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.
CovidCare uses EMR data to predict patient outcomes in emerging epidemics.
Deep learning predicts patient trajectories in open Mimic-III dataset.
Predicting highrisk vascular diseases is a significant issue in the medical domain. Most predicting methods predict the prognosis of patients from pathological and radiological measurements, which are expensive and require much time to be analyzed. Here we propose deep attention models that predict the onset of the hig…
Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. To achieve this, deep learning methods need to be promoted from the level of mere associations to being able to answer causal questions. We present a scenario with real-world medical image…
We explore how Deep Learning (DL) can be utilized to predict prognosis of acute myeloid leukemia (AML). Out of TCGA (The Cancer Genome Atlas) database, 94 AML cases are used in this study. Input data include age, 10 common cytogenetic and 23 most common mutation results; output is the prognosis (diagnosis to death, DTD…
Early diagnosis is important for type 2 diabetes (T2D) to improve patient prognosis, prevent complications and reduce long-term treatment costs. We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers. We used expenditure data related t…
New model identifies cell-specific genes for cancer prognosis.
Proposes using external data to improve predictions in medical applications with limited samples.
Novel fusion network combines polarization and radiomics features for liver cancer classification.
Deep ROC analysis improves model selection and interpretation in medical and AI applications.
Quantitative CT predicts ILD patterns and prognosis.
The digitalization of stored information in hospitals now allows for the exploitation of medical data in text format, as electronic health records (EHRs), initially gathered for other purposes than epidemiology. Manual search and analysis operations on such data become tedious. In recent years, the use of natural langu…
HBR improves normative modeling of neuroimaging data across multiple sites.
Deep feature fusion improves mitosis counting accuracy.
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer pr…
Modeling continuous-time physiological processes that manifest a patient's evolving clinical states is a key step in approaching many problems in healthcare. In this paper, we develop the Hidden Absorbing Semi-Markov Model (HASMM): a versatile probabilistic model that is capable of capturing the modern electronic healt…
Generative models create synthetic MRI brain scans for research.
Deep learning models predict postoperative complications more accurately than random forests.
A data-driven approach predicts morphological development under structural instability.
Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. Materials and Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The sear…
Classification-as-a-Service (CaaS) is widely deployed today in machine intelligence stacks for a vastly diverse set of applications including anything from medical prognosis to computer vision tasks to natural language processing to identity fraud detection. The computing power required for training complex models on l…
Deep learning clusters patient time-series data for better prognosis.
Digital pathology is not only one of the most promising fields of diagnostic medicine, but at the same time a hot topic for fundamental research. Digital pathology is not just the transfer of histopathological slides into digital representations. The combination of different data sources (images, patient records, and *…
SurvCORN predicts survival curves using conditional ordinal ranking networks.
Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS). However, translating NGS data faces several challenges. First, NGS count data are often overdispersed, requiring appropriate modeling. Se…
Proposes a new framework for evaluating diagnostic models with multiple co-primary endpoints.
CN-SBM clusters cancer samples and regions based on copy number variants.
Improves medication name inference for telemedicine and conversational agents.
Proposes ConRad model for lung cancer classification using radiomics and interpretable machine learning.
ITCA optimizes label combination for ambiguous outcomes in multi-class classification.
Fast and cheaper next generation sequencing technologies will generate unprecedentedly massive and highly-dimensional genomic and epigenomic variation data. In the near future, a routine part of medical record will include the sequenced genomes. A fundamental question is how to efficiently extract genomic and epigenomi…
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. …
A clinical Meta-Dataset from TCGA for multi-task learning.
Two large medical dialogue datasets for improving healthcare.
Proposes guidelines for developing medical AI products.
Paper tackles medical question similarity using domain-relevant embeddings.
Automated system extracts medication regimens from medical conversations.
Machine learning models for COVID-19 detection and prognosis from chest images are flawed and unreliable.
A GMM-based method generates new 3D structures from medical images.
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.
ME2Vec learns medical entity vectors from EHR data.
Enhances medical code predictions for multi-morbidity patients using text classification.
Paper detects bias in AI medical models using CART.
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…
We present a new microscopic stochastic model for an ensemble of interacting investors that buy and sell stocks in discrete time steps via limit orders based on individual forecasts about the price of the stock. These orders determine the supply and demand fixing after each round (time step) the new price of the stock …