Supervised learning improves disease outbreak detection accuracy.
problem Early detection of infectious disease outbreaks to protect public health.
method Developed a supervised learning approach based on hidden Markov models for disease outbreak detection.
result Reduces false positive rate by up to 50% while maintaining sensitivity.
Word2vec model captures disease attributes from unstructured text.
problem Augmenting disease surveillance with unstructured data requires accurate taxonomical correlations and trace mapping.
method Developed a disease vocabulary driven word2vec model (Dis2Vec) to model diseases and their attributes.
result Dis2Vec outperforms traditional word2vec methods in capturing taxonomical attributes across different disease classes.
FAA identifies rodent disease carriers for human health.
problem Identifying rodent species that carry zoonotic diseases.
method Applied Formal Concept Analysis to rodent trait data.
result Identified concepts linking rodent traits to zoonotic disease carrier status.
Improved bio-surveillance through automated document classification.
problem Tracking infectious diseases across global news alerts.
method Recurrent neural networks, TF-IDF, Naive Bayes, logistic regression.
result 97% recall and 93.3% accuracy in bio-surveillance event classification.
New model maps malaria prevalence across Kenya's changing administrative boundaries.
problem Mapping disease prevalence with changing administrative boundaries.
method Combines deep learning and MCMC with aggVAE for disease mapping.
result Solves the change-of-support problem in disease surveillance.
Proposes a method to identify key components for predicting epidemic dynamics with limited resources.
problem Predicting epidemic dynamics with limited surveillance resources.
method Developed a group sparse Bayesian learning algorithm to identify sentinel components for monitoring.
result The proposed algorithm effectively predicts epidemic dynamics using partial data from sentinel components.
Exploiting the large amount of available data for addressing relevant social problems has been one of the key challenges in data mining. Such efforts have been recently named "data science for social good" and attracted the attention of several researchers and institutions. We give a contribution in this objective in t…
Framework extracts symptoms from EHRs for rapid disease outbreak detection.
problem Extracting relevant data from unstructured medical texts.
method Conformal active learning for efficient data mining.
result Framework achieves strong performance with minimal manual labeling.
SVM with graph metrics improves diabetes prediction.
problem Improving disease classification accuracy using machine learning.
method Combining SVM modeling with graph theory metrics for disease prediction.
result SVM with graph metrics outperformed without, achieving ROC index of 75.6.
Machine learning algorithms improve autism surveillance accuracy.
problem Efficiently predicting autism from evaluations to reduce human labor.
method 8 supervised learning algorithms tested on CDC ASD data.
result Random forest and NB-SVM perform similarly, with NB-SVM having more false negatives.
Flusion combines multiple data sources to improve flu forecasts.
problem Accurate flu predictions to improve public health actions.
method Ensemble model combining gradient boosting quantile regression and Bayesian autoregressive models.
result Flusion was the top-performing model in the CDC's influenza prediction challenge.
Optimizes COVID-19 testing policy using a Multi-Armed Bandit approach.
problem Balancing discovery of positive cases with population surveillance.
method Risk scoring and random sampling based on Multi-Armed Bandit theory.
result Effective prioritization captures 65-92% of positive cases with varying testing capacity.
Semantic Scan detects subtle, localized events in text streams.
problem Detecting emerging topics in text streams with high accuracy and speed.
method Contrastive topic modeling, online document assignment, spatial scanning.
result Semantic Scan outperforms other methods in detecting anomalous events.
Deep learning model performs differently in TB-specific vs non-TB-specific chest X-rays.
problem Limited generalizability of machine learning models for tuberculosis detection.
method Developed a DCNN model using a TB-specific CXR dataset and tested it on a non-TB-specific CXR dataset.
result A deep learning model trained on one population's TB CXR data does not generalize well to another population.
Adaptive sequential testing optimizes epidemic control by learning optimal test strategies.
problem Optimizing test allocation in epidemics with network and temporal dependence.
method Adaptive sequential design with Online Super Learner for optimal test strategies.
result Superior performance in simulated university COVID-19 pandemic.
Paper uses LSTM autoencoder for ADS-B data to detect surveillance aircraft.
problem Detecting surveillance aircraft from ADS-B flight data.
method LSTM-based sequence autoencoder for ADS-B data.
result LSTM autoencoder effectively learns features for detecting surveillance aircraft.
