Injecting Domain Knowledge in Electronic Medical Records to Improve Hospitalization Prediction

International audienceElectronic medical records (EMR) contain key information about the different symptomatic episodes that a patient went through. They carry a great potential in order to improve the well-being of patients and therefore represent a very valuable input for artificial intelligence approaches. However, the explicit knowledge directly available through these records remains limited, the extracted features to be used by machine learning algorithms do not contain all the implicit knowledge of medical expert. In order to evaluate the impact of domain knowledge when processing EMRs, we augment the features extracted from EMRs with ontological resources before turning them into vectors used by machine learning algorithms. We evaluate these augmentations with several machine learning algorithms to predict hospitalization. Our approach was experimented on data from the PRIMEGE PACA database that contains more than 350,000 consultations carried out by 16 general practitioners (GPs)

Injection of Automatically Selected DBpedia Subjects in Electronic Medical Records to boost Hospitalization Prediction

International audienceAlthough there are many medical standard vocabularies available, it remains challenging to properly identify domain concepts in electronic medical records. Variations in the annotations of these texts in terms of coverage and abstraction may be due to the chosen annotation methods and the knowledge graphs, and may lead to very different performances in the automated processing of these annotations. We propose a semi-supervised approach based on DBpedia to extract medical subjects from EMRs and evaluate the impact of augmenting the features used to represent EMRs with these subjects in the task of predicting hospitalization. We compare the impact of subjects selected by experts vs. by machine learning methods through feature selection. Our approach was experimented on data from the database PRIMEGE PACA that contains more than 600,000 consultations carried out by 17 general practitioners (GPs)

Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data size. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pre-training of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. We propose Med-BERT, which adapts the BERT framework for pre-training contextualized embedding models on structured diagnosis data from 28,490,650 patients EHR dataset. Fine-tuning experiments are conducted on two disease-prediction tasks: (1) prediction of heart failure in patients with diabetes and (2) prediction of pancreatic cancer from two clinical databases. Med-BERT substantially improves prediction accuracy, boosting the area under receiver operating characteristics curve (AUC) by 2.02-7.12%. In particular, pre-trained Med-BERT substantially improves the performance of tasks with very small fine-tuning training sets (300-500 samples) boosting the AUC by more than 20% or equivalent to the AUC of 10 times larger training set. We believe that Med-BERT will benefit disease-prediction studies with small local training datasets, reduce data collection expenses, and accelerate the pace of artificial intelligence aided healthcare.Comment: L.R., X.Y., and Z.X. share first authorship of this wor

Access to Opioids in Palliative Care in Low-and Middle-Income Countries : The Case of Burkina-Faso -How Can Blockchain and Internet of Things Assist? –

Background: People requesting palliative care or suffering from pain are subjected to adhere to opioid-based treatment in order to alleviate their pain. Commonly, access to opioids is strictly controlled. Access to Healthcare delivery services remains challenging in Low-and Middle-Income Countries (LMIC). In Burkina-Faso (BF), a Sub-Saharan African (SSA) country, patients requiring palliative care (PC) are especially facing poor access to pain drugs such as morphine. Facing poor access to pain-alleviating medicine can severely impact the daily quality of life (QoL). On one hand, patients are experiencing poor opioids access. On another hand opioids abuse, leading to drug addiction is noticed. The question arising here is how can they face poor access and at the same time abuse the given drug? One plausible answer is the counterfeit chain, which provides illegal drugs. Beyond the counterfeit issues faced, the public health care system is also facing, amongst others, prescription falsification, fraud in the distribution, and stock shortage. Method & Design:  Mixed-Method-Design was applied to this study. National stipulations, regulations, and the state-of-the-art in the field of palliative care in BF were investigated and analyzed. Based on the investigation‘s outcomes and following the paradigm of design science research, and information system based improvement solution is proposed to tackle the poor access to opioids, improve the supply and distribution chain as well as to efficiently monitor the consumption of opioids in BF, and prevent patients from any health issues, drug addiction, and death. Objectives: The main objectives are to fight against opioid addiction, counterfeits, a stock shortage, and prevent related health safety issues. The main aim is to enable the traceability of any opioids prescription, secure the supply and distribution, and thus early detect any fraud in the system. This editorial paper would, therefore, focus on investigating the reasons underlying the poor access to opioids in palliative care in BF and make suggestions for improvement. A blockchain (BC) and the Internet of Things (IoT) based system to secure and improve opioids supply, distribution, and prescription will be proposed. Results: The contribution analysis reveals the potential of the proposed model to assist in many ways to improve access to opioids and to secure this access. The model could contribute to preventing drug abuse, overprescription, supporting off-label-use of opioids and thus providing a knowledge database for off-label use of opioids. This model shows promise to deliver accurate data and information about the exact opioid’s needs and consumption atlas. This will assist to better distribute the product in the entire country. A proof-of-concept of the proposed model is required. This is ongoing and will be presented in a forthcoming paper. Conclusion: This editorial paper investigates access to opioids in Burkina Faso. It pointed out by analyzing out the computer science perspectives the different causes of the crisis. A contextualized model is provided. A test in situ needs to be performed

GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs

Clinical predictive models often rely on patients' electronic health records (EHR), but integrating medical knowledge to enhance predictions and decision-making is challenging. This is because personalized predictions require personalized knowledge graphs (KGs), which are difficult to generate from patient EHR data. To address this, we propose \textsc{GraphCare}, an open-world framework that uses external KGs to improve EHR-based predictions. Our method extracts knowledge from large language models (LLMs) and external biomedical KGs to build patient-specific KGs, which are then used to train our proposed Bi-attention AugmenTed (BAT) graph neural network (GNN) for healthcare predictions. On two public datasets, MIMIC-III and MIMIC-IV, \textsc{GraphCare} surpasses baselines in four vital healthcare prediction tasks: mortality, readmission, length of stay (LOS), and drug recommendation. On MIMIC-III, it boosts AUROC by 17.6\% and 6.6\% for mortality and readmission, and F1-score by 7.9\% and 10.8\% for LOS and drug recommendation, respectively. Notably, \textsc{GraphCare} demonstrates a substantial edge in scenarios with limited data availability. Our findings highlight the potential of using external KGs in healthcare prediction tasks and demonstrate the promise of \textsc{GraphCare} in generating personalized KGs for promoting personalized medicine.Comment: ICLR 202

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

Using machine learning methods to improve healthcare delivery in diabetes management

This dissertation includes three studies, all focusing on Analytics and Patients information for improving diabetes management, namely educating patients and early detection of comorbidities. In these studies, we develop topic modeling and artificial neural network to acquire, preprocess, model, and predict to minimize the burden on diabetic patients and healthcare providers.The first essay explores the usage of Text Analytics, an unsupervised machine learning model, utilizing the vast data available on social media to improve diabetes education of the patients in managing the condition. Mainly we show the applicability of topic modeling to identify the gaps in diabetes education content and the information and knowledge needs of the patients. While traditional methods of the content decision were based on a group of experts' contributions, our proposed methodology considers the questions raised on social forums for support to extend the education content.The second essay implements Deep Neural Networks on EHR data to assist the clinicians in rank ordering the potential comorbidities that the specific patient may develop in the future. This essay helps prioritize regular screening for comorbidities and rationalize the screening process to improve adherence and effectiveness. Our model prediction helps identify diabetic retinopathy and nephropathy patients with very high precision compared to other traditional methods. Essays 1 and 2 focus on Data Analytics as a research tool for managing a chronic disease in the healthcare environment.The third essay goes through the challenges and best practices of data preprocessing for Analytics studies in healthcare. This study explores the standard preprocessing methodologies and their impact in the case of healthcare data analytics. Highlights the relevant modifications and adaptations to the standards CRISP_DM process. The suggestions are based on past research and the experience obtained in the projects discussed earlier in the thesis.Overall, the dissertation highlights the importance of data analytics in healthcare for better managing and diagnosing chronic diseases. It unfolds the economic value of implementing state-of-the-art IT methods in healthcare, where EHR & IT are predominantly costly and difficult to implement. The dissertation covers ANN and text mining implementation for diabetes management

Secondary use of Structured Electronic Health Records Data: From Observational Studies to Deep Learning-based Predictive Modeling

With the wide adoption of electronic health records (EHRs), researchers, as well as large healthcare organizations, governmental institutions, insurance, and pharmaceutical companies have been interested in leveraging this rich clinical data source to extract clinical evidence and develop predictive algorithms. Large vendors have been able to compile structured EHR data from sites all over the United States, de-identify these data, and make them available to data science researchers in a more usable format. For this dissertation, we leveraged one of the earliest and largest secondary EHR data sources and conducted three studies of increasing scope. In the first study, which was of limited scope, we conducted a retrospective observational study to compare the effect of three drugs on a specific population of approximately 3,000 patients. Using a novel statistical method, we found evidence that the selection of phenylephrine as the primary vasopressor to induce hypertension for the management of nontraumatic subarachnoid hemorrhage is associated with better outcomes as compared to selecting norepinephrine or dopamine. In the second study, we widened our scope, using a cohort of more than 100,000 patients to train generalizable models for the risk prediction of specific clinical events, such as heart failure in diabetes patients or pancreatic cancer. In this study, we found that recurrent neural network-based predictive models trained on expressive terminologies, which preserve a high level of granularity, are associated with better prediction performance as compared with other baseline methods, such as logistic regression. Finally, we widened our scope again, to train Med-BERT, a foundation model, on more than 20 million patients’ diagnosis data. Med-BERT was found to improve the prediction performance of downstream tasks that have a small sample size, which otherwise would limit the ability of the model to learn good representation. In conclusion, we found that we can extract useful information and train helpful deep learning-based predictive models. Given the limitations of secondary EHR data and taking into consideration that the data were originally collected for administrative and not research purposes, however, the findings need clinical validation. Therefore, clinical trials are warranted to further validate any new evidence extracted from such data sources before updating clinical practice guidelines. The implementability of the developed predictive models, which are in an early development phase, also warrants further evaluation