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.
Efficiently fine-tunes patient-independent seizure detection models with tensor kernel machine.
New model identifies patient-specific disease root causes.
New definition of patient-specific root causes of disease using counterfactuals.
FalconBC improves patient-specific cardiovascular modeling by estimating boundary conditions efficiently.
Identifies patient-specific root causes of disease using structural equation models.
Deep CNN-RNN model classifies breathing sounds for respiratory disease diagnosis.
Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models - known as individual survival distributions (ISDs) - produces patient-specific survival functions that offer greater descriptiv…
Personalized models explain TB treatment outcomes considering patient context.
Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to con…
Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparametric framework that has proven useful in jointly modeling the physiological states of patients in medical time series data. However, capturing the short-term effects of drugs and therapeutic interventions on patient physiological state remains chall…
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for …
Deep model integrates MRI and DTI for autism severity prediction.
The paper integrates AI and expert knowledge to optimize radiotherapy decisions.
Framework predicts clinical severity from rs-fMRI data using network optimization.
Hybrid pipeline detects spike-and-wave discharges in long-term EEG recordings.
Epilepsy is an important public health issue. An appropriate epileptiform discharge pattern detection of this neurological disease is a typical problem in biomedical engineering. In this paper, a new method is proposed for spike-and-wave discharge pattern detection based on Kendall's Tau-b coefficient. The proposed app…
Generative adversarial network system improves ECG arrhythmia classification.
Proposes a tool to contrast global vs personalized models in clinical prediction.
A deep learning framework segments deep cerebellar nuclei from 7T MRI, improving accuracy and consistency.
CLOCS uses contrastive learning to improve cardiac signal representations.
Proposes a framework for personalized treatment recommendations using observational data.
In this work we propose a method to compute continuous embeddings for kmers from raw RNA-seq data, without the need for alignment to a reference genome. The approach uses an RNN to transform kmers of the RNA-seq reads into a 2 dimensional representation that is used to predict abundance of each kmer. We report that our…
We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (…
Mathematical conditions and practical computations for adversarial robustness measures are established.
Personalized medicine seeks to identify the causal effect of treatment for a particular patient as opposed to a clinical population at large. Most investigators estimate such personalized treatment effects by regressing the outcome of a randomized clinical trial (RCT) on patient covariates. The realized value of the ou…
Missing data is a common problem in real-world settings and particularly relevant in healthcare applications where researchers use Electronic Health Records (EHR) and results of observational studies to apply analytics methods. This issue becomes even more prominent for longitudinal data sets, where multiple instances …
We present algorithms for the detection of a class of heart arrhythmias with the goal of eventual adoption by practicing cardiologists. In clinical practice, detection is based on a small number of meaningful features extracted from the heartbeat cycle. However, techniques proposed in the literature use high dimensiona…
LI-ITR combines flexible ML with interpretable approximations for personalized treatment rules.
In medicine, both ethical and monetary costs of incorrect predictions can be significant, and the complexity of the problems often necessitates increasingly complex models. Recent work has shown that changing just the random seed is enough for otherwise well-tuned deep neural networks to vary in their individual predic…
DeepCoDA provides personalized interpretability for complex health data.
New method learns DAG structure in clustered data, accounting for local variations.
Photoplethysmogram (PPG) is increasingly used to provide monitoring of the cardiovascular system under ambulatory conditions. Wearable devices like smartwatches use PPG to allow long term unobtrusive monitoring of heart rate in free living conditions. PPG based heart rate measurement is unfortunately highly susceptible…
New methods use RL and DA to improve dosing precision and reduce side effects.
SepVAE separates patient-specific patterns from healthy ones using contrastive VAE.
Proposes a method to estimate personalized treatments from high-dimensional data.
A new algorithm uses concavity in Gaussian processes to optimize decisions in bandit problems.
The distribution of absorbed dose in radionuclide therapy with Lu can be approximated by convolving an image of the time-integrated activity distribution with a dose voxel kernel representing different tissue types. This fast but inaccurate approximation is unsuitable for personalised dosimetry because it negle…
A new framework optimizes fMRI and behavioral data for better understanding of Autism.
Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To assess the effects of radiation therapy, treatment plans are typically simu…
The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a pre-specified outcome is of particular…
The paper estimates personalized treatment effects in medical settings with competing risks.
Deep learning models predict epileptic seizures with high accuracy.
Developing a Brain-Computer Interface~(BCI) for seizure prediction can help epileptic patients have a better quality of life. However, there are many difficulties and challenges in developing such a system as a real-life support for patients. Because of the nonstationary nature of EEG signals, normal and seizure patter…
In both the fields of computer science and medicine there is very strong interest in developing personalized treatment policies for patients who have variable responses to treatments. In particular, I aim to find an optimal personalized treatment policy which is a non-deterministic function of the patient specific cova…
NOTMAD estimates context-specific Bayesian networks without breaking datasets.
We consider a problem of ranking and selection via simulation in the context of personalized decision making, where the best alternative is not universal but varies as a function of some observable covariates. The goal of ranking and selection with covariates (R&S-C) is to use simulation samples to obtain a selection p…
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
Proposes a new Q-learning method for survival outcomes in clinical trials.