论文题目：Hypoglycemia Prediction Using Machine Learning Models for Patients With Type 2 Diabetes
scholar 引用：56
页数：5
发表时间：2014.10
发表刊物：Journal of Diabetes Science and Technology
作者：Bharath Sudharsan,Malinda Peeples,and Mansur Shomali
摘要：
Background:
Minimizing the occurrence of hypoglycemia in patients with type 2 diabetes is a challenging task since these patients typically check only 1 to 2 self-monitored blood glucose (SMBG) readings per day.
Method:
We trained a probabilistic model using machine learning algorithms and SMBG values from real patients. Hypoglycemia was defined as a SMBG value < 70 mg/dL. We validated our model using multiple data sets. In addition, we trained a second model, which used patient SMBG values and information about patient medication administration.
Results:
The optimal number of SMBG values needed by the model was approximately 10 per week. The sensitivity of the model for predicting a hypoglycemia event in the next 24 hours was 92% and the specificity was 70%. In the model that incorporated medication information, the prediction window was for the hour of hypoglycemia, and the specificity improved to 90%.
Conclusions:
Our machine learning models can predict hypoglycemia events with a high degree of sensitivity and specificity. These models—which have been validated retrospectively and if implemented in real time—could be useful tools for reducing hypoglycemia in vulnerable patients.
结论：
We developed hypoglycemia prediction models using machine learning methods. These models performed with a high degree of sensitivity and specificity using sparse SMBG data from distinct data sets.Models which used medication information had improved specificity, which would reduce false positive predictions.Future studies comparing the accuracy of machine learning models and traditional statistical methods for predicting hypoglycemia may be useful.Further testing should be conducted to confirm that the models can be deployed in real time and could extend beyond predicting hypoglycemia to also deliver an intervention message, which could further reduce the occurrence of this serious diabetes complication.Discussion：
The sparse data in the type 2 diabetes situation make a mathematical approach unfeasible. We decided to make the problem a classification one (ie, hypoglycemia y/n?)After testing multiple models and cross-validating them with multiple data sets, we have demonstrated the usefulness of this approach. As expected, the addition of more variables (in our case, medication information) improved the performance of the models.Introduction：
Most patients with type 2 diabetes have only sparse SMBG data, which do not lend themselves to statistical methods.正文组织架构：
1. Introduction
2. Methods
3. Results
4. Discussion
5. Conclusion
正文部分内容摘录：
1. Biological Problem: What biological problems have been solved in this paper?
hypoglycemia prediction, a binary (yes/no) classification problem2. Main discoveries: What is the main discoveries in this paper?
These models performed with a high degree of sensitivity and specificity using sparse SMBG data from distinct data sets.Models which used medication information had improved specificity, which would reduce false positive predictions.Note that random forest and support vector machine (SVM) performed best across all data sets with a prediction accuracy of over 90%.3. ML(Machine Learning) Methods: What are the ML methods applied in this paper?
random forest, support vector machine (SVM)The training data set of SMBG values came from deidentified patient data from a clinical trial of patients with type 2 diabetes.The original data set contained 56 000 SMBG data points collected in a 1-year prospective study.4. ML Advantages: Why are these ML methods better than the traditional methods in these biological problems?
traditional methods: k-nearest neighbor, and naïve Bayes.First, the algorithm must be widely accepted in the data science community for better support during modeling.Second, the algorithm should have a proven track record for accuracy and efficiency based on the type of data (mixed measure—numerical predictors and categorical response).Third, the algorithm should have a history of successful commercial usage in scoring data in a production level system for a large user base.the accuracy of the prediction should have significant confidencethe prediction algorithm should require only approximately 1 to 2 SMBG values per day5. Biological Significance: What is the biological significance of these ML methods’ results?
After testing multiple models and cross-validating them with multiple data sets, we have demonstrated the usefulness of this approach.6. Prospect: What are the potential applications of these machine learning methods in biological science?
Future studies comparing the accuracy of machine learning models and traditional statistical methods for predicting hypoglycemia may be useful.Further testing should be conducted to confirm that the models can be deployed in real time and could extend beyond predicting hypoglycemia to also deliver an intervention message, which could further reduce the occurrence of this serious diabetes complication.7. Mine Question(Optional)
kNN和navie bayes的结果很差，为什么还要选这两种方法展示在结果中？ 选择算法的三条原则中没有太关注这篇文章想解决的问题特征。