Introduction We present a methodology to automatically evaluate the performance of patients during picture description tasks. Methods Transcriptions and audio recordings of the Cookie Theft picture description task were used. With 25 healthy elderly control (HC) samples and an information coverage measure, we automatically generated a population-specific referent. We then assessed 517 transcriptions (257 Alzheimer's disease [AD], 217 HC, and 43 mild cognitively impaired samples) according to their informativeness and pertinence against this referent. We extracted linguistic and phonetic metrics which previous literature correlated to early-stage AD. We trained two learners to distinguish HCs from cognitively impaired individuals. Results Our measures significantly ( P < .001) correlated with the severity of the cognitive impairment and the Mini–Mental State Examination score. The classification sensitivity was 81% (area under the curve of receiver operating characteristics = 0.79) and 85% (area under the curve of receiver operating characteristics = 0.76) between HCs and AD and between HCs and AD and mild cognitively impaired, respectively. Discussion An automated assessment of a picture description task could assist clinicians in the detection of early signs of cognitive impairment and AD.

BackgroundClinical notes such as discharge summaries have a semi- or unstructured format. These documents contain information about diseases, treatments, drugs, etc. Extracting meaningful information from them becomes challenging due to their narrative format. In this context, we aimed to compare the automatic extraction capacity of medical entities using two tools: MetaMap and cTAKES.MethodsWe worked with i2b2 (Informatics for Integrating Biology to the Bedside) Obesity Challenge data. Two experiments were constructed. In the first one, only one UMLS concept related with the diseases annotated was extracted. In the second, some UMLS concepts were aggregated.ResultsResults were evaluated with manually annotated medical entities. With the aggregation process the result shows a better improvement. MetaMap had an average of 0.88 in recall, 0.89 in precision, and 0.88 in F-score. With cTAKES, the average of recall, precision and F-score were 0.91, 0.89, and 0.89, respectively.ConclusionsThe aggregation of concepts (with similar and different semantic types) was shown to be a good strategy for improving the extraction of medical entities, and automatic aggregation could be considered in future works.

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In many domains, important events are not represented as the common scenario, but as deviations from the rule. The importance and impact associated with these particular, outnumbered, deviant, and sometimes even previously unseen events is directly related to the application domain (e.g., breast cancer detection, satellite image classification, etc.). The detection of these rare events or outliers has recently been gaining popularity as evidenced by the wide variety of algorithms currently available. These algorithms are based on different assumptions about what constitutes an outlier, a characteristic pointing toward their integration in an ensemble to improve their individual detection rate. However, there are two factors that limit the use of current ensemble outlier detection approaches: first, in most cases, outliers are not detectable in full dimensionality, but instead are located in specific subspaces of data; and second, despite the expected improvement on detection rate achieved using an ensemble of detectors, the computational efficiency of the ensemble will increase linearly as the number of components increases. In this article, we propose an ensemble approach that identifies outliers based on different subsets of features and subsamples of data, providing more robust results while improving the computational efficiency of similar ensemble outlier detection approaches.