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1. Mitigating the Risk of Health Inequity Exacerbated by Large Language Models

Author

Ji, Yuelyu, Ma, Wenhe, Sivarajkumar, Sonish, Zhang, Hang, Sadhu, Eugene Mathew, Li, Zhuochun, Wu, Xizhi, Visweswaran, Shyam, and Wang, Yanshan

Subjects
Computer Science - Computation and Language
Abstract

Recent advancements in large language models have demonstrated their potential in numerous medical applications, particularly in automating clinical trial matching for translational research and enhancing medical question answering for clinical decision support. However, our study shows that incorporating non decisive sociodemographic factors such as race, sex, income level, LGBT+ status, homelessness, illiteracy, disability, and unemployment into the input of LLMs can lead to incorrect and harmful outputs for these populations. These discrepancies risk exacerbating existing health disparities if LLMs are widely adopted in healthcare. To address this issue, we introduce EquityGuard, a novel framework designed to detect and mitigate the risk of health inequities in LLM based medical applications. Our evaluation demonstrates its efficacy in promoting equitable outcomes across diverse populations.

Published
2024

2. LADDER: Language Driven Slice Discovery and Error Rectification

Author

Ghosh, Shantanu, Syed, Rayan, Wang, Chenyu, Poynton, Clare B., Visweswaran, Shyam, and Batmanghelich, Kayhan

Subjects
Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition
Abstract

Error slice discovery associates structured patterns with model errors. Existing methods discover error slices by clustering the error-prone samples with similar patterns or assigning discrete attributes to each sample for post-hoc analysis. While these methods aim for interpretability and easier mitigation through reweighting or rebalancing, they may not capture the full complexity of error patterns due to incomplete or missing attributes. Contrary to the existing approach, this paper utilizes the reasoning capabilities of the Large Language Model (LLM) to analyze complex error patterns and generate testable hypotheses. This paper proposes LADDER: Language Driven slice Discovery and Error Rectification. It first projects the model's representation into a language-aligned feature space (eg CLIP) to preserve semantics in the original model feature space. This ensures the accurate retrieval of sentences that highlight the model's errors. Next, the LLM utilizes the sentences and generates hypotheses to discover error slices. Finally, we mitigate the error by fine-tuning the classification head by creating a group-balanced dataset using the hypotheses. Our entire method does not require any attribute annotation, either explicitly or through external tagging models. We validate our method with \textbf{five} image classification datasets.

Published
2024

3. Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammography

Author

Ghosh, Shantanu, Poynton, Clare B., Visweswaran, Shyam, and Batmanghelich, Kayhan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

The lack of large and diverse training data on Computer-Aided Diagnosis (CAD) in breast cancer detection has been one of the concerns that impedes the adoption of the system. Recently, pre-training with large-scale image text datasets via Vision-Language models (VLM) (\eg CLIP) partially addresses the issue of robustness and data efficiency in computer vision (CV). This paper proposes Mammo-CLIP, the first VLM pre-trained on a substantial amount of screening mammogram-report pairs, addressing the challenges of dataset diversity and size. Our experiments on two public datasets demonstrate strong performance in classifying and localizing various mammographic attributes crucial for breast cancer detection, showcasing data efficiency and robustness similar to CLIP in CV. We also propose Mammo-FActOR, a novel feature attribution method, to provide spatial interpretation of representation with sentence-level granularity within mammography reports. Code is available publicly: \url{https://github.com/batmanlab/Mammo-CLIP}., Comment: MICCAI 2024, early accept, top 11%

Published
2024

4. Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning

Author

Gao, Fengyi, Zhang, Xingyu, Sivarajkumar, Sonish, Denny, Parker, Aldhahwani, Bayan, Visweswaran, Shyam, Shi, Ryan, Hogan, William, Bove, Allyn, and Wang, Yanshan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients' rehabilitation exercises and demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. We collected data for 265 stroke patients from the University of Pittsburgh Medical Center. We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models-logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)-to predict outcomes in functional ability. Statistical analyses revealed significant associations between functional improvements and specific exercises. The RF model achieved the best performance in predicting functional outcomes. In this study, we identified three rehabilitation exercises that significantly contributed to patient post-stroke functional ability improvement in the first two months. Additionally, the successful application of a machine learning model to predict patient-specific functional outcomes underscores the potential for precision rehabilitation.

Published
2024

5. A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review

Author

Tam, Thomas Yu Chow, Sivarajkumar, Sonish, Kapoor, Sumit, Stolyar, Alisa V, Polanska, Katelyn, McCarthy, Karleigh R, Osterhoudt, Hunter, Wu, Xizhi, Visweswaran, Shyam, Fu, Sunyang, Mathur, Piyush, Cacciamani, Giovanni E., Sun, Cong, Peng, Yifan, and Wang, Yanshan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

With generative artificial intelligence (AI), particularly large language models (LLMs), continuing to make inroads in healthcare, it is critical to supplement traditional automated evaluations with human evaluations. Understanding and evaluating the output of LLMs is essential to assuring safety, reliability, and effectiveness. However, human evaluation's cumbersome, time-consuming, and non-standardized nature presents significant obstacles to comprehensive evaluation and widespread adoption of LLMs in practice. This study reviews existing literature on human evaluation methodologies for LLMs in healthcare. We highlight a notable need for a standardized and consistent human evaluation approach. Our extensive literature search, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, includes publications from January 2018 to February 2024. The review examines the human evaluation of LLMs across various medical specialties, addressing factors such as evaluation dimensions, sample types and sizes, selection, and recruitment of evaluators, frameworks and metrics, evaluation process, and statistical analysis type. Drawing on the diverse evaluation strategies employed in these studies, we propose a comprehensive and practical framework for human evaluation of LLMs: QUEST: Quality of Information, Understanding and Reasoning, Expression Style and Persona, Safety and Harm, and Trust and Confidence. This framework aims to improve the reliability, generalizability, and applicability of human evaluation of LLMs in different healthcare applications by defining clear evaluation dimensions and offering detailed guidelines.

