Difference between revisions of "Natural language processing and its future in medicine"

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Natural language processing and its future in medicine, Friedman, C; Hripcsak, G
 
Natural language processing and its future in medicine, Friedman, C; Hripcsak, G
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Submitted by (Tamer Etman)
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Revision as of 14:33, 1 March 2010

Natural language processing and its future in medicine

Physician regularly enter a huge amount of data for each patient into their electronic health record (EHR). Data entered can be either in a narrative text (free text) or it can be in a form of coded (structured) data entry. Structured data entry helps easy retrieval of information in the HER but free text fields are used in EHRs to facilitate rapid data entry by clinicians and it is some times more appropriate to enter information about patients that is not well described if entered as structured data. Yet it creates a limitation as it is very hard to access clinical information that is locked in these free text fields. It is important to retrieve data to be used for many purposes such as for automated decision support or for statistical purposes.

Natural Language processing (NLP)can offer a solution for this problem as it extracts words from free text and also present well defined relations among these words and include an appropriate modifier for the records. NLP encode data through using terminology from a well defined vocabulary, represent relations among the concepts. To understand textual language NLP involves many components as syntactic, semantic and domain knowledge components.

There are many systems using the NLP to achieve their goals. Some of these systems are: The SPRUS system, The MedLEE system and MENELAS system. Some of the important resources for NLP is the UMLS and SPECIALIST Lexicon, grammar and domain models. There is some work investigating the use of SNOMED and ICD-10 like UMLS in the NLP systems.

NLP can provide some important features in the future that can benefit EHR: These benefits could include providing a reasonable and accurate way to retrieve data from the electronic health records that could be used for billing in a more accurate way than assigning the ICD-9 manually to patients at the time of discharge and for web searching about clinical information and to find data for statistical and researches. It could be used with XML tags in a n easy way to retrieve data. Continues voice recognition systems can also be integrated with NLP in a way that could facilitate data entry and reduce time consumption for physicians. This will help in translating the textual report into a structure data encoded and sorted in real time along with original text in a clinical repository. NLP system would likely help producing standardized output forms suitable for the web through the use of XML,

Reference: Natural language processing and its future in medicine, Friedman, C; Hripcsak, G

Submitted by (Tamer Etman)