In order to submit a claim to insurance companies, a doctor needs to code a patient encounter with both the diagnosis (ICDs) and procedures performed (CPTs) in an Electronic Health Record (EHR). Identifying and applying relevant procedures code is a cumbersome and time-consuming task as a doctor has to choose from arou…
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48 results for “diagnosis codes”
High-throughput machine learning predicts thousands of diagnosis codes with high accuracy.
problem Predicting disease risk for thousands of diagnosis codes at various time points.
method Training machine learning algorithms on EHR data to predict diagnosis risks.
result Achieved AUCs of 0.803 and 0.758 for 1 and 6-month predictions, respectively.
Categorical Co-Frequency Analysis: Clustering Diagnosis Codes to Predict Hospital Readmissionsstat.AP
Categorical Co-Frequency Analysis clusters diagnoses to predict hospital readmissions.
problem Predicting patients' risk of 30-day hospital readmission.
method Categorical Co-Frequency Analysis (CoFA) measures diagnosis similarity using random forests.
result Identified three groups of diagnoses with varying readmission risk.
Quasi-orthonormal encoding reduces high dimensionality for categorical data.
problem High dimensionality and low sample size issues in categorical data encoding.
method Quasi-orthonormal encoding (QOE) for categorical data.
result QOE reduces dimensionality and improves machine learning performance.
We show how to learn low-dimensional representations (embeddings) of patient visits from the corresponding electronic health record (EHR) where International Classification of Diseases (ICD) diagnosis codes are removed. We expect that these embeddings will be useful for the construction of predictive statistical models…
Enhances detection of adverse drug events using diverse healthcare record data.
problem Detecting adverse drug events from mixed data types in electronic health records.
method Aggregate diagnosis codes, drug codes, and lab measurements; use recursive feature selection.
result Significant improvement in AUC using additional features, statistically significant.
Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated…
A new COVID-19 CT dataset helps develop AI diagnosis models.
problem Lack of publicly available COVID-19 CT datasets due to privacy issues.
method Built an open-sourced COVID-CT dataset and developed AI diagnosis methods.
result Developed AI diagnosis models achieving high accuracy and performance.
This work develops a high precision fault diagnosis classifier using XAI insights.
problem Fault diagnosis of steel plates in manufacturing industry.
method Incorporation of XAI insights into Data Science process using SMOTE and medoids.
result 10 fold cross validated performance of 94% achieved.
In this paper we are interested in the prediction of preterm birth based on diagnosis codes from longitudinal EHR. We formulate the prediction problem as a supervised classification with noisy labels. Our base classifier is a Recurrent Neural Network with an attention mechanism. We assume the availability of a data sub…
Models extract relevant EHR snippets to aid radiologists in diagnosis.
problem Difficulty in identifying relevant patient record information for diagnosis.
method Distantly supervised transformer-based neural model for extractive summarization.
result Models yield better extractive summaries than unsupervised approaches.
Inflated neural networks improve IPMN diagnosis from MRI scans.
problem Challenges in diagnosing IPMN from limited annotated data and 3D volumetric MRI.
method Developed two novel inflated deep network architectures, InceptINN and DenseINN, trained on ImageNet.
result Absolute improvement of 8.76% in accuracy for diagnosing IPMN.
Deep tabular models outperform GBDT in medical diagnosis tasks.
problem Transfer learning for tabular data in medical diagnosis.
method Proposes a pseudo-feature method for transfer learning between different feature sets.
result Tabular neural networks outperform GBDT in medical diagnosis tasks.
MD tree diagnoses model failures using loss landscape metrics.
problem Diagnose model failures without knowing training configuration.
method MD tree based on loss landscape metrics.
result MD tree achieves 87.7% accuracy in dataset transfer tasks, outperforming validation-based approaches.
The paper proposes a new method for comparing logistic regression models across different populations.
problem Comparing logistic regression models across sub-populations can lead to misleading results.
method Develops a cascading set of equivalence tests for logistic regression models, addressing coding, predictions, and overall accuracy.
result Equivalence testing incentivizes accurate inference and avoids perverse incentives from significance tests.
MedGraph learns patient visit embeddings from EMRs, capturing both attributes and temporal sequences.
problem Limited EMR embedding methods fail to capture patient demographics, utilisation, and code descriptions.
method MedGraph constructs an attributed bipartite graph and uses a point process to model temporal sequences.
result MedGraph outperforms state-of-the-art methods in medical risk prediction tasks.
Unsupervised learning summarizes EHR data into a patient status vector.
problem Challenges in modeling electronic health records due to irregularities and varying procedures/diagnoses.
method Two-step unsupervised representation learning scheme using auto-encoders and forecasting tasks.
result Improved generalization performance on mortality and readmission tasks.
New benchmark evaluates BDL methods in medical retinopathy diagnosis.
problem Evaluate robustness and scalability of BDL methods in medical applications.
method Developed a new benchmark with real-world diabetic retinopathy tasks.
result Some BDL techniques overfit uncertainty to datasets, underperforming on new benchmark.
Sepsis is a life-threatening disease and one of the major causes of death in hospitals. Imaging of microcirculatory dysfunction is a promising approach for automated diagnosis of sepsis. We report a machine learning classifier capable of distinguishing non-septic and septic images from dark field microcirculation video…
DoWhy-GCM extends causal inference in graphical models for diverse queries.
problem Addressing diverse causal queries in graphical causal models.
method Specify cause-effect relations via a causal graph, fit causal mechanisms, pose causal queries.
result Identification of root causes, attribution of causal influences, diagnosis of causal structures.
