A Machine Learning Model for Clinical Decision Support for Drug Recommendation (International Journal of Innovative Research in Computer Science & Technology, 2022)
Modern machine learning techniques plays  a very crucial role in dealing with very complex unstructured  data that is available in the medical domain. The wide range  of applications in this area is capable of changing the  available data to valuable information that could be used for  recommendation of appropriate treatment and drugs by  analysing the symptoms and other information regarding the  patient . In this work, the data available in the form of plain  text in the form of electronic health records were used to give  appropriate recommendation regarding the medication that  could be given to the patient. Thus it acts as a clinical  decision support system that can assist the doctor in taking  suitable decisions regarding the treatment plan of the patient.  The model used techniques from Natural Language  processing and Deep Learning to process that raw data and  build a learning model for recommendation. The model was  able to give an accuracy of 77 percent with raw text as input.
Keywords: Clinical Decision Support System; Electronic Health Record(EHR); Long Short Term Memory(LSTM); Machine Learning; Natural Language Processing; Recurrent Neural Networks (RNN)