professional fields. Therefore, the process of knowledge representation and acquisition is often a complex and time-consuming task [12]. The other is the use of machine learning methods that train classifiers of machine learning through historical data to filter out irrelevant clashes [6].…”

Optimasi Derajat Keanggotaan Fuzzy Tsukamoto Menggunakan Algoritma Genetika Untuk Diagnosis Penyakit Sapi Potong

“…Kegagalan suatu diagnosis akan memberikan beberapa permasalahan seperti keselamatan hidup ternak (Schiff, 2008). Ternak yang sedang terjangkit penyakit, risiko hidupnya sangat bergantung terhadap diagnosis awal seorang dokter (Fernandez-Millan et al, 2015). Sehingga, ketepatan dalam mendiagnosis penyakit pada pasien sangat diperlukan.…”

A Framework for Systematizing Personalized Thai Herbal Medicine: A Case Study Covering Gynecological Conditions

“…Similar to Nopparatkiat et al (2014) who proposed the ES on recommending skin problem treatment in TTM, based on rule-based systems with the total of 416 rules from Thai herbal cosmetics formulations texts. Fernandez-Millan et al (2015) recently proposed the ES on clinical diagnosis in primary healthcare. Moreover, some data mining techniques have been used to support CAM and TM in terms of recommended herbal compositions for treatments.…”