{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:25:48Z","timestamp":1775665548633,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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 precise information at any time. Therefore, we present drug recommender systems with a stacked artificial neural network (ANN) model to improve the fairness and safety of treatment for infectious diseases. To reduce side effects, drugs are recommended based on a patient\u2019s previous health profile, lifestyle, and habits. The proposed system produced results with 97.5% accuracy. A system such as this could be useful in recommending safe medicines to patients, especially during health emergencies.<\/jats:p>","DOI":"10.3390\/a15060186","type":"journal-article","created":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T07:05:07Z","timestamp":1653635107000},"page":"186","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["A Fair and Safe Usage Drug Recommendation System in Medical Emergencies by a Stacked ANN"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0246-1420","authenticated-orcid":false,"given":"Usharani","family":"Bhimavarapu","sequence":"first","affiliation":[{"name":"School of Competitive Coding (SOCC), Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijayawada 522502, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0818-306X","authenticated-orcid":false,"given":"Nalini","family":"Chintalapudi","sequence":"additional","affiliation":[{"name":"Informatics Centre, School of Science and Technology, University of Camerino, 62032 Camerino, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0603-2356","authenticated-orcid":false,"given":"Gopi","family":"Battineni","sequence":"additional","affiliation":[{"name":"Clinical Research Center, School of Medicinal Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Battineni, G., Sagaro, G.G., Chintalapudi, N., Di Canio, M., and Amenta, F. 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