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Artykuły w czasopismach na temat „Drug recommendation system”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Dr. Meenakshi A, Aakash Kumar B, Raja Aswin T, and Prakash A. "Affordable Medicine Recommendation System." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 527–31. https://doi.org/10.47392/irjaeh.2025.0074.

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The high cost of medications is a major hurdle for many people, especially those in low-income communities, who often can't afford the treatments they need. Although generic medications offer a cheaper option, it’s not always easy for patients to find generics that are both affordable and as effective as their prescribed drugs. This paper introduces the Affordable Medicine Recommendation System, a tool designed to help people find cost-effective alternatives by comparing the chemical compositions of their prescribed medications with available generics. The system uses Cosine Similarity, a meth

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Kale, Dr Arati, Anup Lohar, Umar Shaikh, and Shrikant Gophane. "Drug Recommendation System Based on Symptoms." International Journal of Advanced Pharmaceutical Sciences and Research 5, no. 2 (2025): 1–4. https://doi.org/10.54105/ijapsr.a4060.05020225.

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The integration of digital health technologies has transformed patient care by enabling the development of intelligent systems that assist in medical decision-making. This paper introduces a Drug Recommendation System (DRS) designed to analyze user-inputted symptoms and recommend appropriate medications. Utilizing advanced Natural Language Processing (NLP) techniques, the system preprocesses and classifies textual symptom data, facilitating accurate drug suggestions. The implementation of machine learning algorithms, particularly the Multinomial Naive Bayes classifier, allows for the effective

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Souda Lakshmi Priya, Gunda Durgesh, Vadthyavath Naveen, and Mrs.J.Santoshi. "Drug Recommendation System In Medical Emergencies." International Journal of Information Technology and Computer Engineering 13, no. 2 (2025): 1296–300. https://doi.org/10.62647/ijitce2025v13i2pp1296-1300.

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In recent years, the convergence of artificial intelligence (AI) and healthcare has unlocked transformative possibilities for personalized patient care. This study presents a Drug Recommendation System that employs a transformer-based natural language processing (NLP) model to deliver medication suggestions based on a user’s reported symptoms, medical history, and profile data.The system features a robust Python backend powered by a fine-tuned ClinicalBERT transformer, coupled with a Next.js and TailwindCSS frontend that provides a modern, responsive, and engaging user experience. It processes

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Anup, Lohar. "Drug Recommendation System Based on Symptoms." International Journal of Advanced Pharmaceutical Sciences and Research (IJAPSR) 5, no. 2 (2025): 1–4. https://doi.org/10.54105/ijapsr.A4060.05020225.

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<strong>Abstract: </strong>The integration of digital health technologies has transformed patient care by enabling the development of intelligent systems that assist in medical decision-making. This paper introduces a Drug Recommendation System (DRS) designed to analyze user-inputted symptoms and recommend appropriate medications. Utilizing advanced Natural Language Processing (NLP) techniques, the system preprocesses and classifies textual symptom data, facilitating accurate drug suggestions. The implementation of machine learning algorithms, particularly the Multinomial Naive Bayes classifie

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Gousiya Begum, S., and Pokkuluri Kiran Sree. "Drug recommendation using recurrent neural networksaugmented with cellular automata." BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning 2, no. 1 (2023): 19–25. http://dx.doi.org/10.54646/bijiam.2023.13.

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Drug recommendation systems are systems that have the capability to recommend drugs. On a daily basis, a hugeamount of data is being generated by the patients. All this valuable data can be properly utilized to create a reliabledrug recommendation system. In this paper, we recommend a system for drug recommendations. The main scopeof our system is to predict the correct medication based on reviews and ratings. Our proposed system uses naturallanguage processing techniques (NLP), recurrent neural networks (RNN), and cellular automata (CA). We alsoconsidered various metrics like precision, recal

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Gawande, Sunad D., Likhit Y. Shende, Vedant R. Dhajekar, Shrushti A. Mankar, Krutika D. Wankhade, and Prof. Jaicky R. Sancheti. "MEDINTEL: Disease Prediction and Drug Recommendation System Using ML." International Journal of Ingenious Research, Invention and Development (IJIRID) 4, no. 2 (2025): 256–62. https://doi.org/10.5281/zenodo.15206426.

