Relevant bibliographies by topics / Drug recommendation system

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Academic literature on the topic 'Drug recommendation system'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Drug recommendation system"

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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 &amp; 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.&nbsp; 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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Books on the topic "Drug recommendation system"

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Office, General Accounting. Financial management: Recommendations on Indian trust fund Strategic Plan proposals : report to the Secretary of the Interior. The Office, 1997.

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Blume, H., and U. Gundert-Remy. Controlled/modified release products recommendation in support of EC-guidelines. CRC Press, 1991.

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HIV and drug free: Cancer and the immune system, including natural therapy recommendations. Renders Wellness/Pub., 2004.

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Al-Darraji, Haider A., and Frederick L. Altice. The Perfect Storm. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199374847.003.0008.

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Globally, tuberculosis (TB) is a major cause of morbidity and mortality among people who use drugs (PWUD), particularly those co-infected with HIV. This chapter describes how TB is prevalent in several prison systems by virtue of the concentration of PWUD and people living with HIV. TB is further amplified within this system through overcrowding, poor ventilation, and delayed access to quality prevention and treatment services. In many countries, individuals cycling through prisons are inadequately screened and treated for TB, and affected individuals may have frequent treatment interruptions.
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Cheong-Ann, Png. Part I The International Law of Tainted Money, 4 International Legal Sources III—FATF Recommendations. Oxford University Press, 2017. http://dx.doi.org/10.1093/law/9780198716587.003.0004.

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This chapter looks at the role and development of the Financial Action Task Force (FAFT). The FAFT was formally established at the G7 Summit in Paris in July 1989 by the Heads of State or Government of the G7 countries and the President of the European Commission. The main concern motivating the establishment of the FATF was the proliferation of drug production and drug-related activities, including the laundering of drug proceeds. The G7 leaders understood that decisive action at the national and international levels would be needed to deal with this concern. The chapter looks at how the FAFT
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McDougald, Laura. Recommendations for the implementation of an adverse drug event reporting and monitoring system for Hamilton Health Sciences Corporation. 1998.

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Wang, Cynthia, and Michelle Y. Braunfeld. Acute Liver Failure. Edited by Matthew D. McEvoy and Cory M. Furse. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190226459.003.0035.

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Acute liver failure produces widespread physiologic derangements including encephalopathy, coagulopathy, peripheral vasodilation, a systemic inflammatory response, and multiorgan failure. Morbidity is significant, and mortality is 50%. The classification of liver failure and the various etiologies, including viral hepatitis, drug-induced, toxins, and autoimmunity are reviewed here. The multisystem effects of acute liver failure influence all aspects of perioperative care and adequate supportive care during this time is crucial to providing the best possible outcome for the patient. Specific tr
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Guideline for Preventive Chemotherapy for the Control of Taenia solium Taeniasis. Pan American Health Organization, 2021. http://dx.doi.org/10.37774/9789275123720.

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The larval stage of the parasite Taenia solium can encyst in the central nervous system causing neurocysticercosis, which is the main cause of acquired epilepsy in the countries in which the parasite is endemic. Endemic areas are those with the presence (or likely presence) of the full life cycle of Taenia solium. The parasite is most prevalent in poor and vulnerable communities in which pigs roam free, open defecation is practiced, basic sanitation is deficient, and health education is absent or limited. Several tools are available for the control of Taenia solium. Preventive chemotherapy for
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Rajagopal, M. R., and Reena George. Providing palliative care in economically disadvantaged countries. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199656097.003.0002.

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A public health approach is the most sustainable way to provide palliative care for the majority of the world’s people who live in economically disadvantaged nations. With only 7% of the world’s medical morphine currently being used in these countries, much remains to be done to implement the World Health Organization’s recommendations on policy, drug availability and education. International expertise is crucial for planting palliative care. Local efforts have to propagate it by creating feasible and acceptable models. Partnerships should advocate for palliative care to become an integral par
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Stanford, Carol. Addiction in the Lives of Registered Nurses and Their Wake-Up Jolt to Recovery. The Rowman & Littlefield Publishing Group, 2018. https://doi.org/10.5040/9780761877684.

