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.
Ancestry improves genealogy search results by ranking diverse record types.
SODA-RL learns diverse treatment options for hypotension from data.
ME2Vec learns medical entity vectors from EHR data.
Biodiversity monitoring using audio recordings is achievable at a truly global scale via large-scale deployment of inexpensive, unattended recording stations or by large-scale crowdsourcing using recording and species recognition on mobile devices. The ability, however, to reliably identify vocalising animal species is…
Bayesian model predicts patient survival from sparse EHR data.
Enhances detection of adverse drug events using diverse healthcare record data.
A machine learning approach to record fusion with high accuracy.
Synthesizes computational approaches to understand neural timescales.
Framework harmonizes EHR data across institutions for better analysis.
PEHRT harmonizes EHR data for translational research.
Dataset for object detection at Oktoberfest beer tent.
Proposes Ada-Sit method for mortality prediction of rare diseases.
New method generates diverse EHR data types while maintaining privacy.
New dataset for evaluating speech recognition fairness across demographics.
CREIMBO models diverse brain activity by identifying hidden neural sub-circuits and their non-stationary interactions.
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 (…
Introduces LDM to estimate machine learning algorithm capacity.
Unified model of urban consumer behavior and mobility patterns.
EcoCast predicts biodiversity risks using satellite data and citizen science records.
Conventional modeling approaches have found limitations in matching the increasingly detailed neural network structures and dynamics recorded in experiments to the diverse brain functionalities. On another approach, studies have demonstrated to train spiking neural networks for simple functions using supervised learnin…
Predicts user next location using CDR data.
Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems. External resources such as medical ontologies are used to bridge the da…
Study finds multi-task learning and pre-training can improve healthcare models on EHR data.
The paper extracts structured data from physician-patient conversations, reducing clerical burden.
Deep learning model creates patient representations for scalable EHR-based stratification.
In recent years, reinforcement learning (RL) methods have been applied to model gameplay with great success, achieving super-human performance in various environments, such as Atari, Go, and Poker. However, those studies mostly focus on winning the game and have largely ignored the rich and complex human motivations, w…
Obesity is a serious public health concern world-wide, which increases the risk of many diseases, including hypertension, stroke, and type 2 diabetes. To tackle this problem, researchers across the health ecosystem are collecting diverse types of data, which includes biomedical, behavioral and activity, and utilizing m…
We study the statistics of record-breaking events in daily stock prices of 366 stocks from the Standard and Poors 500 stock index. Both the record events in the daily stock prices themselves and the records in the daily returns are discussed. In both cases we try to describe the record statistics of the stock data with…
The paper provides bounds and methods for estimating causal effects from observational data.
XGBoost outperforms other models in predicting housing prices.
Record linkage involves merging records in large, noisy databases to remove duplicate entities. It has become an important area because of its widespread occurrence in bibliometrics, public health, official statistics production, political science, and beyond. Traditional linkage methods directly linking records to one…
We review recent advances on the record statistics of strongly correlated time series, whose entries denote the positions of a random walk or a Lévy flight on a line. After a brief survey of the theory of records for independent and identically distributed random variables, we focus on random walks. During the last few…
The study of record statistics of correlated series is gaining momentum. In this work, we study the records statistics of the time series of select stock market data and the geometric random walk, primarily through simulations. We show that the distribution of the age of records is a power law with the exponent lyi…
Deep architecture learns transferable features for robust speech emotion recognition.
New approach protects privacy of deleted records in machine learning.
We present a probabilistic method for linking multiple datafiles. This task is not trivial in the absence of unique identifiers for the individuals recorded. This is a common scenario when linking census data to coverage measurement surveys for census coverage evaluation, and in general when multiple record-systems nee…
Timely prediction of clinically critical events in Intensive Care Unit (ICU) is important for improving care and survival rate. Most of the existing approaches are based on the application of various classification methods on explicitly extracted statistical features from vital signals. In this work, we propose to elim…
Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos or neural recordings, are now a common feature of modern data analysis. Explaining the phenomena underlying these diverse data sets requires flexible and accurate models. In this paper, we promote Gaussian pro…
Deep learning detects sepsis early, improving patient outcomes.
Paper proposes a method to locate power grid recordings using ENF sequences.
KT models improved slightly with synthetic student data.
Paper uses ensemblers to predict sepsis early from patient records.
We consider the occurrence of record-breaking events in random walks with asymmetric jump distributions. The statistics of records in symmetric random walks was previously analyzed by Majumdar and Ziff and is well understood. Unlike the case of symmetric jump distributions, in the asymmetric case the statistics of reco…
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that …
Systematic review of multimodal data challenges and solutions.
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods ha…
Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospi…