Extracting clinical terms from radiology reports with deep learning.

Extracting clinical terms from free-text format radiology reports is a first important step toward their secondary use. However, there is no general consensus on the kind of terms to be extracted. In this paper, we propose an information model comprising three types of clinical entities: observations, clinical findings, and modifiers. Furthermore, to determine its applicability for in-house radiology reports, we extracted clinical terms with state-of-the-art deep learning models and compared the results. We trained and evaluated models using 540 in-house chest computed tomography (CT) reports annotated by multiple medical experts. Two deep learning models were compared, and the effect of pre-training was explored. To investigate the generalizability of the model, we evaluated the use of other institutional chest CT reports. The micro F1-score of our best performance model using in-house and external datasets were 95.36% and 94.62%, respectively. Our results indicated that entities defined in our information model were suitable for extracting clinical terms from radiology reports, and the model was sufficiently generalizable to be used with dataset from other institutions.

Information transmission by stimulus-dependent modulation of noise correlation.

Cortical neurons that are near one another show correlated response variability (noise correlation), which can contribute to synergistic information transmission. In this study, we investigated the relationship between the level of external stimulation and noise correlation and its effect on population coding. Six levels of electrical stimulation were delivered to a rat's hind paw and responses of several neighboring neurons were simultaneously recorded in the primary somatosensory cortex. As the intensity of stimulation increased, noise correlation decreased down to near zero and then increased again to a relatively small value. The degree of synergistic information transmission depended on the amount by which noise correlation was modulated. Our results show that noise correlation among somatosensory cortical neurons is dynamically modulated by external stimulation, which allows transmission of additional information.

A constrained EM algorithm for principal component analysis.

We propose a constrained EM algorithm for principal component analysis (PCA) using a coupled probability model derived from single-standard factor analysis models with isotropic noise structure. The single probabilistic PCA, especially for the case where there is no noise, can find only a vector set that is a linear superposition of principal components and requires postprocessing, such as diagonalization of symmetric matrices. By contrast, the proposed algorithm finds the actual principal components, which are sorted in descending order of eigenvalue size and require no additional calculation or postprocessing. The method is easily applied to kernel PCA. It is also shown that the new EM algorithm is derived from a generalized least-squares formulation.