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.
AI predicts medical specialty diagnostic choices from EHR records.
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…
Research simulates Lloyd's of London's specialty insurance market dynamics.
Deep learning has been successfully applied to a variety of image classification tasks. There has been keen interest to apply deep learning in the medical domain, particularly specialties that heavily utilize imaging, such as ophthalmology. One issue that may hinder application of deep learning to the medical domain is…
Mnay models situated in the current research landscape of modelling and simulating social processes have roots in physics. This is visible in the name of specialties as Econophysics or Sociophysics. This chapter describes the history of knowledge transfer from physics, in particular physics of self-organization and evo…
We develop a unifed theory to study geometry of manifolds with different holonomy groups. They are classified by (1) real, complex, quaternion or octonion number they are defined over and (2) being special or not. Specialty is an orientation with respect to the corresponding normed algebra A. For example, special Riema…
Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust. Explainability, or the ability of an ML model to justify its outcomes and assist clinicians in rationalizing the model prediction, has been generally understood to be critical to establishing trust. Howeve…
Improves medication name inference for telemedicine and conversational agents.
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. …
Two large medical dialogue datasets for improving healthcare.
Proposes guidelines for developing medical AI products.
Automated system extracts medication regimens from medical conversations.
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…
This paper benchmarks privacy-preserving machine learning on medical images.
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…
Machine learning constructs problem-based medical records from electronic health records.
Bayesian optimization speeds up bioprocess development across scales.
Enhances medical code predictions for multi-morbidity patients using text classification.
Paper detects bias in AI medical models using CART.
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…
Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that h…
This study applies neural models to automatically recognize medical entities from natural language.
Survey of deep learning methods for medical anomaly detection.
MedGraph learns patient visit embeddings from EMRs, capturing both attributes and temporal sequences.
Recent years have witnessed the emergence of 3D medical imaging techniques with the development of 3D sensors and technology. Due to the presence of noise in image acquisition, registration researchers focused on an alternative way to represent medical images. An alternative way to analyze medical imaging is by underst…
Unified deep learning predicts Parkinson's disease from medical images.
Privacy-preserving deep learning for medical data across distributed platforms.
RL algorithms with medical integration improve personalized treatment recommendations.
Review of deep learning methods in medical image registration.
IAGAN method improves medical image reconstruction by incorporating adaptive GAN priors.
Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset,…
Improves reliability of medical diagnosis uncertainty estimates.
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize imag…
Big data, data science, deep learning, artificial intelligence are the key words of intense hype related with a job market in full evolution, that impose to adapt the contents of our university professional trainings. Which artificial intelligence is mostly concerned by the job offers? Which methodologies and technolog…
This paper tackles label noise in deep learning for medical image analysis.
DiffDenoise preserves fine structures in medical images using conditional diffusion models.
Paper detects biases in medical imaging ML models using counterfactual analysis.
This paper reviews deep learning for multi-modality medical image segmentation.
Automatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare informatics that turns heterogeneous medical records into structured and actionable information. Here we propose ME2Vec, an algorithmic framework for learning low-dimensional vectors of the most co…
A new metric FRD improves comparing medical images.
A deep learning approach classifies medical images hierarchically.
Proposes a new method for medical diagnosis using network-based representation learning.
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established…
This paper addresses privacy in federated learning for medical imaging by estimating model uncertainty.
This work provides a strong baseline for the problem of multi-source multi-target domain adaptation and generalization in medical imaging. Using a diverse collection of ten chest X-ray datasets, we empirically demonstrate the benefits of training medical imaging deep learning models on varied patient populations for ge…
Generative models solve medical imaging inverse problems without needing paired data.
Paper proposes a new method to handle missing data in medical records using sequential variational autoencoders.
PAC-Bayesian method improves deep learning generalization in medical imaging.