OpenRaiser/Intern-Atlas huggingface dataset:
Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization
Authors: Joyce C. Ho, Joydeep Ghosh, Jimeng Sun
Venue: Conference KDD 2014
Year: 2014

The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to phenotypes, or medical concepts, that clinical researchers need or use. Existing phenotyping approaches typically require labor intensive supervision from medical experts. We propose Marble, a novel sparse non-negative tensor factorization method to derive phenotype candidates with virtually no human supervision. Marble decomposes the observed tensor into two terms, a bias tensor and an interaction tensor. The bias tensor represents the baseline characteristics common amongst the overall population and the interaction tensor defines the phenotypes. We demonstrate the capability of our proposed model on both simulated and patient data from a publicly available clinical database. Our results show that Marble derived phenotypes provide at least a 42.8% reduction in the number of non-zero element and also retains predictive power for classification purposes. Furthermore, the resulting phenotypes and baseline characteristics from real EHR data are consistent with known characteristics of the patient population. Thus it can potentially be used to rapidly characterize, predict, and manage a large number of diseases, thereby promising a novel, data-driven solution that can benefit very large segments of the population.
HiTANet: Hierarchical Time-Aware Attention Networks for Risk Prediction on Electronic Health Records
Authors: Junyu Luo 0001, Muchao Ye, Cao Xiao, Fenglong Ma
Venue: Conference KDD 2020
Year: 2020

Deep learning methods especially recurrent neural network based models have demonstrated early success in disease risk prediction on longitudinal patient data. Existing works follow a strong assumption to implicitly assume the stationary disease progression during each time period, and thus, take a homogeneous way to decay the information from previous time steps for all patients. However,in reality, disease progression is non-stationary. Besides, the key time steps for a target disease vary among patients. To leverage time information for risk prediction in a more reasonable way, we propose a new hierarchical time-aware attention network, named HiTANet, which imitates the decision making process of doctors inrisk prediction. Particularly, HiTANet models time information in local and global stages. The local evaluation stage has a time aware Transformer that embeds time information into visit-level embed-ding and generates local attention weight for each visit. The global synthesis stage further adopts a time-aware key-query attention mechanism to assign global weights to different time steps. Finally, the two types of attention weights are dynamically combined to generate the patient representations for further risk prediction. We evaluate HiTANet on three real-world datasets. Compared with the best results among twelve competing baselines, HiTANet achieves over 7% in terms of F1 score on all datasets, which demonstrates the effectiveness of the proposed model and the necessity of modeling time information in risk prediction task.
Kame: Knowledge-based attention model for diagnosis prediction in healthcare
Authors: Fenglong Ma, Quanzeng You, Houping Xiao, Radha Chitta, Jing Zhou, Jing Gao
Venue: Conference CIKM 2018
Year: 2018

The goal of diagnosis prediction task is to predict the future health information of patients from their historical Electronic Healthcare Records (EHR). The most important and challenging problem of diagnosis prediction is to design an accurate, robust and interpretable predictive model. Existing work solves this problem by employing recurrent neural networks (RNNs) with attention mechanisms, but these approaches suffer from the data sufficiency problem. To obtain good performance with insufficient data, graph-based attention models are proposed. However, when the training data are sufficient, they do not offer any improvement in performance compared with ordinary attention-based models. To address these issues, we propose KAME, an end-to-end, accurate and robust model for predicting patients' future health information. KAME not only learns reasonable embeddings for nodes in the knowledge graph, but also exploits general knowledge to improve the prediction accuracy with the proposed knowledge attention mechanism. With the learned attention weights, KAME allows us to interpret the importance of each piece of knowledge in the graph. Experimental results on three real world datasets show that the proposed KAME significantly improves the prediction performance compared with the state-of-the-art approaches, guarantees the robustness with both sufficient and insufficient data, and learns interpretable disease representations.
Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis
Authors: Liantao Ma, Xinyu Ma, Junyi Gao, Xianfeng Jiao, Zhihao Yu, Chaohe Zhang, Wenjie Ruan, Yasha Wang, Wen Tang, Jiangtao Wang
Venue: Conference WWW 2021
Year: 2021