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In this age of increasing headlines about drug addiction and prescription drug abuse, this book is a timely revelation of how the nursing profession is also impacted by substance abuse. It allows nurses, who are the most trusted profession in society, who have been hidden within their profession and living with substance use disorders, to openly voice their personal experiences with addiction. Seven nurses detail their journey through family dynamics, early use as nursing students and later career nurses as they traveled deeper and deeper into their addiction. They discuss their shame, humilia
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Book chapters on the topic "Drug recommendation system"

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Al Mubasher, Hadi, and Mariette Awad. "An EANN-Based Recommender System for Drug Recommendation." In Engineering Applications of Neural Networks. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62495-7_4.

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Keikhosrokiani, Pantea, Katheeravan Balasubramaniam, and Minna Isomursu. "Drug Recommendation System for Healthcare Professionals’ Decision-Making Using Opinion Mining and Machine Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_15.

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AbstractThe concern has been raised regarding errors in drugs prescription and medical diagnostics that need to be carefully thought through. Both patient diagnosis and medication prescription are the responsibilities of healthcare providers. As the number of people with health issues rises, the healthcare professionals’ burden is increased. Medical errors may occur in the healthcare sector as a result of healthcare professionals prescribing drugs medicines based on inadequate information related to patient history and drug side effects. Therefore, this study aims to propose a drug recommender
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Singh, Nitya, Akshita Sah, Vartika, et al. "A Hybrid Ayurvedic Drug Recommendation System with Generative AI." In Innovative Computing and Communications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3588-4_15.

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Al Mubasher, Hadi, Ziad Doughan, Layth Sliman, and Ali Haidar. "A Novel Neural Network-Based Recommender System for Drug Recommendation." In Engineering Applications of Neural Networks. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34204-2_46.

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Verma, Madhav Mukund, and D. Anitha. "A Transformer Based Medicine Recommendation System that Uses Drug Reviews." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68905-5_34.

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Sahoo, Plavani, Dasari Prashanth Naidu, Mullapudi Venkata Sai Samartha, Shantilata Palei, Biswajit Jena, and Sanjay Saxena. "Drug Recommendation System for Cancer Patients Using XAI: A Traceability Perspective." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58174-8_24.

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Lei, Peng, Changan Yuan, Hongjie Wu, and Xingming Zhao. "Drug–Target Interaction Prediction Based on Graph Neural Network and Recommendation System." In Intelligent Computing Theories and Application. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13829-4_6.

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Kolla, Morarjee, Vemula Ishitha Reddy, and Rddhi Reddy. "A Framework for Patient Specific Drug Recommendation and Side-Effect Prediction System." In Power Energy and Secure Smart Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003661917-35.

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Komal Kumar, N., and D. Vigneswari. "A Drug Recommendation System for Multi-disease in Health Care Using Machine Learning." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5341-7_1.

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Montalvo, Lourdes, and Edwin Villanueva. "Drug Recommendation System for Geriatric Patients Based on Bayesian Networks and Evolutionary Computation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39512-4_77.

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Conference papers on the topic "Drug recommendation system"

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Kumar, C. Siva, P. Lakshmi Sagar, Golla Dinesh Kumar, Chereddy Harshitha Reddy, Bandlamudi Sai Krushna, and Chikkem Mahidhar Reddy. "Drug Recommendation System Using Patient Reviews Based on Sentimental Analysis." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10967924.

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Narayana, V. Lakshman, K. Sai Nikitha, SK Fathima Yaseen, P. Madhu Latha, and T. G. Sahithi. "Drug Recommendation System and Side Effects Based on Sentiment Analysis of Drug Reviews Using Machine Learning." In 2025 Fourth International Conference on Smart Technologies, Communication and Robotics (STCR). IEEE, 2025. https://doi.org/10.1109/stcr62650.2025.11019585.

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Anandhan, Logeswaran, Nandhini A., Sree Abinaya P., Ravikumar V., and Sabarish Sanjay. "Personalized Drug Information and Recommendation System Using Gradient Boosting Algorithm (GBM)." In 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2024. https://doi.org/10.1109/smart63812.2024.10882477.

