Interactive exploration of population scale pharmacoepidemiology datasets

Population-scale drug prescription data linked with adverse drug reaction (ADR) data supports the fitting of models large enough to detect drug use and ADR patterns that are not detectable using traditional methods on smaller datasets. However, detecting ADR patterns in large datasets requires tools for scalable data processing, machine learning for data analysis, and interactive visualization. To our knowledge no existing pharmacoepidemiology tool supports all three requirements. We have therefore created a tool for interactive exploration of patterns in prescription datasets with millions of samples. We use Spark to preprocess the data for machine learning and for analyses using SQL queries. We have implemented models in Keras and the scikit-learn framework. The model results are visualized and interpreted using live Python coding in Jupyter. We apply our tool to explore a 384 million prescription data set from the Norwegian Prescription Database combined with a 62 million prescriptions for elders that were hospitalized. We preprocess the data in two minutes, train models in seconds, and plot the results in milliseconds. Our results show the power of combining computational power, short computation times, and ease of use for analysis of population scale pharmacoepidemiology datasets. The code is open source and available at: https://github.com/uit-hdl/norpd_prescription_analyse

Adverse Drug Reaction Classification With Deep Neural Networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs

PRACTICAL IMPLICATIONS OF SPONTANEOUS ADVERSE DRUG REACTION REPORTING SYSTEM IN HOSPITALS-AN OVERVIEW

Adverse drug reactions (ADRs) are global problems of major concern which leads to morbidity and mortality. It causes 30 of hospitalized patients and lead 2-6 of all medical admissions. Spontaneous reporting of ADRs is the cornerstone of pharmacovigilance and is essential for maintaining patient safety. The necessity of a spontaneous ADR surveillance system is addressed by many authorities like World Health Organization, Food and Drug Administration, Joint Commission International and Uppsala monitoring center. However, existing postmarketing surveillance systems massively rely on spontaneous reports of ADRs which suffer from serious underreporting, latency, and inconsistent reporting. Studies estimated that only 6–10 of all ADRs are reported in hospitals. It is a very low percentage to go in deep and analyze the reason for the same and to resolve that underlying factors. Researchers proved that knowledge, attitude and false perceptions about the ADRs are the major challenges in the spontaneous reporting of ADRs. Which includes personal, professional, system related and organization related conflicts. Majority of them can improve by doing the system and personal targeted implications. Identifying, analyzing and working on these issues can improve the ADR surveillance system in hospitals to attain the patient safety. Understanding the pharmacovigilance, identifying and sorting out the obstacles of spontaneous reporting through an efficient pharmacovigilance department, continuous educational interventions, patient centered surveillance programs, health care team work efforts towards the detection of ADRs and implementation of the computer or personal assisted ADR trigger tool programs can furnish out a successful pharmacovigilance system in the hospitals and thereby we can constitute a good quality health care system.Key words: Spontaneous reporting system, adverse drug reaction, pharmacovigilance, Patient safet