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from life-threatening systemic problems and need to be carefully monitored in ICUs. An intelligent prognosis can help physicians take an early intervention, prevent adverse outcomes, and optimize the medical resource allocation, which is urgently needed, especially in this ongoing global pandemic crisis. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, the rarity of the cases, and privacy concerns. In this paper, we propose a distilled transfer learning framework, which leverages the existing publicly available online Electronic Medical Records to enhance the prognosis for inpatients with emerging infectious diseases. It learns to embed the COVID-19-related medical features based on massive existing EMR data. The transferred parameters are further trained to imitate the teacher model’s representation based on distillation, which embeds the health status more comprehensively on the source dataset. We conduct Length-of-Stay prediction experiments for patients in ICUs on real-world COVID-19 datasets. The experiment results indicate that our proposed model consistently outperforms competitive baseline methods. In order to further verify the scalability of o deal with different clinical tasks on different EMR datasets, we conduct an additional mortality prediction experiment on End-Stage Renal Disease datasets. The extensive experiments demonstrate that an benefit the prognosis for emerging pandemics and other diseases with limited EMR.
Metacare++: Meta-learning with hierarchical subtyping for cold-start diagnosis prediction in healthcare data
Authors: Yanchao Tan, Carl Yang, Xiangyu Wei, Chaochao Chen, Weiming Liu, Longfei Li, Jun Zhou, Xiaolin Zheng
Venue: Conference SIGIR 2022
Year: 2022

Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a few visits per patient and a few observations per disease can be exploited. Although meta-learning is widely adopted to address the data sparsity problem in general domains, directly applying it to healthcare data is less effective, since it is unclear how to capture both the temporal relations in clinical visits and the complicated relations among syndromic diseases for precise personalized diagnosis. To this end, we first propose a novel Meta-learning framework for cold-start diagnosis prediction in healthCare data (MetaCare). By explicitly encoding the effects of disease progress over time as a generalization prior, MetaCare dynamically predicts future diagnosis and timestamp for infrequent patients. Then, to model complicated relations among rare diseases, we propose to utilize domain knowledge of hierarchical relations among diseases, and further perform diagnosis subtyping to mine the latent syndromic relations among diseases. Finally, to tailor the generic meta-learning framework with personalized parameters, we design a hierarchical patient subtyping mechanism and bridge the modeling of both infrequent patients and rare diseases. We term the joint model as MetaCare++. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by MetaCare++, yielding average improvements of 7.71% for diagnosis prediction and 13.94% for diagnosis time prediction over the state-of-the-art baselines.
Lsan: Modeling long-term dependencies and short-term correlations with hierarchical attention for risk prediction
Authors: Muchao Ye, Junyu Luo, Cao Xiao, Fenglong Ma
Venue: Conference CIKM 2020
Year: 2020

Risk prediction using electronic health records (EHR) is a challenging data mining task due to the two-level hierarchical structure of EHR data. EHR data consist of a set of time-ordered visits, and within each visit, there is a set of unordered diagnosis codes. Existing approaches focus on modeling temporal visits with deep neural network (DNN) techniques. However, they ignore the importance of modeling diagnosis codes within visits, and a lot of task-unrelated information within visits usually leads to unsatisfactory performance of existing approaches. To minimize the effect caused by noise information of EHR data, in this paper, we propose a novel DNN for risk prediction termed as LSAN, which consists of a Hierarchical Attention Module (HAM) and a Temporal Aggregation Module (TAM). Particularly, LSAN applies HAM to model the hierarchical structure of EHR data. Using the attention mechanism in the hierarchy of diagnosis code, HAM is able to retain diagnosis details and assign flexible attention weights to different diagnosis codes by their relevance to corresponding diseases. Moreover, the attention mechanism in the hierarchy of visit learns a comprehensive feature throughout the visit history by paying greater attention to visits with higher relevance. Based on the foundation laying by HAM, TAM uses a two-pathway structure to learn a robust temporal aggregation mechanism among all visits for LSAN. It extracts long-term dependencies by a Transformer encoder and short-term correlations by a parallel convolutional layer among different visits. With the construction of HAM and TAM, LSAN achieves the state-of-the-art performance on three real-world datasets with larger AUCs, recalls and F1 scores. Furthermore, the model analysis results demonstrate the effectiveness of the network construction with good interpretability and robustness of decision making by LSAN.
Medpath: Augmenting health risk prediction via medical knowledge paths
Authors: Muchao Ye, Suhan Cui, Yaqing Wang, Junyu Luo, Cao Xiao, Fenglong Ma
Venue: Conference WWW 2021
Year: 2021

