AI-Integrated Smart Medicine Dispenser with IoT Connectivity
Bibliographic record
Abstract
The healthcare industry encounters difficulties in patient management due to a shortage of personnel. As reported by the World Health Organization (WHO), there is an average of 3 nurses for every 1,000 patients worldwide, a number that drops to 1.96 in nations such as India. A considerable number of chronic patients, frequently older or unable to move, need prompt medication, which leads to diminished efficiency and increased mental and physical stress on nursing staff. Current medication management systems are deficient in real-time data processing, emergency response features, and predictive notifications for refills, which worsens these issues. To tackle these shortcomings, a proposed automated medicine dispensing system utilizing the Internet of Things (IoT) is presented. This system employs QR codes to log patient information, allocate devices to specific beds, and organize medication schedules with alerts for any missed doses. It connects with a Firebase real-time database, allowing for continuous monitoring and high-alert functions, which include audio-visual alarms for emergencies and mobile notifications through a dedicated app. The application also keeps track of medication refill timelines based on patient discharge dates recorded in the database. This solution decreases the chances of accidental overdoses and underdoses by up to 98% and achieves 96% accuracy in medication alert notifications. By reducing workload and enhancing communication, resource efficiency is improved and nurse welfare, both mentally and physically, is supported. The proposed system signifies a major advancement in IoT-enabled healthcare, enhancing both medication management and the quality of patient care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".