Paper addresses data heterogeneity in federated learning for CoxPH models in healthcare.
problem Data heterogeneity in federated learning of CoxPH models for healthcare.
method Feature-based clustering and event-based reporting strategy.
result Enhanced model accuracy and performance in federated survival analysis.
New method tackles anomaly detection in video surveillance using continual learning.
problem Challenges in continual learning for high-dimensional applications like video surveillance.
method Transfer learning and continual learning for online anomaly detection.
result Significantly reduces training complexity and continual learning from recent data.
Study uses Instagram data to predict flu-like illnesses.
problem Influenza surveillance challenges due to resource limitations.
method Machine learning models trained on Instagram data, including visual content.
result Best nowcasting model had an MAE of 11.33 and correlation of 0.963.
Classifiers and beamforming algorithms improved audio surveillance detection accuracy.
problem Detecting surveillance sound events with high accuracy and efficiency.
method Evaluated seven classifiers and two beamforming algorithms; used data augmentation and tested with varying SNR levels.
result SVM and Delay-and-Sum (DaS) combination achieved the highest accuracy (86.0%), but had high computational cost.
New algorithm rSVDdpd improves robustness and scalability for video surveillance background modeling.
problem Camera tampering and noisy videos make background separation challenging.
method Introduces rSVDdpd, a robust singular value decomposition technique for scalable video surveillance.
result Demonstrates superior performance on benchmark and real-life datasets.
Online anomaly detection in surveillance videos with false alarm rate bounds.
problem Lack of theoretical performance analysis and online decision making in anomaly detection.
method Proposes an online anomaly detection method with asymptotic bounds on false alarm rate.
result Demonstrates effectiveness on publicly available data sets, outperforming state-of-the-art algorithms.
Graph-based framework predicts ADR signals from clinical data.
problem Detecting ADRs in post-market surveillance using clinical data.
method Developed a Drug-disease graph with Graph Neural Network for ADR signal prediction.
result Improved AUROC and AUPRC performance (0.795 and 0.775) compared to other algorithms.
Hidden Markov Model helps track asymptomatic carriers in pandemic.
problem Tracking spread of asymptomatic carriers (super-spreaders) during pandemic.
method Applied Hidden Markov Model to analyze COVID-19 data.
result Better assessment of spread extent for calibrated interventions.
Smart city surveillance benefits from sound event recognition.
problem Improving monitoring capabilities in smart cities.
method Exploration of several classifiers on the SESA dataset.
result SGD achieved 72.13% accuracy in sound event recognition.
New method optimizes searchers' allocation on perimeters over time.
problem Optimizing searchers' allocation on perimeters to detect intrusions.
method Combinatorial multi-armed bandit (CMAB) with upper confidence bound approach.
result Upper and lower bounds on expected performance of the method.
Online surveillance detects systemic risk in financial markets.
problem Detecting and monitoring systemic risk in financial markets.
method Online monitoring procedures for multiple series, controlling for false rejections.
result Procedures allow timely detection of financial distress.
Study classifies pathology reports using TF-IDF features and machine learning.
problem Classifying pathology reports for cancer surveillance and diagnostic workflow.
method Extracted TF-IDF features from pathology reports and classified them using SVM, XGBoost, and Logistic Regression.
result XGBoost achieved 92% accuracy in classifying pathology reports.
Paper describes a video surveillance system for highway traffic events.
problem Detecting specific sequences of situations in highway traffic videos.
method Compares RNN and CNN architectures for analyzing video frames and sequences.
result Best architecture performs well in real conditions.
Deep learning models outperform traditional methods in automated chief complaint classification for syndromic surveillance.
problem Improving accuracy and speed of automated classification of emergency department records for outbreak detection.
method Implemented two LSTM and GRU models compared to MNB and SVM classifiers trained on 3.6 million de-identified records.
result RNN models outperform bag-of-words classifiers, especially for chief complaints.
Study shows diverse data types improve SARS-COV-2 case surge predictions.
problem Improving pandemic case surge predictions using multimodal data.
method Investigated the effectiveness of biological, public health, and behavioral features.
result Diverse feature sets enhance prediction accuracy, varying by country and phase.