The broad adoption of electronic health records (EHR) data and the availability of biomedical knowledge graphs (KGs) on the web have provided clinicians and researchers unprecedented resources and opportunities for conducting health risk predictions to improve healthcare quality and medical resource allocation. Existing methods have focused on improving the EHR feature representations using attention mechanisms, time-aware models, or external knowledge. However, they ignore the importance of using personalized information to make predictions. Besides, the reliability of their prediction interpretations needs to be improved since their interpretable attention scores are not explicitly reasoned from disease progression paths. In this paper, we propose MedPath to solve these challenges and augment existing risk prediction models with the ability to use personalized information and provide reliable interpretations inferring from disease progression paths. Firstly, MedPath extracts personalized knowledge graphs (PKGs) containing all possible disease progression paths from observed symptoms to target diseases from a large-scale online medical knowledge graph. Next, to augment existing EHR encoders for achieving better predictions, MedPath learns a PKG embedding by conducting multi-hop message passing from symptom nodes to target disease nodes through a graph neural network encoder. Since MedPath reasons disease progression by paths existing in PKGs, it can provide explicit explanations for the prediction by pointing out how observed symptoms can finally lead to target diseases. Experimental results on three real-world medical datasets show that MedPath is effective in improving the performance of eight state-of-the-art methods with higher F1 scores and AUCs. Our case study also demonstrates that MedPath can greatly improve the explicitness of the risk prediction interpretation.1
Service time prediction for delivery tasks via spatial meta-learning
Authors: Sijie Ruan, Cheng Long 0001, Zhipeng Ma, Jie Bao 0003, Tianfu He, Ruiyuan Li, Yiheng Chen, Shengnan Wu, Yu Zheng 0004
Venue: Conference KDD 2022
Year: 2022

Service time is a part of time cost in the last-mile delivery, which is the time spent on delivering parcels at a certain location. Predicting the service time is fundamental for many downstream logistics applications, e.g., route planning with time windows, courier workload balancing and delivery time prediction. Nevertheless, it is non-trivial given the complex delivery circumstances, location heterogeneity, and skewed observations in space. The existing solution trains a supervised model based on aggregated features extracted from parcels to deliver, which cannot handle above challenges well. In this paper, we propose MetaSTP, a meta-learning based neural network model to predict the service time. MetaSTP treats the service time prediction at each location as a learning task, leverages a Transformer-based representation layer to encode the complex delivery circumstances, and devises a model-based meta-learning method enhanced by location prior knowledge to reserve the uniqueness of each location and handle the imbalanced distribution issue. Experiments show MetaSTP outperforms baselines by at least 9.5% and 7.6% on two real-world datasets. Finally, an intelligent waybill assignment system based on MetaSTP is deployed and used internally in JD Logistics.
Casmos: A framework for learning candidate selection models over structured queries and documents
Authors: Fedor Borisyuk, Krishnaram Kenthapadi, David Stein, Bo Zhao
Venue: Conference KDD 2016
Year: 2016

User experience at social media and web platforms such as LinkedIn is heavily dependent on the performance and scalability of its products. Applications such as personalized search and recommendations require real-time scoring of millions of structured candidate documents associated with each query, with strict latency constraints. In such applications, the query incorporates the context of the user (in addition to search keywords if present), and hence can become very large, comprising of thousands of Boolean clauses over hundreds of document attributes. Consequently, candidate selection techniques need to be applied since it is infeasible to retrieve and score all matching documents from the underlying inverted index. We propose CaSMoS, a machine learned candidate selection framework that makes use of Weighted AND (WAND) query. Our framework is designed to prune irrelevant documents and retrieve documents that are likely to be part of the top-k results for the query. We apply a constrained feature selection algorithm to learn positive weights for feature combinations that are used as part of the weighted candidate selection query. We have implemented and deployed this system to be executed in real time using LinkedIn's Galene search platform. We perform extensive evaluation with different training data approaches and parameter settings, and investigate the scalability of the proposed candidate selection model. Our deployment of this system as part of LinkedIn's job recommendation engine has resulted in significant reduction in latency (up to 25%) without sacrificing the quality of the retrieved results, thereby paving the way for more sophisticated scoring models.
Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learned
Authors: Sahin Cem Geyik, Qi Guo, Bo Hu, Cagri Ozcaglar, Ketan Thakkar, Xianren Wu, Krishnaram Kenthapadi
Venue: Conference SIGIR 2018
Year: 2018

LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities. LinkedIn's job ecosystem has been designed as a platform to connect job providers and job seekers, and to serve as a marketplace for efficient matching between potential candidates and job openings. A key mechanism to help achieve these goals is the LinkedIn Recruiter product, which enables recruiters to search for relevant candidates and obtain candidate recommendations for their job postings. In this work, we highlight a set of unique information retrieval, system, and modeling challenges associated with talent search and recommendation systems.
How to get them a dream job?: Entity-aware features for personalized job search ranking
Authors: Jia Li, Dhruv Arya, Viet Ha-Thuc, Shakti Sinha
Venue: Conference KDD 2016
Year: 2016

