| Título: | SIGNIFYING SENTENCE FRAMEWORK IN MACHINE LEARNING METHOD FOR INFORMATION MINING |
| Autores: |
Kumar, V.Praveen; M.Tech Student, Dept of CSE, RVR Institute of Engineering & Technology, Sheriguda, Ibrahimpatnam, Hyderabad, RR Dist (A.P) Gangadhar, V.V.R.L.S.; Professor, Dept of CSE, RVR Institute of Engineering & Technology, Sheriguda, Ibrahimpatnam, Hyderabad, RR Dist (A.P) |
| Fecha: | 2013-08-07 |
| Publicador: | International journal of computer and electronics research |
| Fuente: |
 |
| Tipo: | Peer-reviewed Article |
| Tema: | Electronic Health Records; Machine Learning; Textual representation; Short medical text |
| Descripción: | Electronic Health Records are becoming the criterion in the healthcare province. The potential benefits of having an Electronic Health Records system are: Health information recording and clinical data repositories, Medication supervision, Decision hold up, Obtain treatments that are personalized to definite health needs. Usage of Natural Language Processing and Machine Learning techniques build a tool, capable to recognize and distribute textual information related to diseases and treatments. An extensive study of various Machine Learning algorithms and textual representations for classifying short medical texts and recognizing semantic relations among two medical entities diseases and treatments. There are at least two challenges that can be come across although working with Machine Learning method. One is to discover the most appropriate model for prediction. The Machine Learning field offers a collection of predictive models that can be used and deployed. The job of finding the appropriate one relies greatly on empirical studies and knowledge proficiency. The second one is to find a good data depiction and to do aspect engineering because features powerfully manipulate the performance of the models. |
| Idioma: | Inglés |
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