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…
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48 results for “record data”
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
problem Lack of historical information in single-record patients hinders modeling rare medical events.
method Hybrid framework combining supervised and unsupervised learning to integrate concurrent and single-record data.
result Single-record data and concurrent diagnoses provide valuable information for improving suicide risk modeling.
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…
New approach protects privacy of deleted records in machine learning.
problem Privacy of deleted records in machine learning models.
method Sound deletion guarantee and noisy gradient descent algorithm.
result Privacy of existing records is necessary for deleted records' privacy.
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…
A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedde…
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…
DNI recovers missing brain data from corrupted recordings.
problem Corrupted neural recordings from multielectrode systems.
method Deep Neural Imputation framework using autoencoders.
result DNI recovers both time series and frequency content from corrupted data.
Genealogy research is the study of family history using available resources such as historical records. Ancestry provides its customers with one of the world's largest online genealogical index with billions of records from a wide range of sources, including vital records such as birth and death certificates, census re…
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…
Deep models for financial transactions are vulnerable to adversarial attacks, especially when adding transaction tokens.
problem Vulnerability of deep models to adversarial attacks on financial transaction records.
method Examine adversarial attacks and defenses on transaction records data, considering black-box attacks and adding transaction tokens.
result A few generated transactions can fool a deep-learning model, highlighting the need for robustness improvements.
Method reconstructs glacier front trajectories from record moraine data.
problem Understanding past glacier dynamics from limited record data.
method Stochastic generator based on Brownian motion and NBI hyper parameter tuning.
result Reconstructed glacier front trajectories from moraine records.
XLabel tool reduces medical experts' workload by 40% and explains its decisions.
problem Efficiently labeling large electronic health records.
method Visual-interactive tool using Explainable Boosting Machine (EBM) for classification and explanation.
result EBM achieves high accuracy and explainability, even with mislabeled data.
Bayesian approach for multifile record linkage and duplicate detection.
problem Challenges in merging overlapping datafiles with duplicates.
method Bayesian approach with novel partition representation and loss functions.
result Proposes a flexible prior for partitions and uncertain unresolved portions.
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 …
ODVICE augments EHR cohorts using ontology to improve analysis robustness.
problem Limited records in cohorts for rare diseases hamper robust analysis.
method Ontology-driven Monte-Carlo graph spanning algorithm for data augmentation.
result ODVICE augmented cohorts show ~30% improvement in AUC over non-augmented datasets.
Machine learning constructs problem-based medical records from electronic health records.
problem Difficulty in finding relevant medical information for clinical questions.
method Knowledge base completion using machine learning on electronic health records.
result Automatic construction of problem-based groupings of medications, procedures, and lab tests.
The task of matching co-referent records is known among other names as rocord linkage. For large record-linkage problems, often there is little or no labeled data available, but unlabeled data shows a reasonable clear structure. For such problems, unsupervised or semi-supervised methods are preferable to supervised met…
Paper tackles missing data in medical records using advanced optimization.
problem Missing data in medical records hinders machine learning applications.
method Applied multi-level stochastic optimization for imputation.
result The method significantly outperforms current approaches and reduces error up to 75%.
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…
Visual analytics system for comparing medical records using sequence embeddings.
problem Challenges in analyzing medical records due to high dimensionality, irregularity, and sparsity.
method Event and sequence embeddings using autoencoder and self-attention mechanism, with sequence alignment for comparison.
result Demonstrated effectiveness with real-world neonatal ICU dataset.
Motivated by the need of the linking records across various databases, we propose a novel graphical model based classifier that uses a mixture of Poisson distributions with latent variables. The idea is to derive insight into each pair of hypothesis records that match by inferring its underlying latent rate of error us…
In this paper we present a method for the unsupervised clustering of high-dimensional binary data, with a special focus on electronic healthcare records. We present a robust and efficient heuristic to face this problem using tensor decomposition. We present the reasons why this approach is preferable for tasks such as …
Two solutions for multi-modal record linkage using Deep Learning inspired by Visual Question Answering.
problem Matching records from multiple sources representing the same entity.
method Two fusion modules: Recurrent Neural Network + Convolutional Neural Network and Stacked Attention Network. A Siamese Neural Network computes similarity.
result Recurrent Neural Network + Convolutional Neural Network fusion module outperforms a simple model.
Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.
problem Estimating optimal treatment regimes from electronic medical records.
method Imputation-based semi-supervised method using unlabeled data.
result Proposed method yields more efficient estimators of optimal treatment regimes.
