Basically, prognostics task aims at predicting the degradation of engineering assets.
However, there is neither a clear definition of the data quality nor evaluation methods to quantify if acquired data is suitable for these prognostic modeling tasks such as failures detection, diagnosis and prediction.
The main aim of these tools is to improve performance of cardiologists on prognostic and diagnostic tasks, i.e., reducing both the number of missed diagnoses or prognoses and the time taken to reach such decisions.
In general terms, the performance of these classifiers in prognostic or diagnostic tasks is relatively high (80% to 95% sensitivity in the best cases); however, they have been used for the recognition of several patterns in specific cardiac diseases (e.g., CHF, paroxysmal AF, MI, cardiac arrhythmias, amongst others) rather than for the prognosis of cardiovascular risk.
Six articles conducted a postoperative examination between 3 months and 12 months [ 46, 58, 59, 61, 71, 72], and another two designed a prognostic study in which tasks were revealed associated with postoperative relapse in cognition [ 40, 49].
It is advisable to support the complex task of prognostic assessment and treatment decisions with several clinical and laboratory parameters that may mirror different physiological aspects.
It is advisable to base the difficult task of prognostic assessment and treatment decisions on several and not only one parameter, each mirroring different pathophysiological aspects.
In our opinion, when one is presented with the difficult task of prognostic assessment and making treatment decisions, it is advisable to rely on several parameters that reflect different physiological aspects.
Similarity search based on patient mutation profiles can solve various translational bioinformatics tasks, including prognostics and treatment efficacy predictions for better clinical decision making through large volume of data.
