A model learns symptom-drug relations for PD patients.
problem Automatic prescription recommendation for Parkinson's Disease patients.
method Builds a dataset of PD symptoms and prescriptions, learns latent symptom space, uses alternating optimization.
result Effective in recommending suitable prescription drugs for new PD patients.
This research improves infection prediction from symptoms across diverse study designs.
problem Predicting infection from symptom data is challenging due to varied study formats and contexts.
method Assesses transfer learning methods for improving prediction across different study types.
result It is possible to use data from one study to predict infection in another, with performance close to or better than using a single dataset.
MRI identifies chronic symptoms in mTBI patients.
problem Chronic symptoms in mTBI patients are hard to characterize.
method Multi-parametric MRI and low-dimensional projection.
result MRI metrics correlate with patient symptoms.
Bayesian networks and ML improve COVID-19 symptom classification and severity analysis.
problem Understanding the relationship between COVID-19 symptoms and demographic variables.
method Bayesian network structure learning followed by unsupervised clustering and demographic symptom identification.
result 99.99% testing accuracy compared to 41.15% for a heuristic method.
Study integrates causal inference and temporal complexity measures to analyze mental health symptoms.
problem Examining how individual symptom trajectories reveal diagnostic patterns in mental disorders.
method Causal inference, graph analysis, temporal complexity measures, machine learning.
result 91% accuracy in diagnosing symptom dynamics, highlighting disorder-specific causal mechanisms.
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.
Research uses activity analysis to identify mental health symptoms.
problem Identifying mental health symptoms using objective activity metrics.
method Proposes a framework for mHealth monitoring of psychiatric patients based on physical activity time series.
result Identifies distinct behavioural phenotypes and measures for mood assessment.
This paper studies the trade-off between model accuracy and coverage for diagnosis models used by patients.
problem Balancing accuracy and coverage in diagnosis models for patient use.
method Learned diagnosis models with varying coverage from EHR data.
result A 1% drop in top-3 accuracy for every 10 diseases added to the coverage.
Low-cost device diagnoses diseases using deep learning.
problem Limited healthcare access and variable disease properties.
method Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) on a Raspberry Pi Zero.
result Achieves 90% accuracy for symptom-based diagnoses and 91% for visual skin diseases.
Deep learning diagnoses MS from smartphone data.
problem Diagnosing MS with complex clinical assessments and tests.
method Deep-learning approach using smartphone-derived digital biomarkers.
result Deep-learning models distinguish MS with 88% accuracy.
A brief historical perspective is first given concerning financial crashes, - from the 17th till the 20th century. In modern times, it seems that log periodic oscillations are found before crashes in several financial indices. The same is found in sand pile avalanches on Sierpinski gaskets. A discussion pertains to the…
Method predicts motor symptoms of Parkinson's disease from daily activities.
problem Objective monitoring of Parkinson's disease symptoms.
method Multi-layer Gaussian process models for three types of movement abnormalities.
result Strong agreement between model predictions and clinical annotations.
DEBIAS learns causal effects from psychiatric longitudinal data by optimizing outcome weights.
problem Causal inference challenges in psychiatric longitudinal data due to symptom heterogeneity and latent confounding.
method DEBIAS algorithm that optimizes outcome weights to maximize durable treatment effects and minimize confounding.
result DEBIAS consistently outperforms state-of-the-art methods in recovering causal effects for clinically interpretable composite outcomes.
System assesses patient urgency and recommends care based on medical notes.
problem Assessing patient urgency and recommending appropriate care.
method Attention-based convolutional neural network trained on medical notes.
result Precision increases to 85% when using attention scores for warning symptoms.
Study uses logistic regression and association rules to identify early symptoms of malignant mesothelioma.
problem Difficult diagnosis of malignant mesothelioma leading to late-stage detection and poor patient survival.
method Implemented logistic regression and developed association rules to identify early symptoms.
result Categorical logistic regression improved training accuracy from 72.30% to 81.40%.
Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that ha…
Deep learning system tracks stool consistency for GI disease assessment.
problem Lack of objective stool consistency measurements in chronic GI disease.
method Computer vision and deep convolutional neural networks (CNN).
result Developed a stool detection and tracking system.
Ensemble learning improves anomaly detection for milder symptoms.
problem Difficulty in detecting incipient anomalies due to similarity to normal conditions.
method Utilize uncertainty information from ensemble learning to identify misclassified incipient anomalies.
result Ensemble learning methods show improved performance on incipient anomaly detection.
ABC improves uncertainty quantification in LLMs for clinical diagnostics.
problem Overconfident and poorly calibrated estimates of LLMs in clinical domains.
method Approximate Bayesian Computation (ABC) for likelihood-free inference.
result Improves accuracy by up to 46.9%, reduces Brier scores by 74.4%, and enhances calibration.
Machine learning aids in diagnosing Parkinson's disease with higher accuracy.
problem Subjectivity in traditional PD diagnosis methods and missed early symptoms.
method Machine learning applied to various data modalities for PD and control group classification.
result Machine learning methods show high potential for improving PD diagnosis.
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
problem Heterogeneous disease progression and treatment response in Parkinson's Disease.
method Two-stage conformal prediction framework with statistical guarantees.
result Quantifies uncertainty in medication needs predictions, improving clinical trust and quality of life.
Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new compu…
The study uses demographical data to predict health conditions.
problem Predicting health conditions based on patient demographics and symptoms.
method Analyzing Electronic Health Records (EHR) from Brazil to identify age-related clusters of health conditions.
result Age of patients significantly influences the likelihood of certain health conditions.
AI model identifies chemical agents in MCI with high accuracy.
problem Identifying chemical agents in mass casualty incidents.
method Reverse engineered signs/symptoms, trained using ANN, BDT, and WISER.
result WISER outperformed ANN and BDT in identifying chemical agents.
Paper uses text mining to predict reliability issues from customer complaints.
problem Detecting emerging reliability issues in after-sales service businesses.
method Essential text mining concepts applied to analyze customer complaints.
result Proactive detection of reliability problems through customer feedback.
Online learning improves dengue fever detection with minimal training data.
problem Improving dengue fever detection accuracy with limited training data.
method Online learning approach for incremental learning from patient symptoms and diagnostic investigations.
result The proposed model effectively identifies patients with high likelihood of dengue disease.
Bluetooth data predicts depression severity, showing 18.8% extra variance.
problem Predicting depressive symptom severity using Bluetooth data.
method Extracted 49 Bluetooth features from NBDC data, used linear mixed-effect and hierarchical Bayesian linear regression models.
result Hierarchical Bayesian model achieved best prediction metrics (R2=0.526, RMSE=3.891).
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.
Deep learning identifies plant stresses accurately and quantitatively.
problem Manual inspection of plant stresses is subjective and time-consuming.
method Developed an explainable deep learning model using gradient-weighted class activation mapping.
result Model accurately identifies and quantifies diverse foliar stresses in soybeans and other species.
Counterfactual diagnosis improves medical accuracy and safety.
problem Existing diagnostic algorithms struggle with distinguishing correlation from causation.
method Reformulated diagnosis as a counterfactual inference task and derived new counterfactual diagnostic algorithms.
result Counterfactual diagnostic algorithms significantly improve accuracy and safety compared to standard Bayesian algorithms.
BIN and CBN models infer health variables from symptoms and signals.
problem Inferring health variables from symptoms and signals.
method Bidirectional inference networks (BIN) and composite BIN (CBIN).
result CBIN achieves state-of-the-art performance and better accuracy.
This study uses smartphone data to predict when mood interventions are needed for bipolar disorder.
problem Chronic mental illness with extreme mood changes that lead to personal or social consequences.
method Anomaly detection framework using Temporal Normalization to predict mood anomalies from natural speech data.
result A framework for real-world speech-focused mood monitoring using deep learning.
In this paper we introduce a novel family of decision lists consisting of highly interpretable models which can be learned efficiently in a greedy manner. The defining property is that all rules are oriented in the same direction. Particular examples of this family are decision lists with monotonically decreasing (or i…
CNN learns diabetic syndromes from patient records.
problem Syndrome differentiation in TCM is complex and lacks standardization.
method Multi-instance multi-task learning with CNN.
result Outperforms other methods on diabetes dataset.
AI triage and diagnostic system performs similarly to human doctors, with safer triage advice.
problem Improving patient care through more reliable symptom checkers.
method Prospective validation study comparing AI system to human doctors.
result AI system's accuracy in identifying conditions comparable to human doctors, with safer triage advice.
A new framework estimates causal effects for ordinal variables.
problem Existing causal inference methods fail for ordinal data.
method Presumes a latent Gaussian DAG model with constrained covariance matrix.
result Closed-form function for ordinal causal effects in latent space.
Hidden Markov Models classify cough events with high accuracy.
problem Identifying coughing events in noisy environments for health monitoring.
method Used Hidden Markov Models (HMMs) for cough classification.
result Multivariate HMMs achieved 92% AUR in classifying cough events.
Study discovers rules linking patient symptoms to unplanned ICU transfers.
problem Identifying patients at risk for unplanned ICU transfers.
method Mixed-integer optimization approach to learn association rules.
result Significant rules discovered for each patient subgroup.
Machine learning models predict depression risk based on various factors.
problem Identifying individuals at greatest risk for depression.
method Random Effects/Expectation Maximization (RE-EM) trees and Mixed Effects Random Forest (MERF) algorithms.
result Machine learning models accurately predict depression severity and identify key predictors.
Proposes a new method for medical diagnosis using network-based representation learning.
problem Improving medical diagnosis accuracy through better data representation.
method Heterogeneous network-based model and modified metapath2vec algorithm for learning latent node representations.
result Significant performance boost in symptom/disease classification and disease prediction tasks.
Model diagnoses coinfection between malaria and arbovirus in Kedougou.
problem Diagnosing coinfection between malaria and arbovirus in tropical regions.
method Multinomial logistic model using patient data from 2009-2013.
result Derived coinfection probabilities and identified disease-specific symptoms.
Unsupervised clustering reveals novel TBI phenotypes.
problem Inadequate categorization of traumatic brain injury (TBI) based on symptoms.
method Applied unsupervised learning with GLRM feature selection.
result Identified four novel TBI phenotypes with distinct feature profiles.
Proposes TPIS for early and low-cost TB vs. pneumonia diagnosis.
problem Challenges in differentiating TB from pneumonia.
method Two-step decision support system with stacked ensemble classifiers.
result TPIS outperforms other methods in early and final diagnosis.
Study predicts internet-based treatment effects for GPPPD based on dyadic coping.
problem Identifying which patients will benefit most from internet-based GPPPD treatment.
method Developed a multivariable decision tree model using recursive partitioning.
result Predicts large effects for high dyadic coping patients, small effects for low dyadic coping patients.
New method estimates composite outcomes for precision medicine.
problem Balancing multiple outcomes in precision medicine.
method Optimal treatment rule for composite outcomes.
result Estimates composite outcomes and optimal treatment rule.
Basic peculiarities of market price fluctuations are known to be well described by a recently developed random walk model in a temporally deforming quadric potential force whose center is given by a moving average of past price traces [Physica A 370, pp91-97, 2006]. By analyzing high-frequency financial time series of …
We study the dynamics of correlation and variance in systems under the load of environmental factors. A universal effect in ensembles of similar systems under the load of similar factors is described: in crisis, typically, even before obvious symptoms of crisis appear, correlation increases, and, at the same time, vari…
Study compares ARIMA, ETS, NNAR and hybrids for COVID-19 hospitalizations.
problem Forecasting the second wave of COVID-19 hospitalizations in Italy.
method ARIMA, ETS, NNAR, and hybrid models.
result Hybrid models outperform single models in capturing epidemic patterns.