Published
2024

6. Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

Author

Visweswaran, Shyam, Colditz, Jason B, O’Halloran, Patrick, Han, Na-Rae, Taneja, Sanya B, Welling, Joel, Chu, Kar-Hai, Sidani, Jaime E, and Primack, Brian A

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundTwitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. ObjectiveThis study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. MethodsWe continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. ResultsLSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. ConclusionsWe derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

Published
2020
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7. Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study

Author

King, Andrew J, Cooper, Gregory F, Clermont, Gilles, Hochheiser, Harry, Hauskrecht, Milos, Sittig, Dean F, and Visweswaran, Shyam

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundElectronic medical record (EMR) systems capture large amounts of data per patient and present that data to physicians with little prioritization. Without prioritization, physicians must mentally identify and collate relevant data, an activity that can lead to cognitive overload. To mitigate cognitive overload, a Learning EMR (LEMR) system prioritizes the display of relevant medical record data. Relevant data are those that are pertinent to a context—defined as the combination of the user, clinical task, and patient case. To determine which data are relevant in a specific context, a LEMR system uses supervised machine learning models of physician information-seeking behavior. Since obtaining information-seeking behavior data via manual annotation is slow and expensive, automatic methods for capturing such data are needed. ObjectiveThe goal of the research was to propose and evaluate eye tracking as a high-throughput method to automatically acquire physician information-seeking behavior useful for training models for a LEMR system. MethodsCritical care medicine physicians reviewed intensive care unit patient cases in an EMR interface developed for the study. Participants manually identified patient data that were relevant in the context of a clinical task: preparing a patient summary to present at morning rounds. We used eye tracking to capture each physician’s gaze dwell time on each data item (eg, blood glucose measurements). Manual annotations and gaze dwell times were used to define target variables for developing supervised machine learning models of physician information-seeking behavior. We compared the performance of manual selection and gaze-derived models on an independent set of patient cases. ResultsA total of 68 pairs of manual selection and gaze-derived machine learning models were developed from training data and evaluated on an independent evaluation data set. A paired Wilcoxon signed-rank test showed similar performance of manual selection and gaze-derived models on area under the receiver operating characteristic curve (P=.40). ConclusionsWe used eye tracking to automatically capture physician information-seeking behavior and used it to train models for a LEMR system. The models that were trained using eye tracking performed like models that were trained using manual annotations. These results support further development of eye tracking as a high-throughput method for training clinical decision support systems that prioritize the display of relevant medical record data.

Published
2020
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8. Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice Guidelines

Author

Oniani, David, Wu, Xizhi, Visweswaran, Shyam, Kapoor, Sumit, Kooragayalu, Shravan, Polanska, Katelyn, and Wang, Yanshan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Background Large Language Models (LLMs), enhanced with Clinical Practice Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS). However, methods for incorporating CPGs into LLMs are not well studied. Methods We develop three distinct methods for incorporating CPGs into LLMs: Binary Decision Tree (BDT), Program-Aided Graph Construction (PAGC), and Chain-of-Thought-Few-Shot Prompting (CoT-FSP). To evaluate the effectiveness of the proposed methods, we create a set of synthetic patient descriptions and conduct both automatic and human evaluation of the responses generated by four LLMs: GPT-4, GPT-3.5 Turbo, LLaMA, and PaLM 2. Zero-Shot Prompting (ZSP) was used as the baseline method. We focus on CDS for COVID-19 outpatient treatment as the case study. Results All four LLMs exhibit improved performance when enhanced with CPGs compared to the baseline ZSP. BDT outperformed both CoT-FSP and PAGC in automatic evaluation. All of the proposed methods demonstrated high performance in human evaluation. Conclusion LLMs enhanced with CPGs demonstrate superior performance, as compared to plain LLMs with ZSP, in providing accurate recommendations for COVID-19 outpatient treatment, which also highlights the potential for broader applications beyond the case study.

Published
2024

10. MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images

Author

Xu, Yanwu, Sun, Li, Peng, Wei, Jia, Shuyue, Morrison, Katelyn, Perer, Adam, Zandifar, Afrooz, Visweswaran, Shyam, Eslami, Motahhare, and Batmanghelich, Kayhan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. The results of comparative assessments indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines, airways, and vascular structures. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.

Published
2023

11. An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing

Author

Sivarajkumar, Sonish, Kelley, Mark, Samolyk-Mazzanti, Alyssa, Visweswaran, Shyam, and Wang, Yanshan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large language models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP), especially in domains where labeled data is scarce or expensive, such as clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches. In this paper, we present a comprehensive and systematic experimental study on prompt engineering for five clinical NLP tasks: Clinical Sense Disambiguation, Biomedical Evidence Extraction, Coreference Resolution, Medication Status Extraction, and Medication Attribute Extraction. We assessed the prompts proposed in recent literature, including simple prefix, simple cloze, chain of thought, and anticipatory prompts, and introduced two new types of prompts, namely heuristic prompting and ensemble prompting. We evaluated the performance of these prompts on three state-of-the-art LLMs: GPT-3.5, BARD, and LLAMA2. We also contrasted zero-shot prompting with few-shot prompting, and provide novel insights and guidelines for prompt engineering for LLMs in clinical NLP. To the best of our knowledge, this is one of the first works on the empirical evaluation of different prompt engineering approaches for clinical NLP in this era of generative AI, and we hope that it will inspire and inform future research in this area.

Published
2023

12. Mining Clinical Notes for Physical Rehabilitation Exercise Information: Natural Language Processing Algorithm Development and Validation Study

Author

Sivarajkumar, Sonish, Gao, Fengyi, Denny, Parker E., Aldhahwani, Bayan M., Visweswaran, Shyam, Bove, Allyn, and Wang, Yanshan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Post-stroke patient rehabilitation requires precise, personalized treatment plans. Natural Language Processing (NLP) offers potential to extract valuable exercise information from clinical notes, aiding in the development of more effective rehabilitation strategies. Objective: This study aims to develop and evaluate a variety of NLP algorithms to extract and categorize physical rehabilitation exercise information from the clinical notes of post-stroke patients treated at the University of Pittsburgh Medical Center. A cohort of 13,605 patients diagnosed with stroke was identified, and their clinical notes containing rehabilitation therapy notes were retrieved. A comprehensive clinical ontology was created to represent various aspects of physical rehabilitation exercises. State-of-the-art NLP algorithms were then developed and compared, including rule-based, machine learning-based algorithms, and large language model (LLM)-based algorithms (ChatGPT). Analysis was conducted on a dataset comprising 23,724 notes with detailed demographic and clinical characteristics. The rule-based NLP algorithm demonstrated superior performance in most areas, particularly in detecting the 'Right Side' location with an F1 score of 0.975, outperforming Gradient Boosting by 0.063. Gradient Boosting excelled in 'Lower Extremity' location detection (F1 score: 0.978), surpassing rule-based NLP by 0.023. It also showed notable performance in 'Passive Range of Motion' with an F1 score of 0.970, a 0.032 improvement over rule-based NLP. The rule-based algorithm efficiently handled 'Duration', 'Sets', and 'Reps' with F1 scores up to 0.65. LLM-based NLP, particularly ChatGPT with few-shot prompts, achieved high recall but generally lower precision and F1 scores. However, it notably excelled in 'Backward Plane' motion detection, achieving an F1 score of 0.846, surpassing the rule-based algorithm's 0.720.

Published
2023

14. Hyperbolic Molecular Representation Learning for Drug Repositioning

Author

Yu, Ke, Visweswaran, Shyam, and Batmanghelich, Kayhan

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

Learning accurate drug representations is essential for task such as computational drug repositioning. A drug hierarchy is a valuable source that encodes knowledge of relations among drugs in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the drug-drug similarity information obtained from the hierarchy to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical relations. Our qualitative results support that the learned drug embedding can induce the hierarchical relations among drugs. We demonstrate that the learned drug embedding can be used for drug repositioning., Comment: Accepted by NeurIPS workshop 2020. arXiv admin note: substantial text overlap with arXiv:2006.00986

Published
2022

15. Generalisable long COVID subtypes: Findings from the NIH N3C and RECOVER programmes

Author

Reese, Justin T, Blau, Hannah, Casiraghi, Elena, Bergquist, Timothy, Loomba, Johanna J, Callahan, Tiffany J, Laraway, Bryan, Antonescu, Corneliu, Coleman, Ben, Gargano, Michael, Wilkins, Kenneth J, Cappelletti, Luca, Fontana, Tommaso, Ammar, Nariman, Antony, Blessy, Murali, TM, Caufield, J Harry, Karlebach, Guy, McMurry, Julie A, Williams, Andrew, Moffitt, Richard, Banerjee, Jineta, Solomonides, Anthony E, Davis, Hannah, Kostka, Kristin, Valentini, Giorgio, Sahner, David, Chute, Christopher G, Madlock-Brown, Charisse, Haendel, Melissa A, Robinson, Peter N, Consortium, N3C, Spratt, Heidi, Visweswaran, Shyam, Flack, Joseph Eugene, Yoo, Yun Jae, Gabriel, Davera, Alexander, G Caleb, Mehta, Hemalkumar B, Liu, Feifan, Miller, Robert T, Wong, Rachel, Hill, Elaine L, Consortium, RECOVER, Thorpe, Lorna E, and Divers, Jasmin

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Infectious Diseases, Precision Medicine, Coronaviruses, Emerging Infectious Diseases, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Good Health and Well Being, Humans, COVID-19, Disease Progression, Post-Acute COVID-19 Syndrome, SARS-CoV-2, N3C Consortium, RECOVER Consortium, Human Phenotype Ontology, Long COVID, Machine learning, Precision medicine, Semantic similarity, Public Health and Health Services, Clinical sciences, Epidemiology
Abstract

BackgroundStratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.MethodsWe present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.FindingsWe found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.InterpretationSemantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.FundingNIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Published
2023

16. Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium

Author

Moal, Bertrand, Orieux, Arthur, Ferté, Thomas, Neuraz, Antoine, Brat, Gabriel A, Avillach, Paul, Bonzel, Clara-Lea, Cai, Tianxi, Cho, Kelly, Cossin, Sébastien, Griffier, Romain, Hanauer, David A, Haverkamp, Christian, Ho, Yuk-Lam, Hong, Chuan, Hutch, Meghan R, Klann, Jeffrey G, Le, Trang T, Loh, Ne Hooi Will, Luo, Yuan, Makoudjou, Adeline, Morris, Michele, Mowery, Danielle L, Olson, Karen L, Patel, Lav P, Samayamuthu, Malarkodi J, Vidorreta, Fernando J Sanz, Schriver, Emily R, Schubert, Petra, Verdy, Guillaume, Visweswaran, Shyam, Wang, Xuan, Weber, Griffin M, Xia, Zongqi, Yuan, William, Zhang, Harrison G, Zöller, Daniela, Kohane, Isaac S, EHR, The Consortium for Clinical Characterization of COVID-19 by, Boyer, Alexandre, and Jouhet, Vianney

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Health Sciences, Infectious Diseases, Acute Respiratory Distress Syndrome, Rare Diseases, Lung, Clinical Research, Emerging Infectious Diseases, Prevention, Patient Safety, Aetiology, 2.4 Surveillance and distribution, Good Health and Well Being, Humans, Young Adult, Aged, Adolescent, Adult, Middle Aged, COVID-19, SARS-CoV-2, Cohort Studies, Retrospective Studies, Electronic Health Records, Respiratory Distress Syndrome, Obesity, Consortium for Clinical Characterization of COVID-19 by EHR, General Science & Technology
Abstract

PurposeIn young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population.MethodsA retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS.ResultsAmong the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%).ConclusionTrough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Published
2023

17. Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing

Author

Sivarajkumar, Sonish, Tam, Thomas Yu CHow, Mohammad, Haneef Ahamed, Viggiano, Samual, Oniani, David, Visweswaran, Shyam, and Wang, Yanshan

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Alzheimer's Disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. A gold standard dataset is created from manual annotation of 570 randomly sampled clinical note documents from the adSLEEP, a corpus of 192,000 de-identified clinical notes of 7,266 AD patients retrieved from the University of Pittsburgh Medical Center (UPMC). We developed a rule-based Natural Language Processing (NLP) algorithm, machine learning models, and Large Language Model(LLM)-based NLP algorithms to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the gold standard dataset. Rule-based NLP algorithm achieved the best performance of F1 across all sleep-related concepts. In terms of Positive Predictive Value (PPV), rule-based NLP algorithm achieved 1.00 for daytime sleepiness and sleep duration, machine learning models: 0.95 and for napping, 0.86 for bad sleep quality and 0.90 for snoring; and LLAMA2 with finetuning achieved PPV of 0.93 for Night Wakings, 0.89 for sleep problem, and 1.00 for sleep duration. The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts. This study focused on the clinical notes of patients with AD, but could be extended to general sleep information extraction for other diseases.

Published
2022

18. Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring

Author

Tran, Tran Quoc Bao, Lip, Stefanie, du Toit, Clea, Kalaria, Tejas Kumar, Bhaskar, Ravi K., O’Neil, Alison Q., Graff, Beata, Hoffmann, Michał, Szyndler, Anna, Polonis, Katarzyna, Wolf, Jacek, Reddy, Sandeep, Narkiewicz, Krzysztof, Dasgupta, Indranil, Dominiczak, Anna F., Visweswaran, Shyam, McCallum, Linsay, and Padmanabhan, Sandosh

Published
2024
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19. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Author

Weber, Griffin M, Hong, Chuan, Xia, Zongqi, Palmer, Nathan P, Avillach, Paul, L’Yi, Sehi, Keller, Mark S, Murphy, Shawn N, Gutiérrez-Sacristán, Alba, Bonzel, Clara-Lea, Serret-Larmande, Arnaud, Neuraz, Antoine, Omenn, Gilbert S, Visweswaran, Shyam, Klann, Jeffrey G, South, Andrew M, Loh, Ne Hooi Will, Cannataro, Mario, Beaulieu-Jones, Brett K, Bellazzi, Riccardo, Agapito, Giuseppe, Alessiani, Mario, Aronow, Bruce J, Bell, Douglas S, Benoit, Vincent, Bourgeois, Florence T, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, DuVall, Scott L, Barrio, Noelia García, Hanauer, David A, Ho, Yuk-Lam, Holmes, John H, Issitt, Richard W, Liu, Molei, Luo, Yuan, Lynch, Kristine E, Maidlow, Sarah E, Malovini, Alberto, Mandl, Kenneth D, Mao, Chengsheng, Matheny, Michael E, Moore, Jason H, Morris, Jeffrey S, Morris, Michele, Mowery, Danielle L, Ngiam, Kee Yuan, Patel, Lav P, Pedrera-Jimenez, Miguel, Ramoni, Rachel B, Schriver, Emily R, Schubert, Petra, Balazote, Pablo Serrano, Spiridou, Anastasia, Tan, Amelia LM, Tan, Byorn WL, Tibollo, Valentina, Torti, Carlo, Trecarichi, Enrico M, Wang, Xuan, Kohane, Isaac S, Cai, Tianxi, and Brat, Gabriel A

Subjects
Health Services and Systems, Health Sciences, Basic Behavioral and Social Science, Behavioral and Social Science, Good Health and Well Being, Consortium for Clinical Characterization of COVID-19 by EHR, Health services and systems
Abstract

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.

Published
2022

22. Potential pitfalls in the use of real-world data for studying long COVID

Author

Zhang, Harrison G., Honerlaw, Jacqueline P., Maripuri, Monika, Samayamuthu, Malarkodi Jebathilagam, Beaulieu-Jones, Brendin R., Baig, Huma S., L’Yi, Sehi, Ho, Yuk-Lam, Morris, Michele, Panickan, Vidul Ayakulangara, Wang, Xuan, Weber, Griffin M., Liao, Katherine P., Visweswaran, Shyam, Tan, Bryce W. Q., Yuan, William, Gehlenborg, Nils, Muralidhar, Sumitra, Ramoni, Rachel B., Kohane, Isaac S., Xia, Zongqi, Cho, Kelly, Cai, Tianxi, and Brat, Gabriel A.

Published
2023
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23. Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space

Author

Yu, Ke, Visweswaran, Shyam, and Batmanghelich, Kayhan

Subjects
Computer Science - Machine Learning, Quantitative Biology - Molecular Networks, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning
Abstract

Learning accurate drug representation is essential for tasks such as computational drug repositioning and prediction of drug side-effects. A drug hierarchy is a valuable source that encodes human knowledge of drug relations in a tree-like structure where drugs that act on the same organs, treat the same disease, or bind to the same biological target are grouped together. However, its utility in learning drug representations has not yet been explored, and currently described drug representations cannot place novel molecules in a drug hierarchy. Here, we develop a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from molecular structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised). We use the Variational Auto-Encoder (VAE) framework to encode the chemical structures of molecules and use the knowledge-based drug-drug similarity to induce the clustering of drugs in hyperbolic space. The hyperbolic space is amenable for encoding hierarchical concepts. Both quantitative and qualitative results support that the learned drug embedding can accurately reproduce the chemical structure and induce the hierarchical relations among drugs. Furthermore, our approach can infer the pharmacological properties of novel molecules by retrieving similar drugs from the embedding space. We demonstrate that the learned drug embedding can be used to find new uses for existing drugs and to discover side-effects. We show that it significantly outperforms baselines in both tasks.

Published
2020

24. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data

Author

Klann, Jeffrey G, Estiri, Hossein, Weber, Griffin M, Moal, Bertrand, Avillach, Paul, Hong, Chuan, Tan, Amelia LM, Beaulieu-Jones, Brett K, Castro, Victor, Maulhardt, Thomas, Geva, Alon, Malovini, Alberto, South, Andrew M, Visweswaran, Shyam, Morris, Michele, Samayamuthu, Malarkodi J, Omenn, Gilbert S, Ngiam, Kee Yuan, Mandl, Kenneth D, Boeker, Martin, Olson, Karen L, Mowery, Danielle L, Follett, Robert W, Hanauer, David A, Bellazzi, Riccardo, Moore, Jason H, Loh, Ne-Hooi Will, Bell, Douglas S, Wagholikar, Kavishwar B, Chiovato, Luca, Tibollo, Valentina, Rieg, Siegbert, Li, Anthony LLJ, Jouhet, Vianney, Schriver, Emily, Xia, Zongqi, Hutch, Meghan, Luo, Yuan, Kohane, Isaac S, EHR, The Consortium for Clinical Characterization of COVID-19 by, Brat, Gabriel A, and Murphy, Shawn N

Subjects
Health Services and Systems, Health Sciences, Patient Safety, HIV/AIDS, Good Health and Well Being, COVID-19, Electronic Health Records, Hospitalization, Humans, Machine Learning, Prognosis, ROC Curve, Sensitivity and Specificity, Severity of Illness Index, novel coronavirus, disease severity, computable phenotype, medical informatics, data networking, data interoperability, Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Information and Computing Sciences, Engineering, Medical and Health Sciences, Medical Informatics, Biomedical and clinical sciences, Health sciences, Information and computing sciences
Abstract

ObjectiveThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.Materials and methodsTwelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.ResultsThe full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review.DiscussionWe developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.ConclusionsWe developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

Published
2021

25. The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment

Author

Haendel, Melissa A, Chute, Christopher G, Bennett, Tellen D, Eichmann, David A, Guinney, Justin, Kibbe, Warren A, Payne, Philip RO, Pfaff, Emily R, Robinson, Peter N, Saltz, Joel H, Spratt, Heidi, Suver, Christine, Wilbanks, John, Wilcox, Adam B, Williams, Andrew E, Wu, Chunlei, Blacketer, Clair, Bradford, Robert L, Cimino, James J, Clark, Marshall, Colmenares, Evan W, Francis, Patricia A, Gabriel, Davera, Graves, Alexis, Hemadri, Raju, Hong, Stephanie S, Hripscak, George, Jiao, Dazhi, Klann, Jeffrey G, Kostka, Kristin, Lee, Adam M, Lehmann, Harold P, Lingrey, Lora, Miller, Robert T, Morris, Michele, Murphy, Shawn N, Natarajan, Karthik, Palchuk, Matvey B, Sheikh, Usman, Solbrig, Harold, Visweswaran, Shyam, Walden, Anita, Walters, Kellie M, Weber, Griffin M, Zhang, Xiaohan Tanner, Zhu, Richard L, Amor, Benjamin, Girvin, Andrew T, Manna, Amin, Qureshi, Nabeel, Kurilla, Michael G, Michael, Sam G, Portilla, Lili M, Rutter, Joni L, Austin, Christopher P, and Gersing, Ken R

Subjects
COVID-19, Computer Security, Data Analysis, Data Science, Ethics Committees, Research, Government Regulation, Humans, Information Dissemination, Intersectoral Collaboration, National Institutes of Health (U.S.), United States, N3C Consortium, EHR data, SARS-CoV-2, clinical data model harmonization, collaborative analytics, open science, Information and Computing Sciences, Engineering, Medical and Health Sciences, Medical Informatics
Abstract

ObjectiveCoronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and methodsThe Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.ResultsOrganized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.ConclusionsThe N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

Published
2021

26. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium

Author

Aaron, James R., Adam, Atif, Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., Angoulvant, François, Anthony, Li LLJ., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Panickan, Vidul Ayakulangara, Azevedo, Paula S., Badenes, Rafael, Balshi, James, Batugo, Ashley, Beaulieu-Jones, Brendin R., Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Boeker, Martin, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Cattelan, Anna Maria, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, García-Barrio, Noelia, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Goh, Rachel SJ., González, Tomás González, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Guzzi, Pietro H., Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Honerlaw, Jacqueline P., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kainth, Mundeep K., Kate, Kernan F., Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Kernan, Kate F., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Makwana, Simran, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Maripuri, Monika, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mazzotti, Diego R., Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku FM., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Ashley C., Pfaff, Emily R., Pillion, Danielle, Pizzimenti, Sara, Priya, Tanu, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Romero-Garcia, Nekane, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin A., Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Sperotto, Francesca, Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia LM., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma MS., Torti, Carlo, Trecarichi, Enrico M., Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zachariasse, Joany M., Zahner, Janet J., Zambelli, Alberto, Zhang, Harrison G., Zöller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Li, Xiudi, Rofeberg, Valerie N., Elias, Matthew D., Laird-Gion, Jessica, and Newburger, Jane W.

Published
2023
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27. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study

Author

Aaron, James R., Agapito, Giuseppe, Albayrak, Adem, Albi, Giuseppe, Alessiani, Mario, Alloni, Anna, Amendola, Danilo F., François Angoulvant, Anthony, Li L.L.J., Aronow, Bruce J., Ashraf, Fatima, Atz, Andrew, Avillach, Paul, Azevedo, Paula S., Balshi, James, Beaulieu-Jones, Brett K., Bell, Douglas S., Bellasi, Antonio, Bellazzi, Riccardo, Benoit, Vincent, Beraghi, Michele, Bernal-Sobrino, José Luis, Bernaux, Mélodie, Bey, Romain, Bhatnagar, Surbhi, Blanco-Martínez, Alvar, Bonzel, Clara-Lea, Booth, John, Bosari, Silvano, Bourgeois, Florence T., Bradford, Robert L., Brat, Gabriel A., Bréant, Stéphane, Brown, Nicholas W., Bruno, Raffaele, Bryant, William A., Bucalo, Mauro, Bucholz, Emily, Burgun, Anita, Cai, Tianxi, Cannataro, Mario, Carmona, Aldo, Caucheteux, Charlotte, Champ, Julien, Chen, Jin, Chen, Krista Y., Chiovato, Luca, Chiudinelli, Lorenzo, Cho, Kelly, Cimino, James J., Colicchio, Tiago K., Cormont, Sylvie, Cossin, Sébastien, Craig, Jean B., Cruz-Bermúdez, Juan Luis, Cruz-Rojo, Jaime, Dagliati, Arianna, Daniar, Mohamad, Daniel, Christel, Das, Priyam, Devkota, Batsal, Dionne, Audrey, Duan, Rui, Dubiel, Julien, DuVall, Scott L., Esteve, Loic, Estiri, Hossein, Fan, Shirley, Follett, Robert W., Ganslandt, Thomas, Barrio, Noelia García, Garmire, Lana X., Gehlenborg, Nils, Getzen, Emily J., Geva, Alon, Gradinger, Tobias, Gramfort, Alexandre, Griffier, Romain, Griffon, Nicolas, Grisel, Olivier, Gutiérrez-Sacristán, Alba, Han, Larry, Hanauer, David A., Haverkamp, Christian, Hazard, Derek Y., He, Bing, Henderson, Darren W., Hilka, Martin, Ho, Yuk-Lam, Holmes, John H., Hong, Chuan, Huling, Kenneth M., Hutch, Meghan R., Issitt, Richard W., Jannot, Anne Sophie, Jouhet, Vianney, Kavuluru, Ramakanth, Keller, Mark S., Kennedy, Chris J., Key, Daniel A., Kirchoff, Katie, Klann, Jeffrey G., Kohane, Isaac S., Krantz, Ian D., Kraska, Detlef, Krishnamurthy, Ashok K., L'Yi, Sehi, Le, Trang T., Leblanc, Judith, Lemaitre, Guillaume, Lenert, Leslie, Leprovost, Damien, Liu, Molei, Will Loh, Ne Hooi, Long, Qi, Lozano-Zahonero, Sara, Luo, Yuan, Lynch, Kristine E., Mahmood, Sadiqa, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Mandl, Kenneth D., Mao, Chengsheng, Maram, Anupama, Martel, Patricia, Martins, Marcelo R., Marwaha, Jayson S., Masino, Aaron J., Mazzitelli, Maria, Mensch, Arthur, Milano, Marianna, Minicucci, Marcos F., Moal, Bertrand, Ahooyi, Taha Mohseni, Moore, Jason H., Moraleda, Cinta, Morris, Jeffrey S., Morris, Michele, Moshal, Karyn L., Mousavi, Sajad, Mowery, Danielle L., Murad, Douglas A., Murphy, Shawn N., Naughton, Thomas P., Breda Neto, Carlos Tadeu, Neuraz, Antoine, Newburger, Jane, Ngiam, Kee Yuan, Njoroge, Wanjiku F.M., Norman, James B., Obeid, Jihad, Okoshi, Marina P., Olson, Karen L., Omenn, Gilbert S., Orlova, Nina, Ostasiewski, Brian D., Palmer, Nathan P., Paris, Nicolas, Patel, Lav P., Pedrera-Jiménez, Miguel, Pfaff, Emily R., Pfaff, Ashley C., Pillion, Danielle, Pizzimenti, Sara, Prokosch, Hans U., Prudente, Robson A., Prunotto, Andrea, Quirós-González, Víctor, Ramoni, Rachel B., Raskin, Maryna, Rieg, Siegbert, Roig-Domínguez, Gustavo, Rojo, Pablo, Rubio-Mayo, Paula, Sacchi, Paolo, Sáez, Carlos, Salamanca, Elisa, Samayamuthu, Malarkodi Jebathilagam, Sanchez-Pinto, L. Nelson, Sandrin, Arnaud, Santhanam, Nandhini, Santos, Janaina C.C., Sanz Vidorreta, Fernando J., Savino, Maria, Schriver, Emily R., Schubert, Petra, Schuettler, Juergen, Scudeller, Luigia, Sebire, Neil J., Serrano-Balazote, Pablo, Serre, Patricia, Serret-Larmande, Arnaud, Shah, Mohsin, Hossein Abad, Zahra Shakeri, Silvio, Domenick, Sliz, Piotr, Son, Jiyeon, Sonday, Charles, South, Andrew M., Spiridou, Anastasia, Strasser, Zachary H., Tan, Amelia L.M., Tan, Bryce W.Q., Tan, Byorn W.L., Tanni, Suzana E., Taylor, Deanne M., Terriza-Torres, Ana I., Tibollo, Valentina, Tippmann, Patric, Toh, Emma M.S., Torti, Carlo, Trecarichi, Enrico M., Tseng, Yi-Ju, Vallejos, Andrew K., Varoquaux, Gael, Vella, Margaret E., Verdy, Guillaume, Vie, Jill-Jênn, Visweswaran, Shyam, Vitacca, Michele, Wagholikar, Kavishwar B., Waitman, Lemuel R., Wang, Xuan, Wassermann, Demian, Weber, Griffin M., Wolkewitz, Martin, Wong, Scott, Xia, Zongqi, Xiong, Xin, Ye, Ye, Yehya, Nadir, Yuan, William, Zambelli, Alberto, Zhang, Harrison G., Zo¨ller, Daniela, Zuccaro, Valentina, Zucco, Chiara, Mesa, Rebecca, and Verdy, Guillame

Published
2023
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29. Dirac Delta Regression: Conditional Density Estimation with Clinical Trials

Author

Strobl, Eric V. and Visweswaran, Shyam

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology
Abstract

Personalized medicine seeks to identify the causal effect of treatment for a particular patient as opposed to a clinical population at large. Most investigators estimate such personalized treatment effects by regressing the outcome of a randomized clinical trial (RCT) on patient covariates. The realized value of the outcome may however lie far from the conditional expectation. We therefore introduce a method called Dirac Delta Regression (DDR) that estimates the entire conditional density from RCT data in order to visualize the probabilities across all possible outcome values. DDR transforms the outcome into a set of asymptotically Dirac delta distributions and then estimates the density using non-linear regression. The algorithm can identify significant differences in patient-specific outcomes even when no population level effect exists. Moreover, DDR outperforms state-of-the-art algorithms in conditional density estimation by a large margin even in the small sample regime. An R package is available at https://github.com/ericstrobl/DDR.

Published
2019

31. Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

Author

Tan, Amelia L.M., Getzen, Emily J., Hutch, Meghan R., Strasser, Zachary H., Gutiérrez-Sacristán, Alba, Le, Trang T., Dagliati, Arianna, Morris, Michele, Hanauer, David A., Moal, Bertrand, Bonzel, Clara-Lea, Yuan, William, Chiudinelli, Lorenzo, Das, Priam, Zhang, Harrison G., Aronow, Bruce J., Avillach, Paul, Brat, Gabriel.A., Cai, Tianxi, Hong, Chuan, La Cava, William G., Hooi Will Loh, He, Luo, Yuan, Murphy, Shawn N., Yuan Hgiam, Kee, Omenn, Gilbert S., Patel, Lav P., Jebathilagam Samayamuthu, Malarkodi, Shriver, Emily R., Shakeri Hossein Abad, Zahra, Tan, Byorn W.L., Visweswaran, Shyam, Wang, Xuan, Weber, Griffin M., Xia, Zongqi, Verdy, Bertrand, Long, Qi, Mowery, Danielle L., and Holmes, John H.

Published
2023
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33. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes

Author

Spratt, Heidi, Visweswaran, Shyam, Flack, Joseph Eugene, IV, Yoo, Yun Jae, Gabriel, Davera, Alexander, G. Caleb, Mehta, Hemalkumar B., Liu, Feifan, Miller, Robert T., Wong, Rachel, Hill, Elaine L., Thorpe, Lorna E., Divers, Jasmin, Reese, Justin T., Blau, Hannah, Casiraghi, Elena, Bergquist, Timothy, Loomba, Johanna J., Callahan, Tiffany J., Laraway, Bryan, Antonescu, Corneliu, Coleman, Ben, Gargano, Michael, Wilkins, Kenneth J., Cappelletti, Luca, Fontana, Tommaso, Ammar, Nariman, Antony, Blessy, Murali, T.M., Caufield, J. Harry, Karlebach, Guy, McMurry, Julie A., Williams, Andrew, Moffitt, Richard, Banerjee, Jineta, Solomonides, Anthony E., Davis, Hannah, Kostka, Kristin, Valentini, Giorgio, Sahner, David, Chute, Christopher G., Madlock-Brown, Charisse, Haendel, Melissa A., and Robinson, Peter N.

Published
2023
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34. SurvMaximin: Robust federated approach to transporting survival risk prediction models

Author

Wang, Xuan, Zhang, Harrison G., Xiong, Xin, Hong, Chuan, Weber, Griffin M., Brat, Gabriel A., Bonzel, Clara-Lea, Luo, Yuan, Duan, Rui, Palmer, Nathan P., Hutch, Meghan R., Gutiérrez-Sacristán, Alba, Bellazzi, Riccardo, Chiovato, Luca, Cho, Kelly, Dagliati, Arianna, Estiri, Hossein, García-Barrio, Noelia, Griffier, Romain, Hanauer, David A., Ho, Yuk-Lam, Holmes, John H., Keller, Mark S., Klann MEng, Jeffrey G., L'Yi, Sehi, Lozano-Zahonero, Sara, Maidlow, Sarah E., Makoudjou, Adeline, Malovini, Alberto, Moal, Bertrand, Moore, Jason H., Morris, Michele, Mowery, Danielle L., Murphy, Shawn N, Neuraz, Antoine, Yuan Ngiam, Kee, Omenn, Gilbert S., Patel, Lav P., Pedrera-Jiménez, Miguel, Prunotto, Andrea, Jebathilagam Samayamuthu, Malarkodi, Sanz Vidorreta, Fernando J, Schriver, Emily R., Schubert, Petra, Serrano-Balazote, Pablo, South, Andrew M., Tan, Amelia L.M., Tan, Byorn W.L., Tibollo, Valentina, Tippmann, Patric, Visweswaran, Shyam, Xia, Zongqi, Yuan, William, Zöller, Daniela, Kohane, Isaac S., Avillach, Paul, Guo, Zijian, and Cai, Tianxi

Published
2022
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36. The All of Us Research Program: Data quality, utility, and diversity

