Diagnosis of a patient forms a critical component in the provision of health services to patients. Diagnostic decision making is a significant skill that the health care providers need to smart in so as not to miss the critical guides to the determination and the treatment of the patient condition (Willis, Beebee & Lasserson, 2013). Pattern recognition of patients provides an avenue that ensures a relatively accurate diagnosis as depicted in this article.
Pattern recognition of disease symptoms depends on the experience of the healthcare provider in clerking and determining the needs of the patient. In most instances, the patients present with a myriad of signs and symptoms that may be attributed to a particular diagnosis with the consideration of other factors such as environment and the history provided (Haller et al., 2014). The symptoms can also give a guide to more than one condition or merely be an alert of an underlying problem. The pattern recognition of these symptoms can lead to diagnosis in circumstances where the order of events can be simulated to a previous scenario, and this can give a hand in the determination of the patient’s condition and management.
The recognition of the symptoms allows for the assignment of relatively correct probabilities to the potential diagnosis as it takes the practitioner off the creation of the differential diagnoses which might be too large or small for determination of the condition (Bezdek, 2013). It, therefore, serves as a means of narrowing down to the specific signs and symptoms that are familiar to the health worker that allows settling on the condition that has its symptoms coinciding with the identified pattern of symptoms.
The pattern recognition of the symptoms allows the care providers to balance the uncertainties and do a deductive reasoning to arrive at the diagnosis (Willis, Beebee &Lasserson, 2013). For instance, in the tropical areas where malaria is endemic, the presentation of a headache, shivering, fever, joint pain, and jaundice can prompt a diagnosis based on the previous occurrences as well as the cluster of symptoms that are suggestive of malaria.
The pattern recognition assists in the diagnosis of patients alongside the laboratory tests. They tend to be reliable when the patterns of reference are similar to a previous occurrence as well as the scientific presentations of the condition.
Bezdek, J. C. (2013). Pattern recognition with fuzzy objective function algorithms. Springer
Science & Business Media.
Haller, S., Lovblad, K. O., Giannakopoulos, P., & Van De Ville, D. (2014). Multivariate pattern
recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current
challenges, and future trends. Brain Topography, 27(3), 329-337.
Willis, B. H., Beebee, H., & Lasserson, D. S. (2013). Philosophy of science and the diagnostic
process. Family practice, 30(5), 501-505.