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.
This study shows unstructured clinical notes can improve mortality prediction.
Deep learning predicts heart failure readmission from clinical notes.
Late fusion of clinical notes and physiological data improves ICU mortality prediction.
Improved model predicts ICU readmission and mortality with interpretable results.
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 these notes we describe heuristics to predict computational-to-statistical gaps in certain statistical problems. These are regimes in which the underlying statistical problem is information-theoretically possible although no efficient algorithm exists, rendering the problem essentially unsolvable for large instances…
Deep learning predicts ICU mortality with enhanced interpretability.
Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI. We developed data-driven prediction models to estimate…
This note introduces the method of cross-conformal prediction, which is a hybrid of the methods of inductive conformal prediction and cross-validation, and studies its validity and predictive efficiency empirically.
The paper improves polyphonic music models by extracting salient features.
These notes survey and explore an emerging method, which we call the low-degree method, for predicting and understanding statistical-versus-computational tradeoffs in high-dimensional inference problems. In short, the method posits that a certain quantity -- the second moment of the low-degree likelihood ratio -- gives…
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…
We advance the state of the art in polyphonic piano music transcription by using a deep convolutional and recurrent neural network which is trained to jointly predict onsets and frames. Our model predicts pitch onset events and then uses those predictions to condition framewise pitch predictions. During inference, we r…
We develop a model using deep learning techniques and natural language processing on unstructured text from medical records to predict hospital-wide -day unplanned readmission, with c-statistic . Our model is constructed to allow physicians to interpret the significant features for prediction.
Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, ho…
This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annot…
The study uncovers invariant features in healthcare models that traditional methods overlook.
Recent decades have seen an interest in prediction problems for which Bayesian methodology has been used ubiquitously. Sampling from or approximating the posterior predictive distribution in a Bayesian model allows one to make inferential statements about potentially observable random quantities given observed data. Th…
BERT-XML automates ICD coding from EHR notes using BERT pretraining.
Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on…
We show how to adjust the coefficient of determination () when used for measuring predictive accuracy via leave-one-out cross-validation.
SAFER improves personalized treatment recommendations for dynamic clinical contexts.
Modeling investor behavior from financial advisor notes using NLP.
Proposes a new method for decision-aware learning in optimization.
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We …
A note proves the binary perceptron's capacity is less than 0.847.
Community moderation drifts towards majority, study finds.
Neural networks predict shapes of first passage percolation sets.
Short proof shows how ridge regression works with random data.
PPBoot simplifies prediction-powered inference.
The study analyzes and benchmarks graph conformal prediction methods.
Databases of electronic health records (EHRs) are increasingly used to inform clinical decisions. Machine learning methods can find patterns in EHRs that are predictive of future adverse outcomes. However, statistical models may be built upon patterns of health-seeking behavior that vary across patient subpopulations, …
Improves comparison of F-measures for imbalanced datasets.
We show that Dehn filling on the manifold results in a non-orderable space for all rational slopes in the interval . This is consistent with the L-space conjecture, which predicts that all fillings will result in a non-orderable space for this manifold.
We provide various formulations of knot homology that are predicted by string dualities. In addition, we also explain the rich algebraic structure of knot homology which can be understood in terms of geometric representation theory in these formulations. These notes are based on lectures in the workshop "Physics and Ma…
Recently, many regularized procedures have been proposed for variable selection in linear regression, but their performance depends on the tuning parameter selection. Here a criterion for the tuning parameter selection is proposed, which combines the strength of both stability selection and cross-validation and therefo…
A note on learning with agents having global perspectives and a principal optimizing their performance.
Deep Autotuner corrects singing pitch using neural networks.
Modern ML methods show unexpected behaviors that contradict classical statistics.
The study examines how class imbalance affects precision-recall curves.
We consider in this paper some structured financial products, known as reverse convertible notes, that resulted in substantial losses to certain buyers of these notes in recent years. We shall focus on specific reverse convertible notes known as "Autocallable Optimization Securities with Contingent Protection Linked to…
Predicts Bitcoin price using Twitter sentiment analysis.
Lecture notes on Finslerian geometry.
We use machine learning for designing a medium frequency trading strategy for a portfolio of 5 year and 10 year US Treasury note futures. We formulate this as a classification problem where we predict the weekly direction of movement of the portfolio using features extracted from a deep belief network trained on techni…
Study finds limit points of bass notes on hyperbolic surfaces.
LINTEL improves INTEL's time series prediction by optimizing computation and accuracy.
The article derives a formula for predicting claims uncertainty using the GCC method.
Electronic Health Records (EHRs) have been heavily used to predict various downstream clinical tasks such as readmission or mortality. One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature. Although recent advances in deep learning (DL…