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<em>MedIntel: Disease Prediction & Drugs Recommendation System For Health Care Using Machine Learning. MedIntel is an advanced healthcare solution designed to enhance disease prediction and drug recommendations using machine learning. Its primary objectives include improving early disease detection, providing personalized treatment recommendations, and increasing healthcare accessibility. By leveraging cutting-edge AI technologies, MedIntel aims to enhance diagnostic precision, minimize treatment delays, and support medical professionals in making informed, data-driven decisions.  The

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Hussain, Marya, Chelsea Wong, Eddy Taguedong, et al. "Impact of Oncology Drug Review Times on Public Funding Recommendations." Current Oncology 30, no. 8 (2023): 7706–12. http://dx.doi.org/10.3390/curroncol30080558.

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New oncology drugs undergo detailed review prior to public funding in a single-payer healthcare system. The aim of this study was to assess how cancer drug review times impact funding recommendations. Drugs reviewed by the pan-Canadian Oncology Drug Review (pCODR) between the years 2012 and 2020 were included. Data were collected including Health Canada approval dates, initial and final funding recommendations, treatment intent, drug class, clinical indications, and incremental cost-effectiveness ratios (ICER). Univariable and multivariable analyses were used to determine the association betwe

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Balakrishnan, Sarojini, and Sobya D. "Leveraging Text Mining for Drug Recommendation System." International Journal of Engineering Trends and Technology 72, no. 10 (2024): 140–48. http://dx.doi.org/10.14445/22315381/ijett-v72i10p114.

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Sree, P. Kiran. "Drug Recommendations Using a Reviews and Sentiment Analysis by Recurrent Neural Network." Journal of Quality in Health Care & Economics 6, no. 3 (2023): 1–7. http://dx.doi.org/10.23880/jqhe-16000335.

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Drug Recommendation systems are the systems that have the capability to recommend drugs. On daily basis a huge amount of data is being generated by the patients. all this valuable data can be properly utilized for creating a reliable drug recommendation system. In this presented paper, we recommend a system for drug recommendations. The main scope of our system is to predict the correct medication based on reviews and ratings. Our proposed system uses natural language processing techniques (NLP), Recurrent neural network (RNN).and we also considering various metrices like Precision, Recall, Ac

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Begum, S. Gousiya, and P. Kiran Sree. "Drug Recommendations Using a “Reviews and Sentiment Analysis” by a Recurrent Neural Network." Indonesian Journal of Multidisciplinary Science 2, no. 9 (2023): 3085–94. http://dx.doi.org/10.55324/ijoms.v2i9.530.

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Drug Recommendation systems have the capability to recommend drugs. On daily basis, a huge amount of data is being generated by the patients. All the valuable data can be properly utilized for creating a reliable drug recommendation system. In this paper, the researchers aimed to propose a system for drug recommendations. The main scope of the system is to predict the correct medication based on reviews and ratings. The proposed system uses Natural Language Processing techniques (NLP) and Recurrent Neural Network (RNN). The researchers also considered various metrices such as precision, recall

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Shao, Mengting, Leiming Jiang, Zhigang Meng, and Jianzhen Xu. "Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity." Molecules 27, no. 4 (2022): 1404. http://dx.doi.org/10.3390/molecules27041404.

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Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug–drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES

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Kaumudhi, Apoorva, Asiya Khatoon, Ayesha Samreen, Manisha Surana, and Brundha ElciJ. "VOICE ASSISTANT DRUG RECOMMENDATION." International Journal of Innovative Research in Advanced Engineering 9, no. 8 (2022): 299–302. http://dx.doi.org/10.26562/ijirae.2022.v0908.26.

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Since the coronavirus emerged, the shortage of appropriate clinical resources—such as specialists, care personnel, the right equipment and medications, etc. has reached an all-time high. The medical profession as a whole is in difficulty, which leads to many people passing away. Due to inconvenience, people began taking medications frequently without getting proper consultation, which made their health situation worse than usual. Recently, machine learning has proven useful in many areas, and creative work for automation is on the rise. It aims to provide a drug recommendation system that will

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Jayaram, Jayapradha, Yukta Kulkarni, Lakshmi Vadhanie Ganesh, Palanichamy Naveen, and Elham Abdulwahab Anaam. "Treatment Recommendation using BERT Personalization." Journal of Informatics and Web Engineering 3, no. 3 (2024): 41–62. http://dx.doi.org/10.33093/jiwe.2024.3.3.3.

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This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named `PharmaBERT`, includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs a

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Indriyani, Tiara, Afu Ichsan Pradana, and Dwi Hartanti. "Penerapan Metode Content-Based Filtering Pada Sistem Rekomendasi Pemilihan Produk Obat Studi Kasus : Apotek Hero Farma." Infotek: Jurnal Informatika dan Teknologi 8, no. 2 (2025): 532–42. https://doi.org/10.29408/jit.v8i2.30498.