Author

Ahmedani, Brian, Cole Johnson, Christine D., Ahsan, Habib, Antoine-LaVigne, Donna, Singleton, Glendora, Anton-Culver, Hoda, Topol, Eric, Baca-Motes, Katie, Steinhubl, Steven, Wade, James, Begale, Mark, Jain, Praduman, Sutherland, Scott, Lewis, Beth, Korf, Bruce, Behringer, Melissa, Gharavi, Ali G., Goldstein, David B., Hripcsak, George, Bier, Louise, Boerwinkle, Eric, Brilliant, Murray H., Murali, Narayana, Hebbring, Scott Joseph, Farrar-Edwards, Dorothy, Burnside, Elizabeth, Drezner, Marc K., Taylor, Amy, Channamsetty, Veena, Montalvo, Wanda, Sharma, Yashoda, Chinea, Carmen, Jenks, Nancy, Cicek, Mine, Thibodeau, Steve, Holmes, Beverly Wilson, Schlueter, Eric, Collier, Ever, Winkler, Joyce, Corcoran, John, D’Addezio, Nick, Daviglus, Martha, Winn, Robert, Wilkins, Consuelo, Roden, Dan, Denny, Joshua, Doheny, Kim, Nickerson, Debbie, Eichler, Evan, Jarvik, Gail, Funk, Gretchen, Philippakis, Anthony, Rehm, Heidi, Lennon, Niall, Kathiresan, Sekar, Gabriel, Stacey, Gibbs, Richard, Gil Rico, Edgar M., Glazer, David, Grand, Joannie, Greenland, Philip, Harris, Paul, Shenkman, Elizabeth, Hogan, William R., Igho-Pemu, Priscilla, Pollan, Cliff, Jorge, Milena, Okun, Sally, Karlson, Elizabeth W., Smoller, Jordan, Murphy, Shawn N., Ross, Margaret Elizabeth, Kaushal, Rainu, Winford, Eboni, Wallace, Febe, Khatri, Parinda, Kheterpal, Vik, Ojo, Akinlolu, Moreno, Francisco A., Kron, Irving, Peterson, Rachele, Menon, Usha, Lattimore, Patricia Watkins, Leviner, Noga, Obedin-Maliver, Juno, Lunn, Mitchell, Malik-Gagnon, Lynda, Mangravite, Lara, Marallo, Adria, Marroquin, Oscar, Visweswaran, Shyam, Reis, Steven, Marshall, Gailen, Jr., McGovern, Patrick, Mignucci, Deb, Moore, John, Munoz, Fatima, Talavera, Gregory, O'Connor, George T., O'Donnell, Christopher, Ohno-Machado, Lucila, Orr, Greg, Randal, Fornessa, Theodorou, Andreas A., Reiman, Eric, Roxas-Murray, Mercedita, Stark, Louisa, Tepp, Ronnie, Zhou, Alicia, Topper, Scott, Trousdale, Rhonda, Tsao, Phil, Weidman, Lisa, Weiss, Scott T., Wellis, David, Whittle, Jeffrey, Wilson, Amanda, Zuchner, Stephan, Zwick, Michael E., Ramirez, Andrea H., Sulieman, Lina, Schlueter, David J., Halvorson, Alese, Qian, Jun, Ratsimbazafy, Francis, Loperena, Roxana, Mayo, Kelsey, Basford, Melissa, Deflaux, Nicole, Muthuraman, Karthik N., Natarajan, Karthik, Kho, Abel, Xu, Hua, Clark, Cheryl R., Cohn, Elizabeth, Schully, Sheri D., Ahmedani, Brian K., Argos, Maria, Cronin, Robert M., O’Donnell, Christopher, Fouad, Mona, Hebbring, Scott J., Smoller, Jordan W., Sodeke, Stephen, Wilbanks, John, Hentges, Justin, Mockrin, Stephen, Lunt, Christopher, Devaney, Stephanie A., Gebo, Kelly, Denny, Joshua C., Carroll, Robert J., Harris, Paul A., and Roden, Dan M.

Published
2022
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37. International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

Author

Zhang, Harrison G., Dagliati, Arianna, Shakeri Hossein Abad, Zahra, Xiong, Xin, Bonzel, Clara-Lea, Xia, Zongqi, Tan, Bryce W. Q., Avillach, Paul, Brat, Gabriel A., Hong, Chuan, Morris, Michele, Visweswaran, Shyam, Patel, Lav P., Gutiérrez-Sacristán, Alba, Hanauer, David A., Holmes, John H., Samayamuthu, Malarkodi Jebathilagam, Bourgeois, Florence T., L’Yi, Sehi, Maidlow, Sarah E., Moal, Bertrand, Murphy, Shawn N., Strasser, Zachary H., Neuraz, Antoine, Ngiam, Kee Yuan, Loh, Ne Hooi Will, Omenn, Gilbert S., Prunotto, Andrea, Dalvin, Lauren A., Klann, Jeffrey G., Schubert, Petra, Vidorreta, Fernando J. Sanz, Benoit, Vincent, Verdy, Guillaume, Kavuluru, Ramakanth, Estiri, Hossein, Luo, Yuan, Malovini, Alberto, Tibollo, Valentina, Bellazzi, Riccardo, Cho, Kelly, Ho, Yuk-Lam, Tan, Amelia L. M., Tan, Byorn W. L., Gehlenborg, Nils, Lozano-Zahonero, Sara, Jouhet, Vianney, Chiovato, Luca, Aronow, Bruce J., Toh, Emma M. S., Wong, Wei Gen Scott, Pizzimenti, Sara, Wagholikar, Kavishwar B., Bucalo, Mauro, Cai, Tianxi, South, Andrew M., Kohane, Isaac S., and Weber, Griffin M.

Published
2022
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39. Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion

Author

Strobl, Eric V., Visweswaran, Shyam, and Spirtes, Peter L.

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

Many real datasets contain values missing not at random (MNAR). In this scenario, investigators often perform list-wise deletion, or delete samples with any missing values, before applying causal discovery algorithms. List-wise deletion is a sound and general strategy when paired with algorithms such as FCI and RFCI, but the deletion procedure also eliminates otherwise good samples that contain only a few missing values. In this report, we show that we can more efficiently utilize the observed values with test-wise deletion while still maintaining algorithmic soundness. Here, test-wise deletion refers to the process of list-wise deleting samples only among the variables required for each conditional independence (CI) test used in constraint-based searches. Test-wise deletion therefore often saves more samples than list-wise deletion for each CI test, especially when we have a sparse underlying graph. Our theoretical results show that test-wise deletion is sound under the justifiable assumption that none of the missingness mechanisms causally affect each other in the underlying causal graph. We also find that FCI and RFCI with test-wise deletion outperform their list-wise deletion and imputation counterparts on average when MNAR holds in both synthetic and real data.

Published
2017

40. Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery

Author

Strobl, Eric V., Zhang, Kun, and Visweswaran, Shyam

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many investigators cannot use KCIT with large datasets because the test scales cubicly with sample size. We therefore devise two relaxations called the Randomized Conditional Independence Test (RCIT) and the Randomized conditional Correlation Test (RCoT) which both approximate KCIT by utilizing random Fourier features. In practice, both of the proposed tests scale linearly with sample size and return accurate p-values much faster than KCIT in the large sample size context. CCD algorithms run with RCIT or RCoT also return graphs at least as accurate as the same algorithms run with KCIT but with large reductions in run time., Comment: R package: github.com/ericstrobl/RCIT

Published
2017

41. Towards cross-application model-agnostic federated cohort discovery.

Author

Dobbins, Nicholas J, Morris, Michele, Sadhu, Eugene, MacFadden, Douglas, Nazaire, Marc-Danie, Simons, William, Weber, Griffin, Murphy, Shawn, and Visweswaran, Shyam

Abstract

Objectives To demonstrate that 2 popular cohort discovery tools, Leaf and the Shared Health Research Information Network (SHRINE), are readily interoperable. Specifically, we adapted Leaf to interoperate and function as a node in a federated data network that uses SHRINE and dynamically generate queries for heterogeneous data models. Materials and Methods SHRINE queries are designed to run on the Informatics for Integrating Biology & the Bedside (i2b2) data model. We created functionality in Leaf to interoperate with a SHRINE data network and dynamically translate SHRINE queries to other data models. We randomly selected 500 past queries from the SHRINE-based national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) network for evaluation, and an additional 100 queries to refine and debug Leaf's translation functionality. We created a script for Leaf to convert the terms in the SHRINE queries into equivalent structured query language (SQL) concepts, which were then executed on 2 other data models. Results and Discussion 91.1% of the generated queries for non-i2b2 models returned counts within 5% (or ±5 patients for counts under 100) of i2b2, with 91.3% recall. Of the 8.9% of queries that exceeded the 5% margin, 77 of 89 (86.5%) were due to errors introduced by the Python script or the extract-transform-load process, which are easily fixed in a production deployment. The remaining errors were due to Leaf's translation function, which was later fixed. Conclusion Our results support that cohort discovery applications such as Leaf and SHRINE can interoperate in federated data networks with heterogeneous data models. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Extraction of sleep information from clinical notes of Alzheimer's disease patients using natural language processing.

