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.
Self-supervised learning improves ECG classification performance.
Self-supervised method improves biosignal models with limited labeled data and subjects.
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the non-sparseness of biosignals presents a major challenge to compressed sensing. This study…
Adversarial transfer learning improves stress assessment across users.
The effectiveness of biosignal generation and data augmentation with biosignal generative models based on generative adversarial networks (GANs), which are a type of deep learning technique, was demonstrated in our previous paper. GAN-based generative models only learn the projection between a random distribution as in…
AutoTransfer improves subject transfer learning for biosignal datasets.
Proposes a framework for extracting consistent physiological features across users.
This paper investigates the use of distributed processing on the problem of emotion recognition from physiological sensors using a popular machine learning library on distributed mode. Specifically, we run a random forests classifier on the biosignal-data, which have been pre-processed to form exclusive groups in an un…
The paper predicts diseases using both clinical and genomics data.
Clinical notes contain a large amount of clinically valuable information that is ignored in many clinical decision support systems due to the difficulty that comes with mining that information. Recent work has found success leveraging deep learning models for the prediction of clinical outcomes using clinical notes. Ho…
Clinical AI models fail to transfer between sites due to site-specific practices.
System suggests clinical concepts in real-time for faster note creation.
We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. However, most registration algorithms are designed for high-resolut…
Perceptrons are neuronal devices capable of fully discriminating linearly separable classes. Although straightforward to implement and train, their applicability is usually hindered by non-trivial requirements imposed by real-world classification problems. Therefore, several approaches, such as kernel perceptrons, have…
AI predicts medical specialty diagnostic choices from EHR records.
Novel metrics improve machine learning models for ICU patient care.
Machine learning predicts patient recruitment for clinical trials.
Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the longand irregular clinical event sequences. We make the observation that clinical events at a…
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinical research. Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic pr…
Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we …
Study develops a new algorithm for assessing clinical trial abstracts.
Improved negation detection in Dutch clinical texts using machine learning.
The paper explores using historical data to improve clinical trial analysis by optimizing covariate weights.
Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, …
Study examines biases in clinical word embeddings, revealing performance gaps across groups.
Study improves healthcare time series imputation by considering structured missingness.
New method detects biomarker-treatment interactions in clinical trials.
In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate to…
Improved language model for French clinical reports achieves state-of-the-art performance in medical NLP tasks.
The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opport…
Late fusion of clinical notes and physiological data improves ICU mortality prediction.
Deep learning analyzes healthcare provider actions and patient outcomes.
Discusses handling intercurrent events in clinical trials with time-to-event outcomes.
Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinical measurements. In this work, we propose an intervention-augmented deep state space generative model to capture the interactions among clin…
Framework improves clinical timeline reconstruction from text and tables.
Machine learning is bringing a paradigm shift to healthcare by changing the process of disease diagnosis and prognosis in clinics and hospitals. This development equips doctors and medical staff with tools to evaluate their hypotheses and hence make more precise decisions. Although most current research in the literatu…
Unified model combines neural networks and dictionary learning for clinical predictions from brain data.
Current clinical practice to monitor patients' health follows either regular or heuristic-based lab test (e.g. blood test) scheduling. Such practice not only gives rise to redundant measurements accruing cost, but may even lead to unnecessary patient discomfort. From the computational perspective, heuristic-based test …
Clinical notes are a rich source of information about patient state. However, using them to predict clinical events with machine learning models is challenging. They are very high dimensional, sparse and have complex structure. Furthermore, training data is often scarce because it is expensive to obtain reliable labels…
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
Method predicts biomarker trajectories with uncertainty bands for Alzheimer's disease.
Predictive models identify patients at risk of severe COVID-19.
Causal ML predicts treatment outcomes, aiding personalized medicine.
Clinical trials in the medical domain are constrained by budgets. The number of patients that can be recruited is therefore limited. When a patient population is heterogeneous, this creates difficulties in learning subgroup specific responses to a particular drug and especially for a variety of dosages. In addition, pa…
Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), predictive models that can effectively deal with clinical sequential data increase the possibility to predict sepsis and take early preventiv…
Convolutional neural network improves assertion detection in multi-label clinical text.
SEEDA optimizes dose allocation in clinical trials to balance efficacy and safety.
A novel dose-finding design for cancer clinical trials using level set estimation.