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Selecting the right drug product is an important aspect in pharmaceutical services, especially for customers who do not yet have a deep understanding of the content and function of each drug. The limited number of pharmacists at Hero Farma Pharmacy often causes the service process to be inefficient, especially when still relying on manual recommendation methods that take a long time. This study aims to design a recommendation system that can assist in drug selection by implementing the content-based filtering method. This system is built by processing product attributes such as drug name, cate

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Chiranth, H. A. Daiwik Gowda S. Harshith B. J. Harshith S. Manasa M. G. "HEALTH-GUARD: MULTI-DISEASE DETECTION SYSTEM." International Journal of Engineering Technology Research & Management (IJETRM) 09, no. 05 (2025): 386–91. https://doi.org/10.5281/zenodo.15501015.

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Medi Genius is an AI-powered healthcare platform designed to enhance diagnostics, treatment recommendations,and patient support. It includes a Disease Detection System using CNNs for early diagnosis, an AlternativeMedicine Recommendation System for drug substitutes, a Doctor Recommendation System using Decision Treesfor specialist matching, and a Healthcare Chatbot powered by Transformers for real-time medical assistance. Builtwith Python, TensorFlow, and Flask, Medi Genius improves healthcare accessibility, accuracy, and efficiency,making medical decisions more predictive and patient-centric.

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Sathappan, Ratna, Tholu Sai Indira, and A. Meenapriyadarsini. "Smart Recommendation System for Off-the Shelf Medicines." International Journal of Engineering & Technology 7, no. 2.24 (2018): 417. http://dx.doi.org/10.14419/ijet.v7i2.24.12126.

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Internet usage has been at an all-time high from 2000’s vintage years. The people who have access to the internet use it for numerous reasons such as social networking, marketing, promoting, enhancing businesses, consultancy, research, gaming and the list goes on. In the recent years, Review websites have flourished, where people share their opinion about a product, with an increase in response rate and reliability. Recommendations are made by mining data from review websites. Traditional Recommendation systems are limited as they only consider certain metrics, such as product purchase details

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Omodunbi, Theresa O., Grace E. Alilu, Kennedy O. Obohwemu, and Rhoda N. Ikono. "Enhancing Drug Recommender System for Peptic Ulcer Treatment." International Journal of Information Technology and Computer Science 16, no. 6 (2024): 15–26. https://doi.org/10.5815/ijitcs.2024.06.02.

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Drug Recommender Systems (DRS) streamline prescription process and contribute to better healthcare. Hence, this study developed a DRS that recommends appropriate drug(s) for the treatment of an ailment using Peptic Ulcer Disease (PUD) as a case study. Patients’ and drug data were elicited from MIMIC-IV and Drugs.com, respectively. These data were analysed and used in the design of the DRS model, which was based on the hybrid recommendation approach (combining the clustering algorithm, the Collaborative Filtering approach (CF), and the Knowledge-Based Filtering approach (KBF)). The factors that

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Sohail, Khan. "Enhancing Medical Prescriptions with Machine Learning: A Symptoms Based Drug Recommendation System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45388.

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Prescribing the right medication usually depends on a doctor’s experience and general treatment guidelines, but it often misses an important piece of the puzzle — the specific symptoms a patient is experiencing. With more health data now available in digital form, especially information directly shared by patients, there’s a real chance to improve how prescriptions are made using machine learning. This project introduces a Symptom-Based Drug Recommendation System (SB-DRS) that uses Natural Language Processing (NLP) and supervised learning to better understand symptoms, identify likely diseases

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Pramita, Made Dinda Pradnya, Made Sudarma, and Ida Bagus Alit Swamardika. "Analysis of Sales Pattern Determination System and Drug Stock Recommendation." Jurnal Ilmu Komputer 12, no. 2 (2019): 53. http://dx.doi.org/10.24843/jik.2019.v12.i02.p04.

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The tight competition in the pharmacy industry, requires pharmacy owners to develop strategies in increasing drug sales. One of the strategies carried out is to analyze patterns of drug sales and determine drug stock recommendations based on sales transaction data. Based on this, an application was built to determine the pattern of drug sales and drug stock recommendations by using a modified Apriori Algorithm and Triple Exponential Smoothing Method. Apriori algorithm modification is used to overcome the problem of large amounts of sales transaction data, thus minimizing the time in the databa

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C, Madhu. "Privacy-Preserving AI for Patient-Specific Drug Recommendation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49901.

