3,987 research outputs found

    Speech Recognition Technology: Improving Speed and Accuracy of Emergency Medical Services Documentation to Protect Patients

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    Because hospital errors, such as mistakes in documentation, cause one in six deaths each year in the United States, the accuracy of health records in the emergency medical services (EMS) must be improved. One possible solution is to incorporate speech recognition (SR) software into current tools used by EMS first responders. The purpose of this research was to determine if SR software could increase the efficiency and accuracy of EMS documentation to improve the safety of patients of EMS. An initial review of the literature on the performance of current SR software demonstrated that this software was not 99% accurate, and therefore, errors in the medical documentation produced by the software could harm patients. The literature review also identified weaknesses of SR software that could be overcome so that the software would be accurate enough for use in EMS settings. These weaknesses included the inability to differentiate between similar phrases and the inability to filter out background noise. To find a solution, an analysis of natural language processing algorithms showed that the bag-of-words post processing algorithm has the ability to differentiate between similar phrases. This algorithm is best suited for SR applications because it is simple yet effective compared to machine learning algorithms that required a large amount of training data. The findings suggested that if these weaknesses of current SR software are solved, then the software would potentially increase the efficiency and accuracy of EMS documentation. Further studies should integrate the bag-of-words post processing method into SR software and field test its accuracy in EMS settings

    Thesaurus-based disambiguation of gene symbols

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    BACKGROUND: Massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. However, disambiguation of gene symbols is a major bottleneck. RESULTS: We developed a simple thesaurus-based disambiguation algorithm that can operate with very little training data. The thesaurus comprises the information from five human genetic databases and MeSH. The extent of the homonym problem for human gene symbols is shown to be substantial (33% of the genes in our combined thesaurus had one or more ambiguous symbols), not only because one symbol can refer to multiple genes, but also because a gene symbol can have many non-gene meanings. A test set of 52,529 Medline abstracts, containing 690 ambiguous human gene symbols taken from OMIM, was automatically generated. Overall accuracy of the disambiguation algorithm was up to 92.7% on the test set. CONCLUSION: The ambiguity of human gene symbols is substantial, not only because one symbol may denote multiple genes but particularly because many symbols have other, non-gene meanings. The proposed disambiguation approach resolves most ambiguities in our test set with high accuracy, including the important gene/not a gene decisions. The algorithm is fast and scalable, enabling gene-symbol disambiguation in massive text mining applications
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