Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction

Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While recent advancement in ADRD risk prediction have primarily relied on imaging analysis, yet not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative method to evaluate relationship importance and its influence on ADRD risk prediction, ensuring comprehensive interpretation. We employed Variationally Regularized Encoder-decoder Graph Neural Network (VGNN) for estimating ADRD likelihood. We created three scenarios to assess the model's efficiency, using Random Forest and Light Gradient Boost Machine as baselines. We further used our relation importance method to clarify the key relationships for ADRD risk prediction. VGNN surpassed other baseline models by 10% in the area under the receiver operating characteristic. The integration of the GNN model and relation importance interpretation could potentially play an essential role in providing valuable insight into factors that may contribute to or delay ADRD progression. Employing a GNN approach with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data

Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions

Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic

Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications

Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variety of applications, such as Visual Question Answering (VQA), Natural Language for Visual Reasoning (NLVR), and Vision Language Retrieval (VLR). Among these applications, cross-modal interaction and complementary information from different modalities are crucial for advanced models to perform any multimodal task, e.g., understand, recognize, retrieve, or generate optimally. Researchers have proposed diverse methods to address these tasks. The different variants of transformer-based architectures performed extraordinarily on multiple modalities. This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks. This study summarizes the (i) recent task-specific deep learning methodologies, (ii) the pretraining types and multimodal pretraining objectives, (iii) from state-of-the-art pretrained multimodal approaches to unifying architectures, and (iv) multimodal task categories and possible future improvements that can be devised for better multimodal learning. Moreover, we prepare a dataset section for new researchers that covers most of the benchmarks for pretraining and finetuning. Finally, major challenges, gaps, and potential research topics are explored. A constantly-updated paperlist related to our survey is maintained at https://github.com/marslanm/multimodality-representation-learning

Representation learning for electronic health records

Vse večji in kompleksnejši nabori podatkov, zbrani v klinični praksi, zahtevajo razvoj učinkovitih metod, namenjenih odkrivanju koristnega znanja. V delu proučimo uspešnost štirih skupin modelov naraščajočih kompleksnosti za ekstrakcijo in uporabo koristnega znanja iz elektronskih zdravstvenih kartotek. Učenje izvedemo z uporabo podatkov v obliki nestrukturiranega kliničnega teksta, z uporabo rezultata algoritma Wordification, s katerim podatke v relacijski podatkovni zbirki predstavimo v obliki dokumentov, in s hkratno uporabo obeh tipov podatkov. Modele evalviramo na nalogah napovedovanja pomembnih kliničnih dogodkov z uporabo referenčne zbirke elektronskih zdravstvenih kartotek MIMIC-III. Najprej evalviramo modele na osnovi klasifikacije agregiranih vložitev besed in dokumentov, ki služijo kot izhodišče za vrednotenje kompleksnejših modelov. Nadaljujemo z evalvacijo modela na osnovi konvolucijskih nevronskih mrež in modela na osnovi arhitekture BERT. Na koncu evalviramo ansamble najuspešnejših modelov prejšnjih skupin, ki agregirajo znanje vsebovano v kliničnem tekstu in rezultatih algoritma Wordification. Rezultati nakazujejo, da lahko z uporabo rezultatov algoritma Wordification naučimo modele, ki so konkurenčni različicam, naučenim z bolje raziskano uporabo kliničnega teksta. Ansambelske modele, ki hkrati izrabljajo oba tipa podatkov, na podlagi uporabljenih metrik ovrednotimo kot najuspešnejše.The growing size and complexity of data collected in clinical practice necessitate the development of efficient methods for discovering the knowledge they contain. We examine the performance of four groups of models of increasing complexity for extracting and utilizing useful knowledge from electronic health records. The models were trained using unstructured clinical text, the relational dataset converted into a document-like form with Wordification, and using both types of data simultaneously. We evaluate the models on the task of predicting important clinical events using the reference MIMIC-III collection of electronic health records. We start by evaluating the models based document classification and aggregated word embeddings. The results serve as the baseline for evaluating models of higher complexity. We next evaluate a model based on convolutional neural networks and a model based on the BERT architecture. Finally, we evaluate ensembles of best-performing models from the previous groups that aggregate the knowledge extracted from clinical text and results of Wordification. The results suggest that models trained using the results of Wordification can compete with models trained using the better-studied approach of utilizing clinical text. Ensemble models that simultaneously exploit both data types are the best performers based on the metrics used