Development and implementation of the algorithm of differential diagnostics

Uvaliyeva, Indira; Belginova, Saule; Ismukhamedova, Aigerim

doi:10.1109/icaict.2018.8747116

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…3) Rule for identifying B 12 deficiency anemia (macrocytic) ( 23): (23) where M B12 represents the overall severity of pathology for B 12 -deficiency anemia, and B 12 B12 indicates the value of vitamin B 12 from the patient's test results.…”

Section: Rules For Differential Diagnosis Of Clinical and Hematologic...mentioning

confidence: 99%

“…Furthermore, Thinaharan and Thiagarasu describe a rule-based clinical decision support system for the healthcare industry, which integrates machine learning algorithms to further refine the accuracy and efficiency of medical diagnostics, including platelet classification [22]. Implementation of a base of rules in medical information systems promises to streamline the diagnostic workflow, making it more accessible and efficient for healthcare practitioners [23].…”

Section: Introduction (Literary Review)mentioning

confidence: 99%

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Implementation of a Base of Rules for Differential Diagnosis of Clinical and Hematological Syndromes Based on Morphological Classification Algorithm

Uvaliуeva,

Ismukhamedova,

Belginova

et al. 2024

sjaitu

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The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards.

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Section: Rules For Differential Diagnosis Of Clinical and Hematologic...mentioning

confidence: 99%

Section: Introduction (Literary Review)mentioning

confidence: 99%