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Abstract—This paper presents a privacy-preserving drug rec- ommendation system that leverages patient symptom inputs and advanced machine learning techniques to predict potential diseases and provide personalized treatment suggestions. The system ensures the secure handling of user data by incorporating privacy-preserving AI methodologies such as data minimization, local processing, and encryption techniques. Additionally, it maintains timestamped patient histories in the IST timezone to support continuity of care without compromising sensitive information. The proposed approach integrates a u

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Thorat, Shubham, Tushar Bargal, Rahul Chavan, Shraddha Ghodekar, and Prof Nilesh Gunaware. "Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1317–22. http://dx.doi.org/10.22214/ijraset.2022.47474.

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Abstract: It's been like this since the coronavirus outbreak It is increasingly difficult to obtain legal treatment resources, such as the lack of specialists and health personnel, Appropriate equipment and medicines, etc. there are many Deaths attributable to the entire healthcare industry Rebellious. People started taking it due to lack of availability self-medication without proper advice, This made her health worse than usual. recent, Machine learning has been demonstrated in various the use of automation and creative work is increasing. The purpose of this paper is to propose a prescripti

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Sivaiah, B., Grishma, S. Tharun, and A. Shashi. "Drug Recommendation System on Sentiment Analysis of Drug Reviews by Using Machine learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 2019–23. http://dx.doi.org/10.22214/ijraset.2024.59247.

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Abstract: With the healthcare system facing increased challenges due to the COVID-19 pandemic, there's a big demand for new ideas to help doctors and nurses. This paper suggests a new way of using computers to help doctors decide which medicines to give to patients. By using smart computer programs, we can make it easier for healthcare workers to handle their workload and provide better care for patients. By analyzing patient reviews, we employ sentiment analysis using advanced vectorization methods like Bag of Words, Term Frequency-Inverse Document Frequency (TF-IDF), and Manual Feature Analy

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Abbas, Khizar, Muhammad Afaq, Talha Ahmed Khan, and Wang-Cheol Song. "A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry." Electronics 9, no. 5 (2020): 852. http://dx.doi.org/10.3390/electronics9050852.

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From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the market. Counterfeit drugs are analyzed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization

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Bhoi, Suman, Mong Li Lee, Wynne Hsu, Hao Sen Andrew Fang, and Ngiap Chuan Tan. "Personalizing Medication Recommendation with a Graph-Based Approach." ACM Transactions on Information Systems 40, no. 3 (2022): 1–23. http://dx.doi.org/10.1145/3488668.

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The broad adoption of electronic health records (EHRs) has led to vast amounts of data being accumulated on a patient’s history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. However, existing medication recommendation systems have yet to make use of all these information sources in a seamless manner, and they do not provide a justification on why a particular medication is recommended. In this work, we design a two-stage personalized medication recommender sys

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Vardhan, P. Harsha. "DRUG RECOMMENDATION SYSTEM BASED ON SENTIMENT ANALYSIS OF DRUG REVIEWS USING PASSIVE AGGRESSIVE CLASSIFIER." international journal of advanced research in computer science 16, no. 2 (2025): 38–45. https://doi.org/10.26483/ijarcs.v16i2.7223.

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The COVID-19 pandemic has severely strained healthcare resources, leading to a shortage of specialists, medical equipment, and medicines. As a result, many individuals have resorted to self-medication without proper consultation, often worsening their health conditions. To address this issue, we propose a Drug Recommendation System that utilizes sentiment analysis of patient reviews to assist in selecting the most effective medications. Our approach involves preprocessing drug review data by removing stop words, correcting misspellings, and tokenizing text. We employ TF-IDF vectorization for f

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G, Prarthana. "Smart Drug Recommendation with AI Chatbot for Personalized Healthcare." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 258–61. https://doi.org/10.22214/ijraset.2025.66248.

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This AI-driven web application combines machine learning with an advanced chatbot to deliver personalized healthcare solutions The chatbot communicates with users in a conversational manner to collect information about their symptoms, which is subsequently processed by a machine learning system to suggest possible health conditions. Following this assessment, the application suggests suitable medications for the identified issues and offers natural home remedies for temporary symptom relief. A unique health reminder system checks in with users periodically, tracking symptom progression and off

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Swetha S, Sowmya S R, and Pratishta S. "Disease Prediction and Drug Recommendation Using ML." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 10 (2024): 3156–59. http://dx.doi.org/10.47392/irjaem.2024.0465.

