Substantially Similar Technologies

A Neural Net That Diagnoses Epilepsy

The team developed the system by training a neural network to recognize the characteristic patterns in interictal data that indicate that the patient is epileptic. And the researchers claim an accuracy rate of 94 percent–about the same as experienced human operators, who usually have to strip various kinds of noise and artifacts out of the data before they can do their job.

A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer

A Bayesian framework is introduced to carry out Automatic Relevance Determination (ARD) in feedforward neural networks to model censored data. A procedure to identify and interpret the prognostic group allocation is also described.

These methodologies are applied to 1616 records routinely collected at Christie Hospital, in a monthly cohort study with 5-year follow-up. Two cohort studies are presented, for low- and high-risk patients allocated by standard clinical staging.

The results of contrasting the Partial Logistic Artificial Neural Network (PLANN)–ARD model with the proportional hazards model are that the two are consistent, but the neural network may be more specific in the allocation of patients into prognostic groups. With automatic model selection, the regularised neural network is more conservative than the default stepwise forward selection procedure implemented by SPSS with the Akaike Information Criterion.

A transdimensional Bayesian model for pattern recognition in DNA sequences

Identification of transcription factor binding sites (TFBSs) is essential to elucidate gene regulatory networks. This article is focused on the recognitionof overpresented short patterns, called “motifs”, that may correspond to regulatory binding sites in the DNA sequences upstream of genes. An integrated Bayesian model is proposed to incorporate all unknown characteristics in motif discovery, including the number of motifs, motif widths, motif compositions, the number of motif sites, and locations of motif sites. Reversible jump Markov chain Monte Carlo is used to obtain posterior inference in the transdimensional parameter space.

Application of Bayesian Networks in Emergency Medicine

Description of a research study in Slovenia proving the value of the technology in the care of cardiology patients.