Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data

The paradigm of representation learning through transfer learning has the potential to greatly enhance clinical natural language processing. In this work, we propose a multi-task pre-training and fine-tuning approach for learning generalized and transferable patient representations from medical language. The model is first pre-trained with different but related high-prevalence phenotypes and further fine-tuned on downstream target tasks. Our main contribution focuses on the impact this technique can have on low-prevalence phenotypes, a challenging task due to the dearth of data. We validate the representation from pre-training, and fine-tune the multi-task pre-trained models on low-prevalence phenotypes including 38 circulatory diseases, 23 respiratory diseases, and 17 genitourinary diseases. We find multi-task pre-training increases learning efficiency and achieves consistently high performance across the majority of phenotypes. Most important, the multi-task pre-training is almost always either the best-performing model or performs tolerably close to the best-performing model, a property we refer to as robust. All these results lead us to conclude that this multi-task transfer learning architecture is a robust approach for developing generalized and transferable patient language representations for numerous phenotypes.Comment: Journal of Biomedical Informatics (in press

A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for relevant work published from 1 January 2012 to 02 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field.Comment: 15 pages, 3 figures, accepted for publication in the IEEE Transactions on Artificial Intelligenc

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

AdaCare:Explainable Clinical Health Status Representation Learning via Scale Adaptive Feature Extraction and Recalibration

Deep learning-based health status representation learning and clinical prediction have raised much research interest in recent years. Existing models have shown superior performance, but there are still several major issues that have not been fully taken into consideration. First, the historical variation pattern of the biomarker in diverse time scales plays an important role in indicating the health status, but it has not been explicitly extracted by existing works. Second, key factors that strongly indicate the health risk are different among patients. It is still challenging to adaptively make use of the features for patients in diverse conditions. Third, using the prediction model as a black box will limit the reliability in clinical practice. However, none of the existing works can provide satisfying interpretability and meanwhile achieve high prediction performance. In this work, we develop a general health status representation learning model, named AdaCare. It can capture the long and short-term variations of biomarkers as clinical features to depict the health status in multiple time scales. It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative in- interpretability. We conduct health risk prediction experiment on two real-world datasets. Experiment results indicate that AdaCare outperforms state-of-the-art approaches and provides effective interpretability which is verifiable by clinical experts