Symptom Based Clinical Document Clustering by Matrix Factorization

Symptom Based Clinical Document Clustering By Matrix Factorization best software project report Clinical documents are rich free-text data sources containing valuable medication and symptom information, which have a great potential to improve health care. In this paper, we build an integrating system for extracting medication names and symptom names from clinical notes. Then we apply non negative matrix factorization (NMF) and multi-view NMF to cluster clinical notes into meaningful clusters based on sample-feature matrices. Our experimental results show that multi-view NMF is a preferable method for clinical document clustering. Moreover, we find that using extracted medication/symptom names to cluster clinical documents outperforms just using words. Student Free Project on Symptom Based Clinical. Click here to get complete Software projects lists.

 

Here we proposed a Doctor’s clinic management kind of system, where patients will visit the clinic and if the patient is new to clinic, then receptionist will feed his/her details into the system else if the patient is already registered then the receptionist will search for the patient’s name and add into the queue. When the patients turn appear, doctor will be able to see his/her details and also can check details about previous conditions if any. After doctor sees the patient, he will make entry of medicines patient needs to take. Receptionist will get those details, and there will be a section to add symptom. So at the end we will have list of symptom and medication provided for that. On this list we will apply NMF and retrieve our best possible symptom and its medication. Student Best Free project on Symptom Based Clinical Document Clustering.