A framework of hybrid recommender system for personalized clinical prescription

© 2015 IEEE. General practitioners are faced with a great challenge of clinical prescription owing to the increase of new drugs and their complex functions to different diseases. A personalized recommender system can help practitioners discover mass of medical knowledge hidden in history medical records to deal with information overload problem in prescription. To support practitioner's decision making in prescription, this paper proposes a framework of a hybrid recommender system which integrates artificial neural network and case-based reasoning. Three issues are considered in this system framework: (1) to define a patient's need by giving his/her symptom, (2) to mine features from free text in medical records and (3) to analyze temporal efficiency of drugs. The proposed recommender system is expected to help general practitioners to improve their efficiency and reduce risks of making errors in daily clinical consultation with patients

Defining architectures for recommended systems for medical treatment. A Systematic Literature Review

This paper presents a Systematic Literature Review(SLR) related to recommender system for medical treatment, aswell as analyze main elements that may provide flexible, accurate,and comprehensive recommendations. To do so, a SLR researchmethodology obey. As a result, 12 intelligent recommendersystems related to prescribing medication were classed dependingto specific criteria. We assessed and analyze these medicinerecommender systems and enumerate the challenges. After studyingselected papers, our study concentrated on two researchquestions concerning the availability of medicine recommendersystems for physicians and the features these systems should have.Further research is encouraged in order to build an intelligentrecommender system based on the features analyzed in this work

Towards Integration of Artificial Intelligence into Medical Devices as a Real-Time Recommender System for Personalised Healthcare:State-of-the-Art and Future Prospects

In the era of big data, artificial intelligence (AI) algorithms have the potential to revolutionize healthcare by improving patient outcomes and reducing healthcare costs. AI algorithms have frequently been used in health care for predictive modelling, image analysis and drug discovery. Moreover, as a recommender system, these algorithms have shown promising impacts on personalized healthcare provision. A recommender system learns the behaviour of the user and predicts their current preferences (recommends) based on their previous preferences. Implementing AI as a recommender system improves this prediction accuracy and solves cold start and data sparsity problems. However, most of the methods and algorithms are tested in a simulated setting which cannot recapitulate the influencing factors of the real world. This review article systematically reviews prevailing methodologies in recommender systems and discusses the AI algorithms as recommender systems specifically in the field of healthcare. It also provides discussion around the most cutting-edge academic and practical contributions present in the literature, identifies performance evaluation matrices, challenges in the implementation of AI as a recommender system, and acceptance of AI-based recommender systems by clinicians. The findings of this article direct researchers and professionals to comprehend currently developed recommender systems and the future of medical devices integrated with real-time recommender systems for personalized healthcare

RECOMED: A Comprehensive Pharmaceutical Recommendation System

A comprehensive pharmaceutical recommendation system was designed based on the patients and drugs features extracted from Drugs.com and Druglib.com. First, data from these databases were combined, and a dataset of patients and drug information was built. Secondly, the patients and drugs were clustered, and then the recommendation was performed using different ratings provided by patients, and importantly by the knowledge obtained from patients and drug specifications, and considering drug interactions. To the best of our knowledge, we are the first group to consider patients conditions and history in the proposed approach for selecting a specific medicine appropriate for that particular user. Our approach applies artificial intelligence (AI) models for the implementation. Sentiment analysis using natural language processing approaches is employed in pre-processing along with neural network-based methods and recommender system algorithms for modeling the system. In our work, patients conditions and drugs features are used for making two models based on matrix factorization. Then we used drug interaction to filter drugs with severe or mild interactions with other drugs. We developed a deep learning model for recommending drugs by using data from 2304 patients as a training set, and then we used data from 660 patients as our validation set. After that, we used knowledge from critical information about drugs and combined the outcome of the model into a knowledge-based system with the rules obtained from constraints on taking medicine.Comment: 39 pages, 14 figures, 13 table

Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health care

Internet of Things (IoT) and Data science have revolutionized the entire technological landscape across the globe. Because of it, the health care ecosystems are adopting the cutting‐edge technologies to provide assistive and personalized care to the patients. But, this vision is incomplete without the adoption of data‐focused mechanisms (like machine learning, big data analytics) that can act as enablers to provide early detection and treatment of patients even without admission in the hospitals. Recently, there has been an increasing trend of providing assistive recommendation and timely alerts regarding the severity of the disease to the patients. Even, remote monitoring of the present day health situation of the patient is possible these days though the analysis of the data generated using IoT devices by doctors. Motivated from these facts, we design a health care recommendation system that provides a multilevel decision‐making related to the risk and severity of the patient diseases. The proposed systems use an all‐disease classification mechanism based on convolutional neural networks to segregate different diseases on the basis of the vital parameters of a patient. After classification, a fuzzy inference system is used to compute the risk levels for the patients. In the last step, based on the information provided by the risk analysis, the patients are provided with the potential recommendation about the severity staging of the associated diseases for timely and suitable treatment. The proposed work has been evaluated using different datasets related to the diseases and the outcomes seem to be promising

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life