An integrated data mining approach to real-time clinical monitoring and deterioration warning
Open accessAbstract
Clinical study found that early detection and intervention are essential for preventing clinical deterioration in patients, for patients both in intensive care units (ICU) as well as in general wards but under real-time data sensing (RDS). In this paper, we develop an integrated data mining approach to give early deterioration warnings for patients under real-time monitoring in ICU and RDS.
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For filtering artifacts, designed modules have usually applied threshold-based methods or used statistical tools to interpolate missing data points.
“According to the literature, for filtering artifacts, designed modules have usually applied threshold-based methods to filter sensor data [36,60] or used statistical tools to interpolate the missing data points [61].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [60]
Reducing the amount of sensor network data is another task in feature extraction and feature selection phases, leading to an arranged vector of features as input to data mining techniques like classifiers.
“Moreover, reducing the amount of sensor network data is another task in feature extraction and feature selection phases which leads to have an arranged vector of features as an input of data mining techniques like classifier methods [45,60].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [60]
Some data analysis systems in healthcare were designed to process short signals such as few minutes of ECG data, a few hours of heart rate or oxygen saturation, and measurement of blood pressures for a day.
“Some data analysis systems in healthcare were designed to process short signals such as few minutes of ECG data [74,92], a few hours of heart rate or oxygen saturation [60,61] and the measurement of blood pressures for a day [56].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [60]
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References 23 · Cited by 76
- Authors
- Mao, Y., Chen, W., Chen, Y., Lu, C., Kollef, M., & Bailey, T.
- Affiliations
- Washington University in St. Louis (US)
- Washington University in St. Louis, St. Louis, MO, USA
- Venue
- Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2012-08-12
- Pages
- 1140-1148
- OpenAlex
- W2062000081
- Cited by
- 76
- References
- 23
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 1 · 2023: 6 · 2022: 3 · 2021: 8 · 2020: 5 · 2019: 5 · 2018: 9 · 2017: 4 · 2016: 11 · 2015: 11 · 2014: 2 · 2013: 3 · 2012: 1
- FWCI
- 53.30
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Open Scholarship Institutional Repository (Washington University in St. Louis) (submittedVersion)
- Washington University in St. Louis Libraries
- Topics
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- early detection, intensive care unit, data mining, patient monitoring, general wards
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