Fetal ECG (FECG) telemonitoring is an important branch in telemedicine. The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable. As an emerging technique, compressed sensing (CS) shows great promise in compressing/reconstructing data with…
Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning. We study this problem in the context of machine listening (Task 1b of the DCASE 2019 Challenge). We propose a novel approach to learn domain-invariant classifiers in a…
A methodology for binary classification of EEG records which correspond to different mental states is proposed. This model-free methodology is based on our theory of the -complexity of continuous functions which is extended here (see Appendix) to the case of vector functions. This extension permits us to handle mult…
De-identification of clinical records is an extremely important process which enables the use of the wealth of information present in them. There are a lot of techniques available for this but none of the method implementation has evaluated the scalability, which is an important benchmark. We evaluated numerous deep le…
Developing electrophysiological recordings of brain neuronal activity and their analysis provide a basis for exploring the structure of brain function and nervous system investigation. The recorded signals are typically a combination of spikes and noise. High amounts of background noise and possibility of electric sign…
This paper presents an example of how demographical characteristics of patients influence their susceptibility to certain medical conditions. In this paper, we investigate the association of health conditions to age of patients in a heterogeneous population. We show that besides the symptoms a patients is having, the a…
Big data from phone calls improves credit scoring models and profits.
problem Improving credit scoring models to enhance financial inclusion.
method Combining call-detail records and traditional data to build scorecards using social network analytics.
result Combining call-detail records with traditional data significantly increases model performance and profit.
The -means algorithm is arguably the most popular nonparametric clustering method but cannot generally be applied to datasets with incomplete records. The usual practice then is to either impute missing values under an assumed missing-completely-at-random mechanism or to ignore the incomplete records, and apply the …
Meta-Dynamic models learn shared neural dynamics across tasks.
problem Learning latent dynamics from neural recordings across different tasks.
method Captures variabilities on a low-dimensional manifold to meta-learn dynamics.
result Meta-Dynamic models can rapidly learn latent dynamics from new recordings.
Enhances generative model for clinical data privacy and accuracy.
problem Data privacy in electronic patient records.
method Improves a time-series generative model with privacy safeguards.
result DP-TimeGAN achieves a mean authenticity of 0.778 on the CKD dataset.
A machine learning approach to record fusion with high accuracy.
problem Aggregating multiple records corresponding to the same entity.
method Constructing feature vectors from attribute-level, record-level, and database-level signals; using a stagewise additive model to learn a classifier.
result Average precision of ~98% with source information and ~94% without source information across diverse datasets.
Reconstructing observed images from fMRI brain recordings is challenging. Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i.e., images with their corresponding fMRI responses) to span the huge space of natural images is prohibitive for many reasons. We present a novel approach which, in addition t…
Large-scale collections of electronic records constitute both an opportunity for the development of more accurate prediction models and a threat for privacy. To limit privacy exposure new privacy-enhancing techniques are emerging such as federated learning which enables large-scale data analysis while avoiding the cent…
Framework detects and mitigates data-poisoning attacks in causal effect estimation.
problem Vulnerability to append-only attacks in observational causal analyses.
method Develops a data-poisoning audit for augmented inverse-probability-weighted estimation.
result Proposes a greedy scan to compute exact worst-case movement at every append budget.
Methodology creates holdout and test/train sets for ML studies, preserving data for future research.
problem Preserving data for future research studies that are analysis-naive.
method Modification of k-fold cross-validation, randomization, and three-way split (holdout, test, training).
result Efficiently creates holdout and test/train sets without forcing.
When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as care practices, database systems, and population demographics evolve. Due to de-identification requirements, however, current experimental pra…
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. This process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient repres…
The superior temporal gyrus (STG) region of cortex critically contributes to speech recognition. In this work, we show that a proposed WaveNet, with limited available data, is able to reconstruct speech stimuli from STG intracranial recordings. We further investigate the impulse response of the fitted model for each re…
AI predicts medical specialty diagnostic choices from EHR records.
problem Predicting timely medical specialty diagnostic workups for patients.
method Ensemble of feed-forward neural networks trained on EHR data.
result Significantly higher accuracy compared to traditional checklists.
Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes. They are preempted by medical co…
Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties. However, traditional methods for dimensionality reduction and clustering are ill-suited to recovering the structure underlying the organization of neural circuits. In particular, they do n…
Regular medical records are useful for medical practitioners to analyze and monitor patient health status especially for those with chronic disease, but such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based model is suggest…
Record statistics of a strongly correlated time series: random walks and Lévy flightscond-mat.stat-mech
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…