Chronic cough is a common condition that presents to both primary and secondary care. Assessment and management are hampered by the absence of well-validated outcome measures. The present study comprises the validation of the Leicester Cough Monitor (LCM), an automated sound-based ambulatory cough monitor.Cough frequency was measured with the LCM and compared with coughs and other sounds counted manually over 2 h of a 6-h recording by two observers in nine patients with chronic cough in order to determine the sensitivity and specificity of the LCM. Automated cough frequency was also compared with manual counts from one observer in 15 patients with chronic cough and eight healthy subjects. All subjects underwent 6-h recordings. A subgroup consisting of six control and five patients with stable chronic cough underwent repeat automated measurements o3 months apart. A further 50 patients with chronic cough underwent 24-h automated cough monitoring.The LCM had a sensitivity and specificity of 91 and 99%, respectively, for detecting cough and a false-positive rate of 2.5 events?h The Leicester Cough Monitor is a valid and reliable tool that can be used to assess 24-h cough frequency in patients with cough. It should be a useful tool to assess patients with cough in clinical trials and longitudinal studies.

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/

Community health workers (CHWs) have gained increased visibility in the United States. We discuss how to strengthen the role of CHWs to enable them to become collaborative leaders in dramatically changing health care from “sickness care” systems to ones that provide comprehensive care for individuals and families and support community and tribal wellness. We recommend drawing on the full spectrum of CHWs’ roles so that they can make optimal contributions to health systems and the building of community capacity for health and wellness. We also urge that CHWs be integrated into ”community health teams” as part of “medical homes” and that evaluation frameworks be improved to better measure community wellness and systems change.

Cough is a common symptom of many respiratory diseases. The evaluation of its intensity and frequency of occurrence could provide valuable clinical information in the assessment of patients with chronic cough. In this paper we propose the use of hidden Markov models (HMMs) to automatically detect cough sounds from continuous ambulatory recordings. The recording system consists of a digital sound recorder and a microphone attached to the patient's chest. The recognition algorithm follows a keyword-spotting approach, with cough sounds representing the keywords. It was trained on 821 min selected from 10 ambulatory recordings, including 2473 manually labeled cough events, and tested on a database of nine recordings from separate patients with a total recording time of 3060 min and comprising 2155 cough events. The average detection rate was 82% at a false alarm rate of seven events/h, when considering only events above an energy threshold relative to each recording's average energy. These results suggest that HMMs can be applied to the detection of cough sounds from ambulatory patients. A postprocessing stage to perform a more detailed analysis on the detected events is under development, and could allow the rejection of some of the incorrectly detected events.

We have shown that there are marked differences in cough frequency between patients with chronic cough and healthy subjects, that these measurements are repeatable, and that they correlate with cough-specific health status.