Aman Verma

2025-02-17

The International Journal of Behavioral Nutrition and Physical Activity (published)

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

Yuesong Zou

Ziyang Song

2022-10-25

Scientific Reports (published)

MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record

Yuri Ahuja

Yuesong Zou

Yue Li

2022-10-01

Journal of Biomedical Informatics (published)

Automatic Phenotyping by a Seed-guided Topic Model

Ziyang Song

Yuanyi Hu

Yue Li

Electronic health records (EHRs) provide rich clinical information and the opportunities to extract epidemiological patterns to understand a… (see more)nd predict patient disease risks with suitable machine learning methods such as topic models. However, existing topic models do not generate identifiable topics each predicting a unique phenotype. One promising direction is to use known phenotype concepts to guide topic inference. We present a seed-guided Bayesian topic model called MixEHR-Seed with 3 contributions: (1) for each phenotype, we infer a dual-form of topic distribution: a seed-topic distribution over a small set of key EHR codes and a regular topic distribution over the entire EHR vocabulary; (2) we model age-dependent disease progression as Markovian dynamic topic priors; (3) we infer seed-guided multi-modal topics over distinct EHR data types. For inference, we developed a variational inference algorithm. Using MixEHR-Seed, we inferred 1569 PheCode-guided phenotype topics from an EHR database in Quebec, Canada covering 1.3 million patients for up to 20-year follow-up with 122 million records for 8539 and 1126 unique diagnostic and drug codes, respectively. We observed (1) accurate phenotype prediction by the guided topics, (2) clinically relevant PheCode-guided disease topics, (3) meaningful age-dependent disease prevalence. Source code is available at GitHub: https://github.com/li-lab-mcgill/MixEHR-Seed.

2022-08-14

Knowledge Discovery and Data Mining (published)

Modeling electronic health record data using a knowledge-graph-embedded topic model

Yuesong Zou

The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic wa… (see more)y. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. KG-ETM distills latent disease topics from EHR data by learning the embedding from the medical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. KG-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations.

2022-06-03

ArXiv (preprint)

arxiv.org

Mortality trends and length of stays among hospitalized patients with COVID-19 in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves

Yiqing Xia

Huiting Ma

M. Brisson

Beate H Sander

A. Chan

Iris Ganser

Nadine Kronfli

Sharmistha Mishra

Mathieu Maheu-Giroux

2022-04-01

International Journal of Infectious Diseases (published)

Mortality trends and length of stays among hospitalized patients with COVID-19 in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves

Yiqing Xia

Huiting Ma

Marc Brisson

Beate Sander

Adrienne Chan

Iris Ganser

Nadine Kronfli

Sharmistha Mishra

Mathieu Maheu-Giroux

2022-04-01

International Journal of Infectious Diseases (published)

Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Quebec (Canada): a population-based cohort study of the first three epidemic waves

Yiqing Xia

Huiting Ma

Marc Brisson

Beate Sander

Adrienne K. Chan

Iris Ganser

Nadine Kronfli

Sharmistha Mishra

Mathieu Maheu-Giroux

Background: Epidemic waves of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stay in inte… (see more)nsive cares units (ICUs) among COVID-19 patients hospitalized through the first three epidemic waves in Canada. Methods: We used population-based provincial hospitalization data from Ontario and Qu&eacutebec to examine mortality risk and lengths of ICU stay. For each province, adjusted estimates were obtained using marginal standardization of logistic regression models, adjusting for patient-level characteristics and hospital-level determinants. Results: Using all hospitalizations from Ontario (N=26,541) and Qu&eacutebec (N=23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6-7%. This general trend remained after controlling for confounders. The odds of in-hospital mortality in the highest hospital occupancy quintile was 1.2 (95%CI: 1.0-1.4; Ontario) and 1.6 (95%CI: 1.3-1.9; Qu&eacutebec) times that of the lowest quintile. Variants of concerns were associated with an increased in-hospital mortality. Length of ICU stay decreased over time from a mean of 16 days (SD=18) to 15 days (SD=15) in the third wave but were consistently higher in Ontario than Qu&eacutebec by 3-6 days. Conclusion: In-hospital mortality risks and lengths of ICU stay declined over time in both provinces, despite changing patient demographics, suggesting that new therapeutics and treatment, as well as improved clinical protocols, could have contributed to this reduction. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.

2021-12-08

medRxiv (preprint)

Supervised multi-specialist topic model with applications on large-scale electronic health record data

Ziyang Song

Xavier Sumba Toral

Yixin Xu

Aihua Liu

Liming Guo

Guido Powell

Ariane Marelli

Yue Li

Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision … (see more)medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled. Materials and Methods: We present MixEHR-S to jointly infer specialist-disease topics from the EHR data. As the key contribution, we model the specialist assignments and ICD-coded diagnoses as the latent topics based on patient's underlying disease topic mixture in a novel unified supervised hierarchical Bayesian topic model. For efficient inference, we developed a closed-form collapsed variational inference algorithm to learn the model distributions of MixEHR-S. Results: We applied MixEHR-S to two independent large-scale EHR databases in Quebec with three targeted applications: (1) Congenital Heart Disease (CHD) diagnostic prediction among 154,775 patients; (2) Chronic obstructive pulmonary disease (COPD) diagnostic prediction among 73,791 patients; (3) future insulin treatment prediction among 78,712 patients diagnosed with diabetes as a mean to assess the disease exacerbation. In all three applications, MixEHR-S conferred clinically meaningful latent topics among the most predictive latent topics and achieved superior target prediction accuracy compared to the existing methods, providing opportunities for prioritizing high-risk patients for healthcare services. Availability and implementation: MixEHR-S source code and scripts of the experiments are freely available at https://github.com/li-lab-mcgill/mixehrS

2021-08-01

Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (published)

arxiv.org

Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records

Yu Luo

David A. Stephens

2020-03-31

Biometrics (published)