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.
Proposes guidelines for developing medical AI products.
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…
Paper shows how to quantify uncertainty in medical ML models.
This chapter introduces reproducibility in machine learning for medical imaging.
Paper detects bias in AI medical models using CART.
Teaches reproducible research to medical students and postgrads.
Deep learning predicts ICU mortality with enhanced interpretability.
The study evaluates AI model performance measures for medical use.
DiffDenoise preserves fine structures in medical images using conditional diffusion models.
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…
COMPASS improves uncertainty quantification for medical segmentation metrics.
Paper detects biases in medical imaging ML models using counterfactual analysis.
We propose a representation learning framework for medical diagnosis domain. It is based on heterogeneous network-based model of diagnostic data as well as modified metapath2vec algorithm for learning latent node representation. We compare the proposed algorithm with other representation learning methods in two practic…
The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field. Currently, deep learning has became a dominant methodology for supporting pathologists and radiologist. Deep learning algorithms have been successfully applied to digital pathology and radiology, nevertheless, there are st…
Is it true that patients with similar conditions get similar diagnoses? In this paper we show NLP methods and a unique corpus of documents to validate this claim. We (1) introduce a method for representation of medical visits based on free-text descriptions recorded by doctors, (2) introduce a new method for clustering…
Prediction models can harm patients even when accurate, leading to self-fulfilling prophecies.
Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in practice: 1) only limited labeled samples are available due to expensive annotation costs over medical images; 2) labeled images may contain considerable label noises (e.g., mi…
This paper benchmarks OoDD methods for medical imaging.
Enhances uncertainty estimation in medical image segmentation.
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events usi…
The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems. In this paper, we demonstrate that adversarial examples are capable of manipulating deep learning systems across three clinical domains. For each of our representative medical deep learning classifiers, bo…
Foundation models alter medical data science workflow, challenging veridical data science principles.
This paper uses neural networks to accurately model competing risks in survival analysis.
Study clusters Kenyan medical insurance companies based on financial performance and reporting consistency.
Paper proposes a human-algorithm approach to reduce medical device recall risk and workload.
Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to hav…
New method explains survival analysis models using median-SHAP.
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations ( hr), designed the task to represent the real word scenario, and explored several alignment approaches to iteratively improve data qua…
Deep neural networks have been proved efficient for medical image denoising. Current training methods require both noisy and clean images. However, clean images cannot be acquired for many practical medical applications due to naturally noisy signal, such as dynamic imaging, spectral computed tomography, arterial spin …
In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making. By evaluating on the prediction of clinically relevant outcomes, we show that in a practical …
A new method relaxes Boolean Matrix Factorization to make it more efficient.
Paper tackles missing data in medical records using advanced optimization.
Machine learning detects NASH patients from medical claims data.
GANs help create realistic synthetic health data, boosting medical research.
New DAM method improves AUC scores in medical image classification.
We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is…
The Intensive Care Unit (ICU) is a hospital department where machine learning has the potential to provide valuable assistance in clinical decision making. Classical machine learning models usually only provide point-estimates and no uncertainty of predictions. In practice, uncertain predictions should be presented to …
Study uses multi-task Bayesian optimization to speed up SVM hyperparameter tuning for nodules diagnosis.
New method combines multiple datasets to estimate ATE with valid confidence intervals.
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they have been trained. However, it is practically infeasible to obtain sufficient training data for every human condition, and once deployed such …
FEET protocol evaluates foundation models across three scenarios.
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. …
Zero-Shot Learning helps learn new concepts without examples, useful for COVID-19 diagnosis.
Estimates causal effects from patient trajectories using DeepACE model.
Data is one of the essential ingredients to power deep learning research. Small datasets, especially specific to medical institutes, bring challenges to deep learning training stage. This work aims to develop a practical deep multimodal that can classify patients into abnormal and normal categories accurately as well a…
Two large medical dialogue datasets for improving healthcare.
Underspecified ML models can behave unpredictably in real-world use.