The detection of anomalous activity in graphs is a statistical problem that arises in many applications, such as network surveillance, disease outbreak detection, and activity monitoring in social networks. Beyond its wide applicability, graph structured anomaly detection serves as a case study in the difficulty of bal…
Algorithm predicts zoonotic virus emergence with high accuracy.
problem Precise spatio-temporal prediction of zoonotic virus emergence.
method Machine inference using protein sequence databases.
result Quantitative indicators of jump risk from genotypic changes.
Anomaly detection identifies unusual malaria transmission patterns in Ghana.
problem Identifying atypical malaria transmission patterns in Ghana's spatiotemporal surveillance data.
method Consensus-based anomaly detection framework applied to monthly malaria surveillance data.
result High-burden areas are not necessarily those with the most frequent anomalous transmission.
Zero-shot understanding of accidents from surveillance videos using vision-language models
problem Accident understanding from surveillance videos
method Three-stage pipeline with vision-language similarity, metadata-driven multi-prompt reasoning, and entropy-gated pairwise adjudicator
result Substantial improvement in harmonic-mean score over baseline
Machine learning detects metastatic breast cancer cases from linked EMR and cancer registry data.
problem Lack of metastatic recurrence data in cancer registries and EMRs.
method Semi-supervised machine learning on linked EMR and CCR data.
result Model achieved high accuracy in detecting metastatic breast cancer cases.
According to the definition of the London Interbank Offered Rate (LIBOR), contributing banks should give fair estimates of their own borrowing costs in the interbank market. Between 2007 and 2009, several banks made inappropriate submissions of LIBOR, sometimes motivated by profit-seeking from their trading positions. …
Mobile apps and machine learning improve malaria prevention and treatment.
problem High malaria cases and deaths in low-income countries.
method Adaptive interventions using mobile health apps and machine learning.
result Increased malaria testing, adherence, and provider skills.
Deep learning model for AIS data improves vessel monitoring.
problem Improving maritime safety and efficiency with AIS data.
method Combining RNNs and latent variable modeling for multi-task learning.
result Demonstrated relevance on real AIS datasets for trajectory reconstruction, anomaly detection, and vessel type identification.
Develops an efficient online robust PCA method for big data.
problem Efficiency and robustness in processing big data with changing subspaces.
method Online moving window robust principal component analysis (OMWRPCA) with change point detection.
result Successfully tracks both slowly and abruptly changing subspaces and detects change points.
DPVis integrates HMMs into visualizations for disease progression analysis.
problem Challenges in interpreting HMMs for disease progression modeling.
method Design study with clinical experts, visualizations of HMM parameters and outcomes.
result DPVis successfully evaluates and summarizes disease progression models.
Improved outbreak detection using machine learning fusion of statistical algorithms.
problem Balancing detection of outbreaks with false alarms.
method Train a fusion classifier using p-values and additional features.
result Fusion classifier using p-values and additional features improves outbreak detection.
SS3M learns disease phenotypes from few labels.
problem Lack of supervised data for disease phenotyping.
method Semi-Supervised Mixed Membership Model (SS3M).
result SS3M learns interpretable disease phenotypes.
Bayesian hypergraph inference models disease pathways from EHR data.
problem Modeling rare diseases influenced by shared risk factors.
method Bayesian hypergraph inference framework reframing multi-disease modeling.
result Interpretable disease pathways and well-calibrated uncertainty quantification.
System recommends disease treatments based on big data and cloud computing.
problem Inaccurate disease classification and treatment recommendations due to complex symptoms and multi-pathogenesis.
method DPCA for disease-symptom clustering, Apriori for D-D and D-T rules, parallel Apache Spark implementation.
result Effective disease-symptom clustering and accurate treatment recommendations for inexperienced doctors.
Model learns disease self-representations for drug repositioning.
problem Drug repositioning for disease treatment.
method Enforces proximity in disease self-representations to preserve human phenome network structure.
result Method outperforms state-of-the-art approaches and produces biologically interpretable disease self-representations.
Bayesian model identifies health disparities in disease progression.
problem Health disparities bias disease progression models.
method Interpretable Bayesian model accounting for three disparities.
result Model identifies and corrects for health disparities.
DKT transfers biomarker information between neurodegenerative diseases.
problem Estimating biomarker trajectories in rare neurodegenerative diseases with limited data.
method DKT is a joint-disease generative model that transfers biomarker progressions from common neurodegenerative diseases to rare ones.
result DKT estimates plausible multimodal biomarker trajectories in rare diseases like PCA using only unimodal data.