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In today's dynamic healthcare landscape, the integration of technology has become indispensable. The project sets out to create a cutting-edge healthcare solution that will transform the very foundations of healthcare decision-making by utilizing the powerful powers of machine learning. The main objective of this project is to develop a dual-dashboard system that will serve both patients and medical staff. The main objective is to reduce the gap between patients and healthcare professionals, which has existed for a long time. It makes it possible for people to receive highly personalized healt

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J, Omana, Jeipratha P. N, Devi K, Benila S, and Revathi K. "Personalized Drug Recommendation System Using Wasserstein Auto-encoders and Adverse Drug Reaction Detection with Weighted Feed Forward Neural Network (WAES-ADR) in Healthcare." Journal of Informatics and Web Engineering 4, no. 1 (2025): 332–47. https://doi.org/10.33093/jiwe.2025.4.1.24.

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In recent years, the use of deep learning approaches in healthcare has yielded promising results in a variety of fields, most notably in the detection of adverse drug reactions (ADRs) and drug recommendations. This paper promises a breakthrough in this field by using Wasserstein autoencoders (WAEs) for personalized medicine recommendation and ADR detection. WAEs' capacity to manage complex data distributions and develop meaningful latent representations makes them ideal for modeling heterogeneous healthcare data. This study intends to improvise the precision and efficiency of drug recommendati

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Gotfrit, Joanna, Ashley Jackson, John J. W. Shin, David J. Stewart, Ranjeeta Mallick, and Paul Wheatley-Price. "Determinants of the Cancer Drug Funding Process in Canada." Current Oncology 29, no. 3 (2022): 1997–2007. http://dx.doi.org/10.3390/curroncol29030162.

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Background: Canada has a publicly funded healthcare system with a complex drug funding process. After Health Canada approval to market a drug, the pan-Canadian Oncology Drug Review (pCODR) (now renamed the CADTH reimbursement review) makes a non-binding funding recommendation to the Canadian provinces (except Quebec), which each then decide whether the drug will be publicly funded. We identified the determinants of funding in this process. Methods: We analyzed drugs for advanced lung (n = 15), breast (n = 8), colorectal (CRC) (n = 7), melanoma (n = 10), and neuroendocrine (NET) (n = 3) cancers

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de Souza, Andrea Brígida, Avila Vidal, Pollyanna Gomes, Vania Canuto, and Clarice Petramale. "PP086 Horizon Scanning In Multiple Sclerosis Decisions In Brazil." International Journal of Technology Assessment in Health Care 33, S1 (2017): 111. http://dx.doi.org/10.1017/s0266462317002549.

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INTRODUCTION:In Brazil, the pharmaceutical sector has requested an individual incorporation in the Brazilian public health system (SUS) for each new drug for multiple sclerosis that receives sanitary authorization for marketing. Horizon Scanning within Brazilian Ministry of Health has played a key role in the recommendations made by the National Committee for Health Technology Incorporation (CONITEC). Horizon Scanning seeks to predict which technologies have potential to impact health care in SUS, before their formal request. This study aims to present the impact of horizon scanning in two ass

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Bhimavarapu, Usharani, Nalini Chintalapudi, and Gopi Battineni. "A Fair and Safe Usage Drug Recommendation System in Medical Emergencies by a Stacked ANN." Algorithms 15, no. 6 (2022): 186. http://dx.doi.org/10.3390/a15060186.

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The importance of online recommender systems for drugs, medical professionals, and hospitals is growing. Today, the majority of people use online consultations for drug recommendations for all types of health issues. Emergencies such as pandemics, floods, or cyclones can be helped by the medical recommender system. In the era of machine learning (ML), recommender systems produce more accurate, quick, and reliable clinical predictions with minimal costs. As a result, these systems maintain better performance, integrity, and privacy of patient data in the decision-making process and provide prec

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Yang, Mengyun, Huimin Luo, Yaohang Li, and Jianxin Wang. "Drug repositioning based on bounded nuclear norm regularization." Bioinformatics 35, no. 14 (2019): i455—i463. http://dx.doi.org/10.1093/bioinformatics/btz331.

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Abstract Motivation Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent

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He, Na, Shan Su, Zhikang Ye, et al. "Evidence-based Guideline for Therapeutic Drug Monitoring of Vancomycin: 2020 Update by the Division of Therapeutic Drug Monitoring, Chinese Pharmacological Society." Clinical Infectious Diseases 71, Supplement_4 (2020): S363—S371. http://dx.doi.org/10.1093/cid/ciaa1536.

