We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic patients over time.
The applications of Hidden Markov processes include automatic character and speech recognition, information theory, statistics and bioinformatics, see [4, 13].
It has been widely and successfully applied to numerous fields, including signal analysis and pattern recognition (see [12]), statistics, geophysical fluid dynamics or meteorology (see [13]).
The query protein sequences are first divided into domains either by domain boundary recognition or Bayesian statistics.
Thus basing object recognition on image statistics, and categorization based on these, is insufficient for transform-invariant object recognition.
Model extrapolations, disturbances or sensor faults are first detected through a combined statistic (that considers the calibration region); then, a diagnosis is made by combining statistics pattern recognition, contribution analysis, and disturbance isolation based on historical fault patterns.
Application of methods for geochemical anomaly recognition based on classical statistics can, thus, be misguided and potentially result in spurious anomalies.
This is an important tool in the social sciences, biology, statistics, pattern recognition and, now, marketing.
