By using machine learning, we develop a drug recommendation system that is determined attempt to approach with care because of possible life-and-death implications. The initial process is recommending the appropriate drugs depend on patient’s medical history, genetic makeup or symptoms. Remember this, every time seek advice from healthcare experts before making any decisions.
Desk based research work are carried by us to frame research topics we assure that no one has attempted it previously. Only by machine learning experts’ topics will be recommended. We also attach along all reference papers so as to support our reach work.
The following points help us to create the drug recommendation project,
The drugs are suggested appropriately for a given patient profile, medical condition and symptoms.
The missing values are controlled properly. Using encoding techniques, we convert the categorical data into numerical format eg) one-hot encoding. The numerical data represented in an ordered manner.
Integrate them with the related features. For example, total number of current medications. The new features are extracted by us similar to the presence of specific genetic markers affecting drug metabolism.
The recommendation system is executed by us in an application or healthcare tool. Make confirm that the system offers explanations for their suggestions, as clarification is much important in healthcare.
Usually, update the model with the result of new patient and validate regularly of our suggestions with healthcare professionals.
Special note: This drug recommendation system never replaces the judgment of medical professionals. It acts as a tool to support decision making and offers recommendations for us or the professional’s ideas. Discussing or interacting with healthcare experts during the system design, development and deployment is very essential to attain our projects.
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Data mining, drug recommendation system, NLP, sentiment analysis
Our article provides medical suggestions to the customers based on ratings and health status. We employed various prototypes for disease forecasting. We examined the reviews by using Vader tool and sentiment analysis based on NLP. To suggest the medicines, we used probabilistic and weighted average techniques. At last, we conclude that, our system assist various healthcare applications and can be used in future researches.
Drug reviews, Supply chain management, Drug recommendation, Deep learning, Optimization algorithm
A drug suggestion framework is proposed for drug supply chain management in our study named Archimedes Optimization with Enhanced Deep Learning based Recommendation System (AOAEDL-RS). We preprocessed the data by using AOAEDL-RS method. Then we performed drug review categorization by utilizing CBLSTM-CNN approach. Finally, for accurate hyperparameter adjustment of CBLSTM-CNN, AOAEDL-RS method utilized AOA.
Electronic health record, Healthcare, Molecular knowledge
Our study suggested a drug recommendation system denoted DKFM with molecular knowledge-guided dual-level drug fusion technique. We can evaluate the effect of patient’s current status by utilizing DKFM that combines the patient’s diagnosis and procedure essential data. To acquire patient’s drug sets, we input the diagnosis and procedure data into drug functional group encoder. At last, we selected the optimal medicine for patient’s current health status.
Adverse drug reaction (ADR) classification, collaborative filtering, recommendation system, social media, support vector machine
A review of medical recommendation framework and ADR research issues are proposed in our paper. We investigated ADR classifications with various methods such as SVM, NB, LR, RF, and DNN. We discussed about the data preprocessing and feature extraction techniques. For language diversity and clinical characteristics, we examined the preprocessed data. We performed multilayer content and collaborative filtering for similarity indexing. For ADR identification and categorization, DNN offers greater end results.
Drug compound analysis, pattern prediction, subset clustering, feature selection
To forecast the relational drug suggestion, a relative drug compound analysis based on RSCF-DSNC is presented in our research. Then we preprocessed the data and marginalized the labeled data to calculate Relative Drug Intensive Weight. Drug molecules closeness is forecasted by RSCFS. We detected the drug molecule’s relational class by trained the chosen features into DSNC integrated with CNN. Therefore, our drug suggestion model provides better results.
Recurrent neural network (RNN), natural language processing (NLP)
A drug suggestion model is recommended in our study to forecast the appropriate medicine by considering reviews of patients. We examined the efficiency of the model in terms of various metrics. We collected relevant data from patient’s information by employing Natural Language Processing method. We evaluated the textual data by using an ML based method named RNN. As a consequence, our suggested model efficiently suggests drugs for patients.
Molecular graph encoder, Graph neural network
A molecular graph encoder for drug recommendation that is denoted by MGEDR is suggested in our study to record the patient’s health condition. To acquire the precise drug description, we encode the drug molecular graph and functional groups independently. We efficiently captured the molecule’s relevant features by creating the degree encoder and functional groups encoder. As a result, our recommended framework offers greater outcomes.
Neural Memory Network, Attention Mechanism, Coverage Mechanism, Adaptive Multi-Hop Reading
A selective coverage mechanism and adaptive memory neural network is suggested in our article and our drug suggestion system is integrated with reading. We carried out data filtering and attention weight tuning in iterative reading procedure by utilizing coverage mechanism and stored the temporal pattern encoding findings with patient’s health status by employing neural memory network. We retrieved important features and provide efficient drug suggestions.
Drug, Machine learning, Prescription
Our system aimed to offer efficient drug recommendation for the sick persons suffering from several diseases by employing ML techniques. We utilized various supervised ML methods including Support Vector Machine (SVM), Random Forest, Decision Tree, and K-nearest neighbors to provide medicine suggestions. We carried out a comparative analysis to find out the optimal method. Results show that, Random Forest achieved highest performance.
Recommender System, Smote, Bow, TF-IDF, Word2Vec
A main objective of our study is to develop a drug suggestion framework. We utilized several vectorization processes such as Bow, TF-IDF, Word2Vec, and Manual Feature Analysis to forecast the sentiment through patient’s reviews and assist to suggest an appropriate medicine for a particular disease through the employment of various categorization techniques. We conclude that, LinearSVC method with TF-IDF vectorization provides better end results.
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