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Abstract Background Clinical practice guidelines or recommendations often require timely and regular updating as new evidence emerges, because this can alter the risk-benefit trade-off. The scientific process of developing and updating guidelines accompanied by adequate implementation can improve outcomes. To promote better management of patients receiving vancomycin therapy, we updated the guideline for the therapeutic drug monitoring (TDM) of vancomycin published in 2015. Methods Our updated recommendations complied with standards for developing trustworthy guidelines, including timeliness a

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Oliveira, Eduardo, Luciana Xavier, Priscila Louly, Clementina Prado, and Luciene Bonan. "PP64 Impact Of The Cost-Effectiveness Threshold On Drug Funding Recommendations: The Case Of Rare Diseases In Brazil." International Journal of Technology Assessment in Health Care 40, S1 (2024): S81. https://doi.org/10.1017/s0266462324002356.

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IntroductionIn 2022, the Brazilian public health system has adopted an explicit cost-effectiveness threshold of USD24,232.91 per quality-adjusted life years (QALY) for evaluating technologies intended for rare diseases treatment. Although regarded as a strategy for increasing efficiency, the National Committee for Health Technology Incorporation (Conitec) has recommended that the threshold should not be used as a knockout parameter.MethodsA retrospective analysis of Conitec’s recommendations regarding technologies for rare diseases issued between January and October 2023 was conducted. The fol

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Zheng, Haotian, Kangming Xu, Huiming Zhou, Yufu Wang, and Guangze Su. "Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis." Academic Journal of Science and Technology 10, no. 1 (2024): 62–68. http://dx.doi.org/10.54097/v160aa61.

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Natural Language Processing (NLP) is an interdisciplinary field of computer science, artificial intelligence, and linguistics that focuses on the ability of computers to understand, process, generate, and simulate human language in order to achieve the ability to have natural conversations with humans. The underlying principles of natural language processing are at multiple levels, including linguistics, computer science, and statistics. It involves the study of language structure, semantics, grammar and pragmatics, as well as the statistical analysis and modeling of large-scale corpora. In th

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Gaurav Goel. "Federated Learning Based Model for Recommendation System Based in Health Care." Journal of Information Systems Engineering and Management 10, no. 4s (2025): 567–76. https://doi.org/10.52783/jisem.v10i4s.567.

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Precision Medicine is an emerging healthcare approach that focuses on tailoring treatments for individual patients. The implementation of patient-centred Decision Support Systems, including Health Recommender Systems, is a key component of this initiative, aimed at augmenting the accuracy and individualization of healthcare delivery. However, a significant challenge in developing these systems is the confidential nature of the medical data, as these systems require large volumes of data to function effectively. Unfortunately, medical data are distributed across multiple institutions and cannot

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Krishna, Vivek, and Manish Singh. "Personalized movie recommendation system: Tailoring cinematic suggestions." International Journal of Applied Research 4, no. 11 (2018): 306–15. http://dx.doi.org/10.22271/allresearch.2018.v4.i11d.11462.

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Dinda Maristha, Made Devayani, Albertus Joko Santoso, and Findra Kartika Sari Dewi. "Sistem Rekomendasi Pembelian Produk Kesehatan pada E-Commerce ABC berbasis Graph Database Amazon Neptune menggunakan Metode Hybrid Content-Collaborative Filtering." Jurnal Buana Informatika 12, no. 2 (2021): 88. http://dx.doi.org/10.24002/jbi.v12i2.4623.

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Abstract. Recommendation System of Health Product Purchasing at ABC E-Commerce System based on Amazon Neptune’s Graph Database using Hybrid ContentCollaborative Filtering Method.Health products purchased by society, either in drugstores or pharmacies may vary according to their needs. ABC e-commerce is a Business to Business (B2B)-based e-commerce owned by PT XYZ. As a health product sales system from distributors to drug stores/pharmacies, they still do not have a health product purchase recommendation system yet. The recommendation system is needed to provide recommendations of health produc

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Rohma Umaysaroh, Nur, Jejen Jaenudin, and Fitria Rachmawati. "Sistem Informasi Rekomendasi Pemesanan Obat Dengan Metode Reorder Point Di Apotek Tunggilis." ETNIK: Jurnal Ekonomi dan Teknik 2, no. 3 (2023): 251–67. http://dx.doi.org/10.54543/etnik.v2i3.170.

