A multi-level hypoglycemia early alarm system based on sequence pattern mining

Abstract Background Early alarm of hypoglycemia, detection of asymptomatic hypoglycemia, and effective control of blood glucose fluctuation make a great contribution to diabetic treatment. In this study, we designed a multi-level hypoglycemia early alarm system to mine potential information in Continuous Glucose Monitoring (CGM) time series and improve the overall alarm performance for different clinical situations. Methods Through symbolizing the historical CGM records, the Prefix Span was adopted to obtain the early alarm/non-alarm frequent sequence libraries of hypoglycemia events. The longest common subsequence was used to remove the common frequent sequence for achieving the hypoglycemia early alarm in different clinical situations. Then, the frequent sequence pattern libraries with different risk thresholds were designed as the core module of the proposed multi-level hypoglycemia early alarm system. Results The model was able to predict hypoglycemia events in the clinical dataset of level-I (sensitivity 85.90%, false-positive 23.86%, miss alarm rate 14.10%, average early alarm time 20.61 min), level-II (sensitivity 80.36%, false-positive 17.37%, miss alarm rate 19.63%, average early alarm time 27.66 min), and level-III (sensitivity 78.07%, false-positive 13.59%, miss alarm rate 21.93%, average early alarm time 33.80 min), respectively. Conclusions The proposed approach could effectively predict hypoglycemia events based on different risk thresholds to meet different prevention and treatment requirements. Moreover, the experimental results confirm the practicality and prospects of the proposed early alarm system, which reflects further significance in personalized medicine for hypoglycemia prevention.

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A Multi-level Hypoglycemia Early Alarm System Based on Sequence Pattern Mining

10.21203/rs.3.rs-89896/v1 ◽

2020 ◽

Author(s):

Xia Yu ◽

Ning Ma ◽

Tao Yang ◽

Yawen Zhang ◽

Qing Miao ◽

...

Keyword(s):

False Positive ◽

Pattern Mining ◽

Longest Common Subsequence ◽

Effective Control ◽

Diabetic Patients ◽

Alarm System ◽

Sequence Pattern ◽

Frequent Sequence ◽

Clinical Dataset ◽

Multi Level

Abstract Background: Early alarm of hypoglycemia, detection of asymptomatic hypoglycemia, and effective control of blood glucose fluctuation make a great contribution to the treatment for diabetic patients. In this study, we designed a multi-level hypoglycemia early alarm system to improve the overall performance of hypoglycemia early alarm. Methods: Through symbolizing the historical CGM data, the Prefix Span was adopted to obtain the early alarm and non-alarm frequent sequence libraries of hypoglycemia. The longest common subsequence was used to remove the common frequent sequence for achieving the hypoglycemia early alarm on the clinical dataset. Frequent sequence pattern libraries with different risk levels were designed by choosing different thresholds and a multi-level hypoglycemia early alarm system for different clinical situations were established. Results: The model was able to predict hypoglycemia events in the clinical dataset of level-I (sensitivity 85.90%, false-positive 23.86%, miss alarm rate 14.10%, average early alarm time 20.61min), level-II (sensitivity 80.36%, false-positive 17.37%, miss alarm rate 19.63%, average early alarm time 27.66min), and level-III (sensitivity 78.07%, false-positive 13.59%, miss alarm rate 21.93%, average early alarm time 33.80min), respectively.Conclusions: The proposed approach could effectively predict hypoglycemia events on the basis of different thresholds to meet different prevention and treatment requirements in clinical situations. Moreover, the experimental results confirm the practicality and prospects of the proposed early alarm system, which reflects further significance in personalized medicine for hypoglycemia prevention.

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Frequent Sequence Pattern Mining with Differential Privacy

Intelligent Computing Theories and Application - Lecture Notes in Computer Science ◽

10.1007/978-3-319-95930-6_42 ◽

2018 ◽

pp. 454-466 ◽

Cited By ~ 1

Author(s):

Fengli Zhou ◽

Xiaoli Lin

Keyword(s):

Pattern Mining ◽

Differential Privacy ◽

Sequence Pattern ◽

Frequent Sequence

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Constructing A Novel Spark-based Distributed Maximum Frequent Sequence Pattern Mining for IoT Log

Proceedings of the 8th International Conference on Communication and Network Security - ICCNS 2018 ◽

10.1145/3290480.3290497 ◽

2018 ◽

Author(s):

Xiong Chen ◽

Ruliang Xiao ◽

Du Xin ◽

Xinhong Lin ◽

Li Lin

Keyword(s):

Pattern Mining ◽

Sequence Pattern ◽

Frequent Sequence

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924-P: Pattern Mining of Trajectories of Glucose Values of Continuous Glucose Monitoring System by Artificial Intelligence in Type 2 Diabetes Patients

Diabetes ◽

10.2337/db19-924-p ◽

2019 ◽

Vol 68 (Supplement 1) ◽

pp. 924-P

Author(s):

MASAKI MAKINO ◽

RYO YOSHIMOTO ◽

MIZUHO KONDO-ANDO ◽

YASUMASA YOSHINO ◽

IZUMI HIRATSUKA ◽

...

Keyword(s):

Artificial Intelligence ◽

Type 2 Diabetes ◽

Monitoring System ◽

Continuous Glucose Monitoring ◽

Pattern Mining ◽

Glucose Monitoring ◽

Continuous Glucose Monitoring System ◽

Diabetes Patients

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Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666191113122840 ◽

2020 ◽

Vol 16 (8) ◽

pp. 1004-1021

Author(s):

Xiaoqi Lu ◽

Yu Gu ◽

Lidong Yang ◽

Baohua Zhang ◽

Ying Zhao ◽

...

Keyword(s):

False Positive ◽

Contextual Information ◽

Lung Nodule ◽

Positive Sample ◽

Lung Nodules ◽

Lung Nodule Detection ◽

Nodule Detection ◽

Level Information ◽

Multi Level ◽

Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

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