Statistical methods to support difficult diagnoses
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Background: Far too often, one meets patients who went for years or even decades from doctor to doctor, without getting a valid diagnosis. This brings pain to millions of patients and their families, not to speak of the enormous costs. Often patients do not know well enough which factors (or combinations thereof) trigger their problems.
Results: If conventional methods fail, we propose the use of statistics and algebra to give doctors much more precise inputs from patients. We propose statistical regression for independent triggering factors for medical problems, and ?balanced incomplete block designs? for non-independent factors. These methods might change a useless statement like ?I feel very tired after meals? to a much more valuable ?After a meal with many carbohydrates, but few vegetables, a moderate physical activity will usually force me into a wheel-chair?. In order to show that these methods do work, we briefly describe a real case in which these methods helped to solve a 60 year old problem in a patient, and give some more examples where these methods might be very useful.
Methods: In this paper, we present a way of getting medical diagnoses when the methods in medicine are insufficient, too time consuming, or very expensive. By asking the patient to conduct tests (often at home) according to a very well-prepared schedule, statistics can use regression analysis to identify the factors (or combinations thereof) which trigger or worsen the patient?s problems. This (very inexpensive) analysis can often give the patient?s doctor(s) a very good and precise input for their diagnosis.
Conclusions: While regression is used in clinical medicine, it seems to be widely unknown among diagnosing doctors. In finding the reason(s) of rare diseases, doctors face very tough problems. So they deserve to know all tools which could offer some help. This can save the health systems much money, and the patients also a lot of pain.