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In ensuring the quality of pharmaceutical services, proper control of pharmaceutical supplies must be carried out. One of them is to prevent or minimize the occurrence of vacancies in pharmaceutical supplies needed. Problems that occur at the Tunggilis Pharmacy include the inaccuracy of officers in checking drug stock, delays in ordering drugs to distributors, inaccurate procurement planning resulting in conditions where inventory is smaller or larger than needed. The process of ordering drugs that is done conventionally will take a lot of time and energy so that an information system is neede

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Freystaetter, G., A. Platz, C. Meier, H. U. Mellinghoff, and R. Theiler. "Capture the fracture by SMS." Osteologie 25, no. 04 (2016): 283–86. http://dx.doi.org/10.1055/s-0037-1619024.

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SummaryIn this observational study a SMS reminder system was tested to improve patient adher- ence to osteoporosis drug therapy. 399 of 1323 osteoporosis fracture patients could be documented. 66 % of patients who received a SMS recommendation arranged an ap- pointment with their primary care physician. A large proportion of the physicians followed these recommendations. As more elderly patients declined to participate, the SMS tool seems to be useful in younger seniors (< 70 years).

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Qashlim, Akhmad, and Basri B. "Integration of Information System Based on Supply Chain Management (SCM) for Pharmaceutical Warehouse in Mamasa Regency." ComTech: Computer, Mathematics and Engineering Applications 9, no. 1 (2018): 1. http://dx.doi.org/10.21512/comtech.v9i1.4027.

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This research applied information system by using Supply Chain Management (SCM) in the pharmaceutical warehouse in Mamasa Regency. The scope of the research was maximized on pharmaceutical warehouse operations and drug distribution to clinics. This research did not focus on cost, profit, lead time, and procurement costs. This system was built using PHP programming language and MySQL database. System development process used waterfall method. It consisted of requirement analysis, system analysis, design, development, and testing. The test system used the xampp 6.2 servers with a black box testi

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Chinnala Balakrishna and Shepuri Srinivasulu. "Drug suggestion mechanism in medical emergencies using machine learning." International Journal of Science and Research Archive 13, no. 1 (2024): 901–6. http://dx.doi.org/10.30574/ijsra.2024.13.1.1768.

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In the field of healthcare, prompt and precise medicine recommendations during medical emergencies can have a substantial impact on patient outcomes. This describes a comprehensive "Drug Suggestion Method in Medical Emergencies Using Machine Learning," which was built in Python. The system uses two sophisticated classification algorithms, the Random Forest Classifier and the Decision Tree Classifier, to achieve amazing 100% accuracy levels on both training and test datasets. The dataset for this system consists of 1100 records, each with 30 features. These aspects encompass a wide range of med

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Cooper, James W., William E. Wade, Christopher L. Cook, and Allison H. Burfield. "Consultant Pharmacist Drug Therapy Recommendations Acceptance and Rejection from Monthly Drug Regimen Reviews in a Geriatric Nursing Facility: Fourth Year Results and Cost Analysis." Hospital Pharmacy 42, no. 8 (2007): 729–36. http://dx.doi.org/10.1310/hpj4208-729.

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Purpose To document and compare the outcomes from monthly drug regimen review recommendation acceptance and rejection in one skilled nursing facility by one consultant pharmacist (CP) in the fourth year of evaluation with the prior 3 years' data. Method A non-randomized, observational, prospective cohort study with all patients being residents for at least 30 days over the 12-month period (October 1, 1997 to September 30, 1998) in a skilled nursing facility with more than 100 beds. The admission problem-oriented records of all patients and their respective CP reports were screened for pharmaco

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Filatova, Yu S., I. N. Solovyov, and A. M. Gruzdev. "Osteoarthritis of the hip joints: possibilities of local therapy." Meditsinskiy sovet = Medical Council, no. 21 (November 23, 2023): 152–58. http://dx.doi.org/10.21518/ms2023-407.

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Osteoarthritis of the hip joints is one of the most common pathology of the musculoskeletal system. The issues of its therapy are discussed in all clinical recommendations and recently more and more attention has been paid to the introduction of hyaluronic acid preparations. In the Russian recommendations for the treatment of coxarthrosis, this recommendation has a high level of credibility and evidence and is based on a significant number of studies confirming the safety and effectiveness of this therapy in the long term. The article provides data from some studies, as well as the conclusions

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Sugandi, Gagan, Tezza Adriansyah Anwar, and Ari Purno Wahyu Wibowo. "Proposed Sales Strategy in Herbal Medicine Products Using the Association Rule Method for Trimitra Herbal Pharmacy." International Journal of Engineering & Technology 7, no. 4.34 (2018): 302. http://dx.doi.org/10.14419/ijet.v7i4.34.25300.

