Editor, Editors, USER, admin, Bureaucrats, Check users, dev, editor, founder, Interface administrators, oversight, Suppressors, Administrators, translator
10,784
edits
Difference between revisions of "Logic of medical language"
no edit summary
| Line 182: | Line 182: | ||
Furthermore, modern diagnostic processes increasingly rely on machine language and non-verbal signals, especially in the era of digital health technologies. Electrophysiological tests, imaging results, and genetic data are forms of "machine language" that require interpretation by clinicians. While these data streams provide invaluable insights, they also add layers of complexity to the diagnostic process, particularly when combined with vague or ambiguous verbal reports from patients. As such, a clinician must integrate both verbal and non-verbal information to form a holistic understanding of a patient's condition. | Furthermore, modern diagnostic processes increasingly rely on machine language and non-verbal signals, especially in the era of digital health technologies. Electrophysiological tests, imaging results, and genetic data are forms of "machine language" that require interpretation by clinicians. While these data streams provide invaluable insights, they also add layers of complexity to the diagnostic process, particularly when combined with vague or ambiguous verbal reports from patients. As such, a clinician must integrate both verbal and non-verbal information to form a holistic understanding of a patient's condition. | ||
In this chapter, we explored the complexities of medical language and its implications for clinical diagnosis. We also introduced the concept of "'''encrypted machine language''' {{Tooltip|2=Let's consider a patient, Mr. Rossi, who presents with symptoms of facial pain and difficulty chewing. These symptoms can be interpreted in various ways depending on the specialist's expertise: a dentist might consider them indicative of temporomandibular disorder (TMD), while a neurologist could interpret them as neuropathic pain.'''Coding Symptoms:''' Symptoms:<math>S_1</math>: Facial pain and <math>S_2</math>: Difficulty chewing. Diagnoses: <math>D_1</math>: Temporomandibular Disorder (TMD) and <math>D_2</math>: Neuropathic Pain (nOP) {{(Tooltip|Mathematical Formalism) We can formalize the process of decoding symptoms using a conditional probability function. Let’s define <math>P(D | S)</math> as the probability of a diagnosis <math>D</math> given the presence of symptoms <math>S</math>. <math> | In this chapter, we explored the complexities of medical language and its implications for clinical diagnosis. We also introduced the concept of "'''encrypted machine language''' {{Tooltip|2=Let's consider a patient, Mr. Rossi, who presents with symptoms of facial pain and difficulty chewing. These symptoms can be interpreted in various ways depending on the specialist's expertise: a dentist might consider them indicative of temporomandibular disorder (TMD), while a neurologist could interpret them as neuropathic pain.'''Coding Symptoms:''' Symptoms:<math>S_1</math>: Facial pain and <math>S_2</math>: Difficulty chewing. Diagnoses: <math>D_1</math>: Temporomandibular Disorder (TMD) and <math>D_2</math>: Neuropathic Pain (nOP) {{(Tooltip|Mathematical Formalism) |We can formalize the process of decoding symptoms using a conditional probability function. Let’s define <math>P(D | S)</math> as the probability of a diagnosis <math>D</math> given the presence of symptoms <math>S</math>. <math> | ||
P(D | S) = \frac{P(S | D) \cdot P(D)}{P(S)} | P(D | S) = \frac{P(S | D) \cdot P(D)}{P(S)} | ||
</math> where: <math>P(D | S)</math> is the Probability of diagnosis <math>D</math> given symptoms <math>S</math>, <math>P(S|D)</math> is the Probability of observing symptoms <math>S</math> if diagnosis <math>D</math> is true, <math>P(D)</math>: is the Prior probability of diagnosis <math>D</math> and <math>P(S)</math> is the prior probability of observing symptoms <math>S</math>. | </math> where: <math>P(D | S)</math> is the Probability of diagnosis <math>D</math> given symptoms <math>S</math>, <math>P(S|D)</math> is the Probability of observing symptoms <math>S</math> if diagnosis <math>D</math> is true, <math>P(D)</math>: is the Prior probability of diagnosis <math>D</math> and <math>P(S)</math> is the prior probability of observing symptoms <math>S</math>. | ||