BIOSOFT HELP FILES

INTERPRETATION OF THE OUTPUT

Explanatory probability = number of symptoms common to both the patient and the disease / number of symptoms elicited from the patient
Contributory probability = number of symptoms common to both the patient and the disease / number of symptoms in the disease set
Rule out probability = number of symptoms not seen in the disease symptom collection / number of symptoms in the disease set
Alternate disease probability = number of symptoms not seen in the patients' symptom collection / number of symptoms in the patient's symptom collection

Explanatory probability is directly proportional to the probability of having a given disease
Contributory probability is directly proportional to the degree of confidence in making the given diagnosis with a given explanatory probability
Rule out probability is directly proportional to the degree of confidence in ruling out a given diagnosis.
Alternate disease probability is directly proportional to the degree of confidence in having another disease as an explanation for the patients diagnosis.

CAMEOS - P OUTPUT DETAILED
Major Sections of the Output include :
Symptoms Reported - Symptoms you have collected from the patient during history taking
Illness Data Reported - duration, severity, disability, impairment and handicap associated with each illness experience
Calculating the Diagnostic Statistics for each Diagnosis in the Differential Diagnosis - For each disease considered by the inference engine the following information is given : number of the symptoms complained of, number of symptoms not complained of, input symptoms unexplained by the diagnosis, explanatory probability, contributory probability, rule out probability, comorbidity probability with the respective probabilities
Differential Diagnosis by Explanatory Probability
Differential Diagnosis by Contributory Probability
Rank Ordering of the Diagnoses by Descending Order of the Explanatory Probability
Rank Ordering of the Diagnoses by Descending Order of the Contributory Probability
Final Diagnosis and the Required Treatment
See the explanation for details and interpretation of the above probabilities

INTERPRETATION OF THE CAMEOS - P OUTPUT
Explanatory probability = number of symptoms common to both the input and the disease / number of symptoms in the input set
Contributory probability = number of symptoms common to both the input and the disease / number of symptoms in the disease set
Rule out probability = number of symptoms not seen in the disease symptom collection / number of symptoms in the disease set
Alternate disease probability = number of symptoms not seen in the disease symptom collection / number of symptoms in the input set
Explanatory probability is directly proportional to the probability of having a given disease. Contributory probability is directly proportional to the degree of confidence in making the given diagnosis with a given explanatory probability. A disease may have a very high explanatory probability but with a very low contributory probability. This may happen when the user input set has few symptoms all of which are seen in the disease array but diagnosis set has so many other indicants not endorsed by the user. The converse too may happen. A given disease may have a low explanatory power with a very high contributory probability. The formula given above clearly shows that this can happen when there are many unrelated symptoms in the input set but all the indicants required to make a diagnosis of the given disease are seen. So then obviously there are many unexplained indicants by the selected diagnosis.
Rule out probability is directly proportional to the degree of confidence in ruling out a given diagnosis. This is because the number of indicants in the complement set is high in comparison to the number of indicants in the disease indicant set. To put it another way as the complement set contains elements not seen in the disease set higher the number of these elements the chances are the disease needs to be ruled out from the diagnostic considerations .
Alternate disease probability is directly proportional to the degree of confidence in having another disease as an explanation for the patients diagnosis. For instance when the alternate disease probability is high the number of input symptoms not seen in the elements of the disease set is high. So then one has to look for other possibilities. But this does not mean the disease whose alternate disease probability is high is the disease to be considered. But any other disease might explain the patients problem. It cannot point to the alternate disease. Thus it cannot be called a rule in probability.
So these statistics gives the mathematical basis for the explanation module of the inference engine.

NOTES FOR FURTHER INFORMATION TO THE USERS

1. Give the patient population for which CAMEOS-P can be used and CAMEOS-C and CAMEOS-S can be used respectively :

CAMEOS - P - to be used in primary care adult consultations ONLY
CAMEOS - C - to be used in primary care pediatric consultations ONLY
CAMEOS - S - to be used in MEDICAL SELF DIAGNOSIS FOR MEDICAL SELF CARE only

2. Users of these programs are guaranteed to :

1. be EVIDENCE BASED
2. be at the cutting edge of the peer reviewed literature
3. be bound by the numerous clinical practice guidelines in Medicine
4. be making diagnosis at a higher level of probability than non-users