This paper proposes an approach to applying standardized entity data to improve job search quality and to make search results more personalized. Specifically, we explore three types of entity-aware features and incorporate them into the job search ranking function. The first is query-job matching features which extract and standardize entities mentioned in queries and documents, then semantically match them based on these entities. The second type, searcher-job expertise homophily, aims to capture the fact that job searchers tend to be interested in the jobs requiring similar expertise as theirs. To measure the similarity, we use standardized skills in job descriptions and searchers' profiles as well as skills that we infer searchers might have but not explicitly list in their profiles. Third, we propose a concept of entity-faceted historical click-through-rates (CTRs) to capture job document quality. Faceting jobs by their standardized companies, titles, locations, etc., and computing historical CTRs at the facet level instead of individual job level alleviate sparseness issue in historical action data. This is particularly important in job search where job lifetime is typically short. Both offline and online experiments confirm the effectiveness of the features. In offline experiment, using the entity-aware features gives improvements of +20%, +12.1% and +8.3% on [email protected], MRR and [email protected], respectively. Online A/B test shows that a new model with these features is +11.3% and +5.3% better than the baseline in terms of click-through-rate and apply rate.
Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks
Authors: Dingyi Zhuang, Shenhao Wang, Haris N. Koutsopoulos, Jinhua Zhao 0001
Venue: Conference KDD 2022
Year: 2022

Origin-Destination (O-D) travel demand prediction is a fundamental challenge in transportation. Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy. However, few studies tackled the uncertainty and sparsity issues in fine-grained O-D matrices. This presents a serious problem, because a vast number of zeros deviate from the Gaussian assumption underlying the deterministic deep learning models. To address this issue, we design a Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) to quantify the uncertainty of the sparse travel demand. It analyzes spatial and temporal correlations using diffusion and temporal convolution networks, which are then fused to parameterize the probabilistic distributions of travel demand. The STZINB-GNN is examined using two real-world datasets with various spatial and temporal resolutions. The results demonstrate the superiority of STZINB-GNN over benchmark models, especially under high spatial-temporal resolutions, because of its high accuracy, tight confidence intervals, and interpretable parameters. The sparsity parameter of the STZINB-GNN has physical interpretation for various transportation applications.
Anomaly subgraph detection with feature transfer
Authors: Ying Sun, Wenjun Wang, Nannan Wu, Wei Yu, Xue Chen
Venue: Conference CIKM 2020
Year: 2020

Anomaly detection in multilayer graphs becomes more critical in many application scenarios, i.e., identifying crime hotspots in urban areas by discovering suspicious and illicit behaviors in social networks. However, it is a big challenge to identify anomalies in a layer graph due to the insufficient anomaly features. Most existing methods of anomaly detection determine whether a node is abnormal by looking at the observable anomalous feature values. However, these methods are not suitable for scenarios in which the abnormal features are scarce, e.g., geometric graphs or non-public data in social network services. In this paper, to detect anomaly in a graph with insufficient anomalous features, we propose a pioneering approach ASD-FT (Anomaly Subgraph Detection with Feature Transfer) based on a strategy of anomalous feature transfers between different layers of a multilayer graph. The proposed ASD-FT detects anomaly subgraphs from the graph of the target layer by analyzing the anomalous features in the graph of another layer. We demonstrate the effectiveness and robustness of our approach ASD-FT with extensive experiments on five real-world datasets.
Towards a multi-view attentive matching for personalized expert finding
Authors: Qiyao Peng, Hongtao Liu, Yinghui Wang, Hongyan Xu, Pengfei Jiao, Minglai Shao, Wenjun Wang
Venue: Conference WWW 2022
Year: 2022

In Community Question Answering (CQA) websites, expert finding aims at seeking suitable experts to answer questions. The key is to explore the inherent relevance based on the representations of questions and experts. Existing methods usually learn these features from single view information (e.g., question title), which would be not insufficient to fully learn their representations. In this paper, we propose a personalized expert finding method with a multi-view attentive matching mechanism. We design three modules under the multi-view paradigm, including a question encoder, an intra-view encoder, and an inter-view encoder, which aims to comprehend the comprehensive relationships between experts and questions. In the question encoder, we learn the multi-view question features from its title, body and tag views respectively. In the intra-view encoder, we design an interactive attention network to capture the view-specific relevance between the target question and the historical answered questions of experts for all different views. Furthermore, in the inter-view encoder we employ a personalized attention network to aggregate different view information to learn expert/question representations. In this way, the match of the expert and question could be fully captured from the multi-view information via the intra- and inter-view mechanisms. Experimental results on six datasets demonstrate that the proposed method could achieve better performance than existing state-of-the-art methods.