Towards Personalized Medicine: Leveraging Patient Similarity and Drug Similarity Analytics

By Ping Zhang, Fei Wang, Jianying Hu, Robert Sorrentino. 2014

119 citations recorded by the source corpus.

The rapid adoption of electronic health records (EHR) provides a comprehensive source for exploratory and predictive analytic to support clinical decision-making. In this paper, we investigate how to utilize EHR to tailor treatments to individual patients based on their likelihood to respond to a therapy. We construct a heterogeneous graph which includes two domains (patients and drugs) and encodes three relationships (patient similarity, drug similarity, and patient-drug prior associations). We describe a novel approach for performing a label propagation procedure to spread the label information representing the effectiveness of different drugs for different patients over this heterogeneous graph. The proposed method has been applied on a real-world EHR dataset to help identify personalized treatments for hypercholesterolemia. The experimental results demonstrate the effectiveness of the approach and suggest that the combination of appropriate patient similarity and drug similarity analytics could lead to actionable insights for personalized medicine. Particularly, by leveraging drug similarity in combination with patient similarity, our method could perform well even on new or rarely used drugs for which there are few records of known past performance.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Fei Wang
2. Wei Zhang
3. Xiaofeng He
4. Hongyuan Zha
5. Anita M. Preininger
6. Pranjul Yadav
7. Yu Cheng
8. Vipin Kumar

Citations in the corpus

• Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (2018, methodology): “Several patternbased methods generate recommendations by utilizing the similarity of patients [15, 33, 40], but they are challenging to directly learn the relation between patients and medications.”
• Risk Prediction with Electronic Health Records: A Deep Learning Approach (2016, background): “[5] proposed a similarity based approach for personalized treatment recommendation.”
• AI in Health: State of the Art, Challenges, and Future Directions (2019, methodology): “Recent years have seen an increase in research and initiatives related to AI in health, integrating different aspects of clinical data [110], linking biorepositories with clinical data [111-113], and forging connections between pharmaceutical research and development with clinical data [84].”
• Mining Electronic Health Records: A Survey (2017, background): “; Zhang et al. 2014a; ?”
• Personalized Prescription for Comorbidity (2018, methodology): “Prescription settled by pattern-based methods is to identify the treatments based on the similarities among records of patients [9,10,21].”