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Niranjana, K., P. Sankarshana, L. Priyadharshini, S. L. S. Raajavinayaga Subaash, S. Jawahar, and V. Prabhakaran. "A Blockchain based Drug Supply Management and Recommendation System using Enhanced Learning Scheme." In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT). IEEE, 2024. https://doi.org/10.1109/gcat62922.2024.10923901.

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Dhawan, Shubhang, Krishan Kumar, and Shilpee Srivastava. "An Enhancement Of Online Drug Recommendation System Using BGFT-DBi-LSTM And PRFFC Approaches." In 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP). IEEE, 2024. http://dx.doi.org/10.1109/tiacomp64125.2024.00074.

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An, Zhongwei, Jiajie Xing, and Xianguo Zhang. "A Disease-Drug Interaction Prediction Framework Based on Knowledge Graph and Graph Contrastive Learning for Recommendation System." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822071.

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Muppidi, Mahesh, Sreeja Ponnagani, and Swetha Sunnapu. "Reinforcement Learning-Driven Multi-Modal Sentiment Analysis for Personalized Drug Recommendations." In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2025. https://doi.org/10.1109/icssas66150.2025.11080957.

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Garapati, Kartheek, Sri Satya Maram, V. M. Manikandan, and Shakeel Ahmed. "A Comprehensive Approach for Healthcare Decision-Making Through Integrated Data Mining and NLP-Enhanced Drug Recommendation Systems." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837245.

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Machemer, Lee, Otakar Jonas, and Barry Dooley. "Steam Cycle Chemistry Advisor." In CORROSION 2004. NACE International, 2004. https://doi.org/10.5006/c2004-04060.

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Abstract This paper reviews experience with installation and use of a cycle chemistry expert system. To help power plant operators and chemists to control cycle chemistry and corrosion, the expert system was developed based on industry guidelines and experience. This software uses inputs from plant analytical and other instrumentation and grab sample analyses to determine current water chemistry and corrosion problems and recommend corrective actions. It also verifies the chemical analytical data, automatically produces reports, and provides cycle and water treatment descriptions. Using this p
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Sripathi, Satwik Reddy, Nadella Venkata Sai Pradyumna, Akula Dhanush, and Subramani R. "Drug Recommendation System Using LDA." In 2022 International Conference on Futuristic Technologies (INCOFT). IEEE, 2022. http://dx.doi.org/10.1109/incoft55651.2022.10094396.

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Reports on the topic "Drug recommendation system"

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Tabunov, I. A., T. N. Mikhalenko, L. D. Kuznetsova, A. V. Suetova, and M. A. Shilovskiy. METHODOLOGICAL RECOMMENDATIONS FOR WORKING WITH CHILDREN IN A SOCIALLY DANGEROUS SITUATION. Cherepovets State University, 2022. http://dx.doi.org/10.12731/er0619.03122022.

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Statistics show that in recent years there has been an increase in the number of families falling into a socially dangerous situation. According to statistics provided by the departments for juvenile affairs of the Ministry of Internal Affairs of Russia in Cherepovets, the number of crimes in 2021 decreased by only 2.1% compared to 2020. This was influenced by objective factors, in particular the low standard of living, "chronic" unemployment, alcohol abuse, drug use. Having embarked on such a path, the family degrades socially and morally, condemning children to the same existence. It is not
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Gutiérrez, Catalina, and Úrsula Giedion. How Many Healthy Life years could Countries in Latin America and the Caribbean Gain with a Better Allocation of Pharmaceutical Spending?: Case Studies for Chile, Colombia, and the Dominican Republic. Inter-American Development Bank, 2023. https://doi.org/10.18235/0005161.

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Health spending is increasing globally due to demographic and epidemiological changes, rising demand for services driven by higher incomes, aging populations, and the emergence of expensive new health technologies. In Latin America and the Caribbean (LAC), health expenditure has risen from 6.6% to 7.9% of GDP over the past two decades. By 2030, it is expected to increase by another 2 percentage points. This projection excludes additional investments required to strengthen health systems post-pandemic. Given fiscal constraints caused by the pandemics economic impact, rising debt, and macroecono
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