Data · dataset · 2025
IoT-Integrated Smart Ward Dataset for Environmental, Physiological, and Clinical Analysis of Patient Recovery
Listed in Teesside University Research Data Repository
The IoT-Integrated Smart Ward Dataset: Environmental, Physiological, and Clinical Insights into Patient Recovery contains information from 688 inpatients admitted to a digitally monitored hospital in Pabna, Bangladesh, between April and October 2025.
Description
The dataset combines patient demographics, IoT-sensed ward conditions, physiological data, and recovery outcomes to explore how smart ward environments influence patient recovery.
It includes 21 features grouped into four categories: - Demographics: Patient_ID, Age, Gender, Blood_Group - Environment (IoT Sensors): Room_Temperature (°C), Humidity (%), Noise_Level (dB), Air_Quality_Index (AQI), Light_Intensity (Lux) - Physiological & Clinical: Heart_Rate (bpm), Blood_Pressure (mmHg), Body_Temperature (°C), Respiration_Rate (bpm), Oxygen_Saturation (SpO₂ %), Pain_Level_Score (1–10), Sleep_Quality (1–10), Primary_Diagnosis, Severity_Level - Treatment & Outcome: Treatment_Type, Ward_Type, Recovery_Speed (days) Data were collected from hospital IoT systems and electronic medical records, then carefully cleaned and standardized to ensure quality and consistency.
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Missing values were imputed, and all patient identifiers were anonymized for ethical use. This dataset serves as a valuable benchmark for data-driven healthcare research, enabling analysis of environmental, physiological, and clinical factors in patient recovery through machine learning and statistical modeling.
Links
Where it is published
- DOI doi.org/10.17632/rv36nm4wy8.3 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Machine learning · Medicine & Health
Provenance · 1 source records, 9 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/rv36nm4wy8.3 | 17 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |