A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
RL algorithms with medical integration improve personalized treatment recommendations.
Segmentation maps of medical images annotated by medical experts contain rich spatial information. In this paper, we propose to decompose annotation maps to learn disentangled and richer feature transforms for segmentation problems in medical images. Our new scheme consists of two main stages: decompose and integrate. …
Research on unique continuation principles in medical and seismic imaging.
Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automatically make lab test imputations and medication recommendations can save costs on potentially redundant lab tests and inform physicians of a more…
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existi…
ChatGPT's medical response accuracy is 56%, but studies vary widely.
Framework harmonizes EHR data across institutions for better analysis.
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
Enhances uncertainty estimation in medical image segmentation.
The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such large annotated datasets, as annotating medical images is not only tedious…
Bayes-CATSI uses variational Bayesian deep learning for medical time series data imputation.
New framework for managing medical risks using convex responses.
HRTPP improves TPP interpretability and accuracy in medical event modeling.
Sparse learning has been shown to be effective in solving many real-world problems. Finding sparse representations is a fundamentally important topic in many fields of science including signal processing, computer vision, genome study and medical imaging. One important issue in applying sparse representation is to find…
TorchIO simplifies medical image processing for deep learning.
Study clusters Kenyan medical insurance companies based on financial performance and reporting consistency.
Deep learning model improves X-ray disease detection accuracy in Thai patients.
The paper proposes a deep generative model for complex disease trajectories.
Simplifies decision-making during medical exams with cost-efficient feature acquisition.
In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making. By evaluating on the prediction of clinically relevant outcomes, we show that in a practical …
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). However, it becomes increasingly challenging to model such data, due to …
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
Teaches reproducible research to medical students and postgrads.
Proposes BONMI for integrating noisy matrices from multi-source data.
LHM integrates expert ODEs with neural ODEs for disease progression prediction.
Enhanced word embedding creates new consumer-friendly health terms.
CuMPerLay vectorizes CMP for deep learning, improving image analysis.
We study the problem of detecting adverse drug events in electronic healthcare records. The challenge in this work is to aggregate heterogeneous data types involving diagnosis codes, drug codes, as well as lab measurements. An earlier framework proposed for the same problem demonstrated promising predictive performance…
Improves medication name inference for telemedicine and conversational agents.
Novel fusion network combines polarization and radiomics features for liver cancer classification.
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. …
An integrated approach is proposed across visual and textual data to both determine and justify a medical diagnosis by a neural network. As deep learning techniques improve, interest grows to apply them in medical applications. To enable a transition to workflows in a medical context that are aided by machine learning,…
Data is one of the essential ingredients to power deep learning research. Small datasets, especially specific to medical institutes, bring challenges to deep learning training stage. This work aims to develop a practical deep multimodal that can classify patients into abnormal and normal categories accurately as well a…
Two large medical dialogue datasets for improving healthcare.
Proposes guidelines for developing medical AI products.
Study identifies risk factors for subsequent suicide attempts in youth.
This research improves deep neural networks for parameter identification and prediction in stochastic Volterra integral equations.
New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of…
The medical field stands to see significant benefits from the recent advances in deep learning. Knowing the uncertainty in the decision made by any machine learning algorithm is of utmost importance for medical practitioners. This study demonstrates the utility of using Bayesian LSTMs for classification of medical time…
Incorporating human domain knowledge for breast tumor diagnosis is challenging, since shape, boundary, curvature, intensity, or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach for integrating visual saliency into a deep learning model for breast t…
This paper benchmarks privacy-preserving machine learning on medical images.
Machine learning constructs problem-based medical records from electronic health records.
The analysis of mixed data has been raising challenges in statistics and machine learning. One of two most prominent challenges is to develop new statistical techniques and methodologies to effectively handle mixed data by making the data less heterogeneous with minimum loss of information. The other challenge is that …
Semantic segmentation is an established while rapidly evolving field in medical imaging. In this paper we focus on the segmentation of brain Magnetic Resonance Images (MRI) into cerebral structures using convolutional neural networks (CNN). CNNs achieve good performance by finding effective high dimensional image featu…
Enhances medical code predictions for multi-morbidity patients using text classification.
Paper detects bias in AI medical models using CART.
Study evaluates different saliency maps for CT image classification.
The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-question pairs could enable questions to be answered more effectively. While many research efforts have focused on the problem of general questio…