Clinical sources of information are markedly increasing in both volume and variety. A significant portion of the valuable data resides in the unstructured or semi-structured clinical text of documents stored in disparate repositories or embedded in HL7 messages. Clinical documents such as discharge summaries, prescriptions, lab reports, and free-form physician notes are filled with abbreviations, acronyms, misspellings, and ungrammatical phrases. However, synoptic reporting methods are restrictive for health care practitioners who wish to express critical and comprehensive patient information in electronic medical records. Furthermore, they have been superseded by systems that use natural language processing (NLP) to extract clinical concepts from free-form text. To address the growing need for efficient NLP solutions that can handle the volume and variety of clinical text, we have developed an optimized rules-based clinical concept extractor called TRACE (Tactical Rules-based AQL Clinical Extractor) using the Annotation Query Language (AQL). We present the experience we have gained applying text mining tools to this challenging domain, as well as a comparison of our solution to cTAKES (clinical Text Analysis and Knowledge Extraction System), an open-source clinical text miner, on a set of prescription documents. We also describe how efficient and scalable clinical text mining techniques will improve several of our company's offerings.
Underwater Wireless Sensor Network offers broad coverage of low data rate acoustic sensor networks, scalability and energy saving routing protocols. Moreover the major problem in underwater networks is energy consumption, which arises due to lower bandwidth and propagation delays. An underwater wireless sensor network frequently employs acoustic channel communications since radio signals not worked in deep water. The transmission of data packets and energy-efficient routing are constraints for the unique characteristics of underwater. The challenging issue is an efficient routing protocol for UWSNs. Routing protocols take advantage of localization sensor nodes. Many routing protocols have been proposed for sensing nodes through a localization process. Here we proposed a Novel vector-based forwarding and efficient depth-based routing protocol. The proposed novel vector-based forwarding provides robust, scalable, and energy-efficient routing. It easily transfers nodes from source to destination. It adopts the localized and distributed alternation that allows nodes to weigh transferring packets and decreases energy consumption and provides better optimal paths. Efficient depth-based routing is a stochastic model that will succeed in a high transmission loss of the acoustic channel. The simulation was used to compare the energy consumption, network lifetime in the form of depth-based routing, delivery ratio, and vector-based forwarding to prove the optimal route finding paths and data transmission propagation delay.
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