Author

Sivarajkumar, Sonish, Tam, Thomas Yu Chow, Mohammad, Haneef Ahamed, Viggiano, Samuel, Oniani, David, Visweswaran, Shyam, and Wang, Yanshan

Abstract

Objectives Alzheimer's disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. We aim to automate the extraction of specific sleep-related patterns, such as snoring, napping, poor sleep quality, daytime sleepiness, night wakings, other sleep problems, and sleep duration, from clinical notes of AD patients. These sleep patterns are hypothesized to play a role in the incidence of AD, providing insight into the relationship between sleep and AD onset and progression. Materials and Methods A gold standard dataset is created from manual annotation of 570 randomly sampled clinical note documents from the adSLEEP, a corpus of 192 000 de-identified clinical notes of 7266 AD patients retrieved from the University of Pittsburgh Medical Center (UPMC). We developed a rule-based natural language processing (NLP) algorithm, machine learning models, and large language model (LLM)-based NLP algorithms to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the gold standard dataset. Results The annotated dataset of 482 patients comprised a predominantly White (89.2%), older adult population with an average age of 84.7 years, where females represented 64.1%, and a vast majority were non-Hispanic or Latino (94.6%). Rule-based NLP algorithm achieved the best performance of F1 across all sleep-related concepts. In terms of positive predictive value (PPV), the rule-based NLP algorithm achieved the highest PPV scores for daytime sleepiness (1.00) and sleep duration (1.00), while the machine learning models had the highest PPV for napping (0.95) and bad sleep quality (0.86), and LLAMA2 with finetuning had the highest PPV for night wakings (0.93) and sleep problem (0.89). Discussion Although sleep information is infrequently documented in the clinical notes, the proposed rule-based NLP algorithm and LLM-based NLP algorithms still achieved promising results. In comparison, the machine learning-based approaches did not achieve good results, which is due to the small size of sleep information in the training data. Conclusion The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts. This study focused on the clinical notes of patients with AD but could be extended to general sleep information extraction for other diseases. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Using Distribution Divergence to Predict Changes in the Performance of Clinical Predictive Models

Author

Tajgardoon, Mohammadamin, Visweswaran, Shyam, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tucker, Allan, editor, Henriques Abreu, Pedro, editor, Cardoso, Jaime, editor, Pereira Rodrigues, Pedro, editor, and Riaño, David, editor

Published
2021
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45. Accrual to Clinical Trials (ACT): A Clinical and Translational Science Award Consortium Network

Author

Visweswaran, Shyam, Becich, Michael J, D’Itri, Vincent S, Sendro, Elaina R, MacFadden, Douglas, Anderson, Nicholas R, Allen, Karen A, Ranganathan, Dipti, Murphy, Shawn N, Morrato, Elaine H, Pincus, Harold A, Toto, Robert, Firestein, Gary S, Nadler, Lee M, and Reis, Steven E

Subjects
Health Services and Systems, Health Sciences, Clinical Trials and Supportive Activities, Clinical Research, clinical trials, accrual, cohort discovery, clinical data research network, electronic health records, Health services and systems
Abstract

The Accrual to Clinical Trials (ACT) network is a federated network of sites from the National Clinical and Translational Science Award (CTSA) Consortium that has been created to significantly increase participant accrual to multi-site clinical trials. The ACT network represents an unprecedented collaboration among diverse CTSA sites. The network has created governance and regulatory frameworks and a common data model to harmonize electronic health record (EHR) data, and deployed a set of Informatics for Integrating Biology and the Bedside (i2b2) data repositories that are linked by the Shared Health Research Information Network (SHRINE) platform. It provides investigators the ability to query the network in real time and to obtain aggregate counts of patients who meet clinical trial inclusion and exclusion criteria from sites across the United States. The ACT network infrastructure provides a basis for cohort discovery and for developing new informatics tools to identify and recruit participants for multi-site clinical trials.

Published
2018

46. Machine Learning - Based Bleeding Risk Predictions in Atrial Fibrillation Patients on Direct Oral Anticoagulants

Author

Chaudhary, Rahul, primary, Nourelahi, Mehdi, additional, Thoma, Floyd, additional, Gellad, Walid F, additional, Lo-Ciganic, Wei-Hsuan, additional, Bliden, Kevin P, additional, Gurbel, Paul A, additional, Neal, Matthew D, additional, Jain, Sandeep K, additional, Bhonsale, Aditya, additional, Mulukutla, Suresh R, additional, Wang, Yanshan, additional, Harinstein, Matthew E, additional, Saba, Samir, additional, and Visweswaran, Shyam, additional

Published
2024
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47. Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion

Author

Estiri, Hossein, primary, Azhir, Alaleh, additional, Murphy, Shawn, additional, Hugel, Jonas, additional, Tian, Jiazi, additional, Bernstam, Elmer, additional, Klann, Jeffrey G., additional, Bassett, Ingrid, additional, Triant, Virginia, additional, Lau, Emily, additional, Farhat, Maha, additional, Henderson, Darren, additional, Visweswaran, Shyam, additional, Morris, Michele, additional, Cheng, Jingya, additional, Strasser, Zachary, additional, Semenov, Yevgeniy, additional, and Bell, Douglass, additional

Published
2024
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49. Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring

Author

Bao Tran, Tran Quoc, primary, Lip, Stefanie, additional, du Toit, Clea, additional, Kalaria, Tejas Kumar, additional, Bhaskar, Ravi K., additional, O’Neil, Alison Q., additional, Graff, Beata, additional, Hoffmann, Michał, additional, Szyndler, Anna, additional, Polonis, Katarzyna, additional, Wolf, Jacek, additional, Reddy, Sandeep, additional, Narkiewicz, Krzysztof, additional, Dasgupta, Indranil, additional, Dominiczak, Anna F., additional, Visweswaran, Shyam, additional, McCallum, Linsay, additional, and Padmanabhan, Sandosh, additional

Published
2024
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