arXiv research

Machine-assisted treatment recommendations hold a promise to reduce physician time and decision errors. We formulate the task as a sequence-to-sequence prediction model that takes the entire time-ordered medical history as input, and predicts a sequence of future clinical procedures and medications. It is built on the …

System suggests clinical concepts in real-time for faster note creation.

Wrist movements can reveal digits, posing security risks.

Labeling training datasets has become a key barrier to building medical machine learning models. One strategy is to generate training labels programmatically, for example by applying natural language processing pipelines to text reports associated with imaging studies. We propose cross-modal data programming, which gen…

Generates biomedical abstracts from titles, years, and keywords.

Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.

Clinical notes in electronic health records contain highly heterogeneous writing styles, including non-standard terminology or abbreviations. Using these notes in predictive modeling has traditionally required preprocessing (e.g. taking frequent terms or topic modeling) that removes much of the richness of the source d…

In this paper we do the first large scale analysis of writing style development among Danish high school students. More than 10K students with more than 100K essays are analyzed. Writing style itself is often studied in the natural language processing community, but usually with the goal of verifying authorship, assess…

Survival analysis models predict loan write-off risk under IFRS 9.

EagerPy simplifies writing code for multiple deep learning frameworks.

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. …

In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling. However, we observe that the attention based knowledge retrieval mechanisms us…

Two large medical dialogue datasets for improving healthcare.

Proposes guidelines for developing medical AI products.

We say that a knot $k_1$ in the $3$-sphere {\it $1$-dominates} another $k_2$ if there is a proper degree 1 map $E(k_1) \to E(k_2)$ between their exteriors, and write $k_1 \ge k_2$. When $k_1 \ge k_2$ but $k_1 \ne k_2$ we write $k_1 > k_2$. One expects in the latter eventuality that $k_1$ is more {\it complicated}. In t…

GRASP removes spurious correlations in fine-tuned models, improving task performance and reducing bias.

Study evaluates three position sizing methods for put-writing on S&P 500 Index options.

Automatically writing stylized Chinese characters is an attractive yet challenging task due to its wide applicabilities. In this paper, we propose a novel framework named Style-Aware Variational Auto-Encoder (SA-VAE) to flexibly generate Chinese characters. Specifically, we propose to capture the different characterist…

Automated system extracts medication regimens from medical conversations.

Memory-augmented neural networks consisting of a neural controller and an external memory have shown potentials in long-term sequential learning. Current RAM-like memory models maintain memory accessing every timesteps, thus they do not effectively leverage the short-term memory held in the controller. We hypothesize t…

Telescope detects LLM generated text by measuring token repetition probability.

SAGE improves memory efficiency by selectively adding, merging, or ignoring new facts.

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.

The paper presents methods to write presentations for Dehn quandles.

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.

Low-rank training improves neural network training on edge devices with non-volatile memory.

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.

The aim of this paper is to write an explicit orthonormal parallelization for all parallelizable products of spheres, using an explicit isomorphism with a trivial vector bundle.

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.

This article is the second part of the article we promised to write at the end of Section 1 of [FOOO15] (arXiv:1209.4410). (Part I appeared in [Part I] (arXiv:1503.07631).) We discuss the foundation of the virtual fundamental chain and cycle technique, especially its version that appeared in [FOn] and also in Section A…

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,…

We write the Dirac equation in curved 4-dimensional Lorentzian spacetime using concepts from the analysis of partial differential equations as opposed to geometric concepts.

Efficiently processes dynamic inputs in AI writing assistants with incremental computation.

Improves reliability of medical diagnosis uncertainty estimates.