딥 뉴럴 네트워크를 활용한 의학 개념 및 환자 표현 학습과 의료 문제에의 응용

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 정교민.본 학위 논문은 전국민 의료 보험데이터인 표본코호트DB를 활용하여 딥 뉴럴 네트워크 기반의 의학 개념 및 환자 표현 학습 방법과 의료 문제 해결 방법을 제안한다. 먼저 순차적인 환자 의료 기록과 개인 프로파일 정보를 기반으로 환자 표현을 학습하고 향후 질병 진단 가능성을 예측하는 재귀신경망 모델을 제안하였다. 우리는 다양한 성격의 환자 정보를 효율적으로 혼합하는 구조를 도입하여 큰 성능 향상을 얻었다. 또한 환자의 의료 기록을 이루는 의료 코드들을 분산 표현으로 나타내 추가 성능 개선을 이루었다. 이를 통해 의료 코드의 분산 표현이 중요한 시간적 정보를 담고 있음을 확인하였고, 이어지는 연구에서는 이러한 시간적 정보가 강화될 수 있도록 그래프 구조를 도입하였다. 우리는 의료 코드의 분산 표현 간의 유사도와 통계적 정보를 가지고 그래프를 구축하였고 그래프 뉴럴 네트워크를 활용, 시간/통계적 정보가 강화된 의료 코드의 표현 벡터를 얻었다. 획득한 의료 코드 벡터를 통해 시판 약물의 잠재적인 부작용 신호를 탐지하는 모델을 제안한 결과, 기존의 부작용 데이터베이스에 존재하지 않는 사례까지도 예측할 수 있음을 보였다. 마지막으로 분량에 비해 주요 정보가 희소하다는 의료 기록의 한계를 극복하기 위해 지식그래프를 활용하여 사전 의학 지식을 보강하였다. 이때 환자의 의료 기록을 구성하는 지식그래프의 부분만을 추출하여 개인화된 지식그래프를 만들고 그래프 뉴럴 네트워크를 통해 그래프의 표현 벡터를 획득하였다. 최종적으로 순차적인 의료 기록을 함축한 환자 표현과 더불어 개인화된 의학 지식을 함축한 표현을 함께 사용하여 향후 질병 및 진단 예측 문제에 활용하였다.This dissertation proposes a deep neural network-based medical concept and patient representation learning methods using medical claims data to solve two healthcare tasks, i.e., clinical outcome prediction and post-marketing adverse drug reaction (ADR) signal detection. First, we propose SAF-RNN, a Recurrent Neural Network (RNN)-based model that learns a deep patient representation based on the clinical sequences and patient characteristics. Our proposed model fuses different types of patient records using feature-based gating and self-attention. We demonstrate that high-level associations between two heterogeneous records are effectively extracted by our model, thus achieving state-of-the-art performances for predicting the risk probability of cardiovascular disease. Secondly, based on the observation that the distributed medical code embeddings represent temporal proximity between the medical codes, we introduce a graph structure to enhance the code embeddings with such temporal information. We construct a graph using the distributed code embeddings and the statistical information from the claims data. We then propose the Graph Neural Network(GNN)-based representation learning for post-marketing ADR detection. Our model shows competitive performances and provides valid ADR candidates. Finally, rather than using patient records alone, we utilize a knowledge graph to augment the patient representation with prior medical knowledge. Using SAF-RNN and GNN, the deep patient representation is learned from the clinical sequences and the personalized medical knowledge. It is then used to predict clinical outcomes, i.e., next diagnosis prediction and CVD risk prediction, resulting in state-of-the-art performances.1 Introduction 1 2 Background 8 2.1 Medical Concept Embedding 8 2.2 Encoding Sequential Information in Clinical Records 11 3 Deep Patient Representation with Heterogeneous Information 14 3.1 Related Work 16 3.2 Problem Statement 19 3.3 Method 20 3.3.1 RNN-based Disease Prediction Model 20 3.3.2 Self-Attentive Fusion (SAF) Encoder 23 3.4 Dataset and Experimental Setup 24 3.4.1 Dataset 24 3.4.2 Experimental Design 26 ii 3.4.3 Implementation Details 27 3.5 Experimental Results 28 3.5.1 Evaluation of CVD Prediction 28 3.5.2 Sensitivity Analysis 28 3.5.3 Ablation Studies 31 3.6 Further Investigation 32 3.6.1 Case Study: Patient-Centered Analysis 32 3.6.2 Data-Driven CVD Risk Factors 32 3.7 Conclusion 33 4 Graph-Enhanced Medical Concept Embedding 40 4.1 Related Work 42 4.2 Problem Statement 43 4.3 Method 44 4.3.1 Code Embedding Learning with Skip-gram Model 44 4.3.2 Drug-disease Graph Construction 45 4.3.3 A GNN-based Method for Learning Graph Structure 47 4.4 Dataset and Experimental Setup 49 4.4.1 Dataset 49 4.4.2 Experimental Design 50 4.4.3 Implementation Details 52 4.5 Experimental Results 53 4.5.1 Evaluation of ADR Detection 53 4.5.2 Newly-Described ADR Candidates 54 4.6 Conclusion 55 5 Knowledge-Augmented Deep Patient Representation 57 5.1 Related Work 60 5.1.1 Incorporating Prior Medical Knowledge for Clinical Outcome Prediction 60 5.1.2 Inductive KGC based on Subgraph Learning 61 5.2 Method 61 5.2.1 Extracting Personalized KG 61 5.2.2 KA-SAF: Knowledge-Augmented Self-Attentive Fusion Encoder 64 5.2.3 KGC as a Pre-training Task 68 5.2.4 Subgraph Infomax: SGI 69 5.3 Dataset and Experimental Setup 72 5.3.1 Clinical Outcome Prediction 72 5.3.2 Next Diagnosis Prediction 72 5.4 Experimental Results 73 5.4.1 Cardiovascular Disease Prediction 73 5.4.2 Next Diagnosis Prediction 73 5.4.3 KGC on SemMed KG 73 5.5 Conclusion 74 6 Conclusion 77 Abstract (In Korean) 90 Acknowlegement 92박

Pharmacovigilance in the European Union: Practical Implementation across Member States

Comparative Politics; Political Economy; European Union Politics; Drug Safety and Pharmacovigilanc

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Abstract Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials