Prognostic methods

Full numerical solution of the mass, energy, and momentum conservation equations (prognostic methods). These methods attempt to solve rigorously the full problem. Unfortunately, the full simulation of the system is an extremely difficult task and often results do not agree with available observations. Data assimilation techniques that force the model to approach observed values have been developed to overcome this problem. 

Medjaher, K., Zerhouni, N. (2013). Hybrid prognostic method applied to mechatronic systems. IntemationalJoumal of Advanced Manufacturing Technology. 69(1—4), 823-834. 

Saha, B., Goebel, K., Poll, S. Christophersen, J. 2009b. Prognostics Methods for Battery Health Monitoring U sing a Bayesian Framework. IEEE Transactions on Instrumentation and Measurement 58(2) 291-296. 

Tobon-Mejia, DA., K. Medjaher, N. Zerhouni, G. Tripot (2012). A data-driven failure prognostics method based on mixture of gaussians hidden markov models. Reliability, IEEE Transactions on 61(2), 491-503. 

E.g. feedbacks cause non-linearity in the system behaviour and cause that the behaviour is not predictable, and therefore, it is not possible to use the common prognostic methods for the identification of the possible states of a system. The consequence is an occasional occurrence of unexpected situations that threaten the humans, and it is difficult to get over them. 

The second task discussed is the validation of the regression models with the aid of the cross-validation (CV) procedures. The leave-one-out (LOO) as well as the leave-many-out CV methods are used to evaluate the prognostic possibilities of QSAR. In the case of noisy and/or heterogeneous data the LM method is shown to exceed sufficiently the LS one with respect to the suitability of the regression models built. The especially noticeable distinctions between the LS and LM methods are demonstrated with the use of the LOO CV criterion. 

Antman EM, Cohen M, Bernink PJ, et al. The TIMI risk score for unstable angina/non-ST-segment elevation MI a method for prognostication and therapeutic decision-making. JAMA 2000 284 835-842. 

The alchemical waters of Saint Giles Text and context. Queen Isabel s dietary and its contents. Methods of prognostication on onomatomantic principles. A collection of medicinal recipes and charms. 

Order of MINISTRY emergency of measures of Ukraine, Ministry of agricultural policy, Ministry of economy and Ministry of ecology and natural resources of Ukraine About claim of Method of prognostication of consequences of outpouring (extrass) of hazardous chemical substances at failures on industrial objects and transport)) from March, 27, 2001 No 73/82/64/122... 

Kolyada A. A., Kochin I. V., Sydorenko P. I. Problems of prognostication of possible sanitary losses from extraordinary situations with the use of the expertly-deliberative computer system of // The Organizational, medical-pharmaceutical and methodical aspects of medicine of catastrophes. Materials of allukrainian scientifically-practical conference. Temopil.-2005.-P. 30-31... 

Baldetorp B, Stal O, Ahrens O, Comelisse C, Corver W, Falkmer U, et al. Different calculation methods for flow cytometric S-phase fraction prognostic implications in breast cancer The Swedish Society of Cancer Study Group. Cytometry. 1998 Dec l 33(4) 385-93. 

Renal dysfunctions can be diagnosed by different methods, depending on the severity of the condition. Examination of urine, which is produced by the kidneys, provides an important indication of renal insufficiency. The urine output, color, odor, acidity, specific gravity, and constituents are important prognostic factors of kidney status. However, in critically ill patients and in acute renal failures induced by several diseases including multiple organ failures and diabetes, urine examination may be impractical and redundant. Such patients require reliable and simple methods to diagnose the onset of renal failure. 

Dynamic randomisation - minimisation When there are a large number of prognostic variables to account for, it may be practically difficult to implement a fully stratified randomisation scheme. The reason being that with a large number of factors there are even more individual stratum combinations and therefore there will be very few patients in many of the combinations. In such circumstances the method of dynamic allocation, or minimisation, has been recommended. 

The main advantages of minimisation are that it achieves good balance on prognostic factors and thereby increases efficiency. However, it has been criticised, particularly deterministic minimisation, for not guaranteeing the imderlying randomness assumed by the statistical methods used to analyse the data. A second disadvantage is that because minimisation is a d)mamic process it uses information on subjects already entered to allocate to future patients. Since these patients may be in other centres the process is usually carried out by a centralised system using the internet, fax or telephone. 

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