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Pharmacies are a place for selling drugs based on a prescription from a doctor. Pharmacies themselves have a close relationship with pharmaceutical companies and of course the Ministry of Health as a regulator related to the sale and marketing of drugs to the public. The main task of a pharmacy is to distribute drugs and provide a report data recap. At present, the problems faced in drug distribution are the uneven demand for drug distribution. Sometimes, the drug runs out or excess stock will be problematic with the accumulation of the amount of goods in the warehouse and can become expired a

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Alshameri, Mariam Sabr, Samar Saad Alotaibi, Nawal Hlal Almutairi, et al. "Pharmaceutical supply chain management: Ensuring quality and security in the distribution of medicines." International journal of health sciences 6, S10 (2022): 1644–58. http://dx.doi.org/10.53730/ijhs.v6ns10.15027.

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Background: The globalization of pharmaceutical supply chains has exposed significant vulnerabilities, enabling counterfeit medications to infiltrate the market. These counterfeit drugs not only fail to aid in the recovery of patients but also pose serious health risks due to their inferior quality. The World Health Organization (WHO) reports that in developing nations, approximately 10% of medications consumed by patients are counterfeit. This alarming statistic, coupled with the estimated annual loss of over $200 billion faced by US pharmaceutical companies, underscores the urgent need for a

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Raole, Vinay M., and Vaidehi V. Raole. "Recommendation for Standardization of Botanical Nomenclature in Traditional and Complementary Medicinal Systems." Journal of Tropical Ethnobiology 5, no. 1 (2022): 1–7. http://dx.doi.org/10.46359/jte.v5i1.102.

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Plant Nomenclature is an essential requirement for publications in drug discovery and in pharmacological investigations in modern and traditional medical systems. Mostly names of plants can be presented by pharmaceutical names or scientific binomial names. In this paper, good and bad aspects of both systems are discussed in the context of the recent scientific nomenclatural framework and the systems for its practical applicability. WHO Programme for International Drug Monitoring and is responsible for the WHO Adverse Drug Reaction (ADR) database that currently contains 3.6 million records. Num

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Pokale, Sarthak. "DOCLAB: A Technology-Driven Platform for Healthcare Accessibility, Prediction and Drug Recommendation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45860.

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ABSTRACT: The current healthcare ecosystem in many regions suffers from inefficiencies such as limited access, lack of early diagnosis, long waiting times, and fragmented services. Doclab is a digital healthcare platform designed to bridge these gaps by offering a comprehensive and user-friendly interface that integrates symptom-based disease prediction, personalized drug recommendations, and medicine delivery. Leveraging both classical machine learning (TF-IDF with Naive Bayes, Logistic Regression, SVM) and advanced deep learning techniques (BiLSTM with Attention), Doclab intelligently proces

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Shehata, Zahraa Hassan Abdelrahman, Nagwa Ali Sabri, and Ahmed Abdelsalam Elmelegy. "Descriptive analysis of medication errors reported to the Egyptian national online reporting system during six months." Journal of the American Medical Informatics Association 23, no. 2 (2015): 366–74. http://dx.doi.org/10.1093/jamia/ocv096.

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Abstract Objectives This study analyzes reports to the Egyptian medication error (ME) reporting system from June to December 2014. Methods Fifty hospital pharmacists received training on ME reporting using the national reporting system. All received reports were reviewed and analyzed. The pieces of data analyzed were patient age, gender, clinical setting, stage, type, medication(s), outcome, cause(s), and recommendation(s). Results Over the course of 6 months, 12 000 valid reports were gathered and included in this analysis. The majority (66%) came from inpatient settings, while 23% came from

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Jadhav, Jayendra S., and Jyoti Deshmukh. "PandemicShield: A Unified AI-Blockchain Framework for Disease Detection, Drug Forecasting, and Traceability." International Journal of Environmental Sciences 11, no. 5s (2025): 56–77. https://doi.org/10.64252/sztsks92.

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This study unveils an innovative system that merges machine learning and Blockchain technologies to tackle essential issues in pandemic scenarios, including the early identification of unknown diseases, prediction of drug requirements, and suggestion of appropriate medications, secure tracking of drug supply chains, and providing clear, interpretable insights. The approach integrates Graph Neural Networks (GNNs) with ensemble learning methods for detecting diseases, employs causal inference alongside ARIMA for forecasting drug needs, utilizes reinforcement learning (RL) for recommending drugs,

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