Tele-Health Monitoring of Patient Wellness
| Autor: | Ross Sparks, Chris Okugami |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
020205 medical informatics
wellness forecasting business.industry Science Vital signs 02 engineering and technology Tele health QA75.5-76.95 medicine.disease vital signs 03 medical and health sciences 0302 clinical medicine machine learning Artificial Intelligence Electronic computers. Computer science 0202 electrical engineering electronic engineering information engineering medicine 030212 general & internal medicine Medical emergency business Software Information Systems |
| Zdroj: | Journal of Intelligent Systems, Vol 25, Iss 4, Pp 515-528 (2016) |
| ISSN: | 0334-1860 |
| Popis: | The vital signs of chronically ill patients are monitored daily. The record flags when a specific vital sign is stable or when it trends into dangerous territory. Patients also self-assess their current state of well-being, i.e. whether they are feeling worse than usual, neither unwell nor very well compared to usual, or are feeling better than usual. This paper examines whether past vital sign data can be used to forecast how well a patient is going to feel the next day. Reliable forecasting of a chronically sick patient’s likely state of health would be useful in regulating the care provided by a community nurse, scheduling care when the patient needs it most. The hypothesis is that the vital signs indicate a trend before a person feels unwell and, therefore, are lead indicators of a patient going to feel unwell. Time series and classification or regression tree methods are used to simplify the process of observing multiple measurements such as body temperature, heart rate, etc., by selecting the vital sign measures, which best forecast well-being. We use machine learning techniques to automatically find the best combination of these vital sign measurements and their rules that forecast the wellness of individual patients. The machine learning models provide rules that can be used to monitor the future wellness of a patient and regulate their care plans. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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