We present an approach to automatic detection of Alzheimer's type dementia based on characteristics of spontaneous spoken language dialogue consisting of interviews recorded in natural settings. The proposed method employs additive logistic regression (a machine learning boosting method) on content-free features extracted from dialogical interaction to build a predictive model. The model training data consisted of 21 dialogues between patients with Alzheimer's and interviewers, and 17 dialogues between patients with other health conditions and interviewers. Features analysed included speech rate, turn-taking patterns and other speech parameters. Despite relying solely on content-free features, our method obtains overall accuracy of 86.5 , a result comparable to those of state-of-the-art methods that employ more complex lexical, syntactic and semantic features. While further investigation is needed, the fact that we were able to obtain promising results using only features that can be easily extracted from spontaneous dialogues suggests the possibility of designing non-invasive and low-cost mental health monitoring tools for use at scale.
An analysis of the CCC corpus by @cite_20 used similar linguistic features. Unlike the work presented in this paper, Guinn's analysis was focused on the differences between interviewers and subjects in the subset of patients with AD. They achieved a combined accuracy of 75-79.5 large discrepancy between AD (38-42 accuracy.
@cite_20
2146089916 1489181569 2520951703 2043335066
We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at . The cTAKES builds on existing open-source technologies—the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text. Researchers in both machine translation (e.g., 1990) and bilingual lexicography (e.g., Klavans and Tzoukermann 1990) have recently become interested in studying bilingual corpora, bodies of text such as the Canadian Hansards (parliamentary proceedings), which are available in multiple languages (such as French and English). One useful step is to align the sentences, that is, to identify correspondences between sentences in one language and sentences in the other language.This paper will describe a method and a program (align) for aligning sentences based on a simple statistical model of character lengths. The program uses the fact that longer sentences in one language tend to be translated into longer sentences in the other language, and that shorter sentences tend to be translated into shorter sentences. A probabilistic score is assigned to each proposed correspondence of sentences, based on the scaled difference of lengths of the two sentences (in characters) and the variance of this difference. This probabilistic score is used in a dynamic programming framework to find the maximum likelihood alignment of sentences.It is remarkable that such a simple approach works as well as it does. An evaluation was performed based on a trilingual corpus of economic reports issued by the Union Bank of Switzerland (UBS) in English, French, and German. The method correctly aligned all but 4 of the sentences. Moreover, it is possible to extract a large subcorpus that has a much smaller error rate. By selecting the best-scoring 80 of the alignments, the error rate is reduced from 4 to 0.7 . There were more errors on the English-French subcorpus than on the English-German subcorpus, showing that error rates will depend on the corpus considered; however, both were small enough to hope that the method will be useful for many language pairs.To further research on bilingual corpora, a much larger sample of Canadian Hansards (approximately 90 million words, half in English and and half in French) has been aligned with the align program and will be available through the Data Collection Initiative of the Association for Computational Linguistics (ACL DCI). In addition, in order to facilitate replication of the align program, an appendix is provided with detailed c-code of the more difficult core of the align program. We use four speech databases with realistic, non-prompted emotions, and a large state-of-the-art acoustic feature vector, for cross-corpus classifications, in turn employing three databases for training and the fourth for testing. Categorical and continuous (dimensional) annotation is mapped onto a representation of valence with the three classes positive, neutral, and negative. This cross-corpus classification is compared with within corpus classifications. We interpret performance and most important features. This article reports on a corpus annotation project that has produced a freely available resource for research on handling negation and uncertainty in biomedical texts (we call this corpus the BioScope corpus). The corpus consists of three parts, namely medical free texts, biological full papers and biological scientific abstracts. The dataset contains annotations at the token level for negative and speculative keywords and at the sentence level for their linguistic scope. The annotation process was carried out by two independent linguist annotators and a chief annotator -- also responsible for setting up the annotation guidelines -- who resolved cases where the annotators disagreed. We will report our statistics on corpus size, ambiguity levels and the consistency of annotations.