Wei-Hung Weng
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Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
Sebastien Baur
Christina Chen
Mariam Jabara
Babak Behsaz
Shravya Shetty
Goodarz Danaei
Diego Ardila
PLOS Global Public Health, 4(6) (2024), e0003204
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Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. We investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction. We developed a deep learning PPG-based CVD risk score (DLS) to predict the probability of having major adverse cardiovascular events (MACE: non-fatal myocardial infarction, stroke, and cardiovascular death) within ten years, given only age, sex, smoking status and PPG as predictors. We compare the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort. All models were trained on a development dataset (141,509 participants) and evaluated on a geographically separate test (54,856 participants) dataset, both from UKB. DLS’s C-statistic (71.1%, 95% CI 69.9–72.4) is non-inferior to office-based refit-WHO score (70.9%, 95% CI 69.7–72.2; non-inferiority margin of 2.5%, p<0.01) in the test dataset. The calibration of the DLS is satisfactory, with a 1.8% mean absolute calibration error. Adding DLS features to the office-based score increases the C-statistic by 1.0% (95% CI 0.6–1.4). DLS predicts ten-year MACE risk comparable with the office-based refit-WHO score. Interpretability analyses suggest that the DLS-extracted features are related to PPG waveform morphology and are independent of heart rate. Our study provides a proof-of-concept and suggests the potential of a PPG-based approach strategies for community-based primary prevention in resource-limited regions.
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An intentional approach to managing bias in embedding models
Atilla P. Kiraly
Jungyeon Park
Rory Pilgrim
Charles Lau
Heather Cole-Lewis
Shravya Shetty
Krish Eswaran
Leo Anthony Celi
The Lancet Digital Health, 6 (2024), E126-E130
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Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuation, or exacerbation of care disparities. Reinforcing these concerns is the finding that medical images often reveal signals about sensitive attributes in ways that are hard to pinpoint by both algorithms and people. This finding raises a question about how to best design general purpose pretrained embeddings (GPPEs, defined as embeddings meant to support a broad array of use cases) for building downstream models that are free from particular types of bias. The downstream model should be carefully evaluated for bias, and audited and improved as appropriate. However, in our view, well intentioned attempts to prevent the upstream components—GPPEs—from learning sensitive attributes can have unintended consequences on the downstream models. Despite producing a veneer of technical neutrality, the resultant end-to-end system might still be biased or poorly performing. We present reasons, by building on previously published data, to support the reasoning that GPPEs should ideally contain as much information as the original data contain, and highlight the perils of trying to remove sensitive attributes from a GPPE. We also emphasise that downstream prediction models trained for specific tasks and settings, whether developed using GPPEs or not, should be carefully designed and evaluated to avoid bias that makes models vulnerable to issues such as distributional shift. These evaluations should be done by a diverse team, including social scientists, on a diverse cohort representing the full breadth of the patient population for which the final model is intended.
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Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals
Louis Blankemeier
Sebastien Baur
Diego Ardila
arXiv (2023)
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders
Shawn Xu
Lin Yang
Christopher Kelly
Timo Kohlberger
Martin Ma
Atilla Kiraly
Sahar Kazemzadeh
Zakkai Melamed
Jungyeon Park
Patricia MacWilliams
Chuck Lau
Christina Chen
Mozziyar Etemadi
Sreenivasa Raju Kalidindi
Kat Chou
Shravya Shetty
Daniel Golden
Rory Pilgrim
Krish Eswaran
arxiv (2023)
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Our approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, leverages a language-aligned image encoder combined or grafted onto a fixed LLM, PaLM 2, to perform a broad range of tasks. We train this lightweight adapter architecture using images paired with corresponding free-text radiology reports from the MIMIC-CXR dataset. ELIXR achieved state-of-the-art performance on zero-shot chest X-ray (CXR) classification (mean AUC of 0.850 across 13 findings), data-efficient CXR classification (mean AUCs of 0.893 and 0.898 across five findings (atelectasis, cardiomegaly, consolidation, pleural effusion, and pulmonary edema) for 1% (~2,200 images) and 10% (~22,000 images) training data), and semantic search (0.76 normalized discounted cumulative gain (NDCG) across nineteen queries, including perfect retrieval on twelve of them). Compared to existing data-efficient methods including supervised contrastive learning (SupCon), ELIXR required two orders of magnitude less data to reach similar performance. ELIXR also showed promise on CXR vision-language tasks, demonstrating overall accuracies of 58.7% and 62.5% on visual question answering and report quality assurance tasks, respectively. These results suggest that ELIXR is a robust and versatile approach to CXR AI.
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Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards majority classes and ignore the importance of the rest. This is especially true for dermatology, a specialty with thousands of skin conditions but many of which rarely occur in the wild. Buoyed by recent advances, we explore meta-learning based few-shot learning approaches in skin condition recognition problem and propose an evaluation setup to fairly assess the real-world impact of such approaches. When compared to conventional class imbalance techniques, we find that the state-of-the-art few-shot learning methods are not as performant, but combining the two approaches using a novel ensemble leads to improvement in all-way classification, especially the rare classes. We conclude that the ensemble can be useful to address the class imbalance problem, yet progress here can further be accelerated by the use of real-world evaluation setups for benchmarking new methods.
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Metadata are general characteristics of the data in a well-curated and condensed format, and have been proven to be useful for decision making, knowledge discovery, and also heterogeneous data organization of biobank. Among all data types in the biobank, pathology is the key component of the biobank and also serves as the gold standard of diagnosis. To maximize the capability of biobank and allow the rapid progress of biomedical science, utilizing the pathology metadata is essential yet require enormous expert effort to annotate due to the unstructured nature and complexity of pathology information. In the study, we develop a multimodal multitask learning framework that learns generalizable representations of pathology data to predict four major biobank metadata of the pathology images. We demonstrate that incorporating multimodal information, such as texts and case-level categorical data, improves the metadata prediction performance while multiple downstream tasks are considered simultaneously. Such pathology metadata prediction system may be adopted to mitigate the expert effort of manual annotation and ultimately accelerate the data-driven research by better utilization of the pathology biobank.
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Human-centric Metric for Accelerating Pathology Reports Annotation
Ruibin Ma
Cameron Chen
Gang Li
Angela Lin
Krishna Kumar Gadepalli
Yuannan Cai
arXiv (2019)
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Pathology medical reports written by physicians contain useful class information such as the main organ type, disease type, etc. These class information can be used for large-scale statistical analysis or labelling data in other modalities such as pathology slices (images). However, manual classification for a huge number of reports on multiple tasks are very inefficient. Moreover, they are very hard to read for non-professionals. In this paper, we investigate a general-purpose NLP model called BERT on multilabel text classification. We test it on five different classification tasks and achieve good discrimination. More importantly, we evaluate it under practical situation by measuring how much human labor on annotation can be saved and the performance on automatically classified cases.
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