Framework harmonizes EHR data across institutions for better analysis.
problem Heterogeneity of medical codes and terminologies hinder EHR data analysis.
method MASH (Multi-source Automated Structured Hierarchy) uses neural optimal transport and learned hyperbolic embeddings to align and structure EHR data.
result MASH generates interpretable hierarchical graphs for unstructured local laboratory codes.
Somed2Vec learns medical concept embeddings from SNOMED-CT, improving healthcare analytics.
problem Lack of effective vector representations for medical concepts in healthcare analytics.
method Graph-based representation learning using random walks and Poincaré embeddings on SNOMED-CT.
result Concept embeddings from SNOMED-CT significantly outperform state-of-the-art embeddings.
Unsupervised anomaly detection aids doctors in evaluating X-ray images of hands.
problem Lack of labeled medical data and ambiguous symptoms in X-ray images.
method Adopting state-of-the-art unsupervised learning methods for anomaly detection and preprocessing.
result Without preprocessing, unsupervised methods perform randomly on X-ray images of hands.
Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse…
Quantum computing improves fault diagnosis in industrial processes.
problem Fault detection and diagnosis in industrial process systems.
method Integrates quantum computing and deep learning to extract features and diagnose faults.
result Quantum-assisted deep learning achieves high fault detection rates (79.2% and 99.39%).
Paper tackles MSDA with GMMs and OT, improving over prior art.
problem Adapting multiple heterogeneous source measures to a target measure.
method Optimal Transport between Gaussian Mixture Models, with novel barycenter calculation.
result Improves image classification and fault diagnosis benchmarks.
Background: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning…
Deep learning system diagnoses AVNFH from plain radiographs.
problem Challenging AVNFH diagnosis from plain radiographs.
method Deep convolutional neural networks for end-to-end diagnosis.
result AVN-net achieves state-of-the-art AUC of 0.97 in AVNFH detection.
Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility of an early detection and diagnosis of the faulty condition can save both time …
Two machine learning models detect anomalies in ER claims, saving up to 40% in improper payments.
problem Improper health insurance payments from fraud and upcoding.
method Two machine learning models: an upcoding model based on severity code distributions and a random forest model for claim sorting.
result Random forest model saved 12% to 40% in improper payments compared to a baseline approach.
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.
X-Caps improves medical diagnosis explainability by encoding visual attributes in capsules.
problem Uninterpretable predictions from deep neural networks in healthcare.
method Teaches a novel multi-task capsule network to encode high-level visual attributes and malignancy scores.
result X-Caps outperforms state-of-the-art deep dense 3D CNNs in capturing visually interpretable attributes and malignancy prediction.
Develops a data-driven fault diagnosis framework for time-series data.
problem Fault diagnosis of dynamic systems using imbalanced and unknown fault classes.
method Kullback-Leibler divergence, data-driven fault classification, open-set classification.
result Framework handles imbalanced datasets, class overlapping, and unknown faults.
Deep learning aids in autism diagnosis and rehabilitation using neuroimaging data.
problem Challenges in automated detection and rehabilitation of ASD using neuroimaging data.
method Deep learning techniques applied to neuroimaging data for ASD diagnosis and rehabilitation.
result Deep learning improves accuracy in ASD diagnosis and rehabilitation.
Power Transformer Fault Diagnosis with Intrinsic Time-scale Decomposition and XGBoost Classifiercs.LG
A method uses ITD and XGBoost for precise power transformer fault diagnosis.
problem Fault diagnosis of power transformers using DGA data.
method Ranking DGA parameters by skewness, extracting ITD features, and using an XGBoost classifier.
result The method achieves over 95% accuracy in classification.
Proposes a sample-efficient method for uncertainty estimation in deep learning.
problem Inaccurate uncertainty estimation in deep learning models, especially with limited data.
method Probabilistic Neighbourhood Component Analysis (PCA) for sample-efficient uncertainty estimation.
result Demonstrates superior uncertainty quantification compared to state-of-the-art methods.
Novel hybrid modeling combines ML and physics for real-time diagnosis.
problem Real-time diagnosis of complex systems.
method Combines machine learning and physics-based models to create reduced-order models.
result Generated models are two orders of magnitude simpler, improving efficiency.
Method augments CTNs for ICD coding with neural network imputation.
problem Time-consuming manual annotation of CTNs for ICD coding.
method Semi-self-supervised neural network imputation of clinical features.
result Data augmentation improves ICD coding performance significantly.
Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosiseess.IV
DiagNet uses adversarial learning and signed graph regularization for better mammography diagnosis.
problem Inadequate data and similarity between benign and cancerous masses in mammography.
method Adversarial learning to generate positive and negative mammograms, signed similarity graph, deep convolutional neural network training.
result DiagNet outperforms state-of-the-art in breast mass diagnosis.
Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new ma…
Deep neural networks justify medical diagnoses with textual explanations.
problem Improving machine learning in medical diagnosis justification.
method Mapping X-Ray images to textual representations, generating explanations, and multi-task training.
result The method significantly outperforms existing justification methods and achieves high accuracy.
Counterfactual diagnosisstat.ML
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.
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.
We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis. We extrapolate to…
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.
Optimizes test set size for accurate diagnosis using machine learning.
problem Determining the minimum test set size for accurate diagnosis.
method Proposes machine learning methods (LASSO and SVM) to predict optimal test set size.
result SVM achieves 90.4% accuracy with a reduced test set by 35.24%.
Study on diagnosing unseen medical conditions using open-set learning.
problem Training models for unseen medical conditions is impractical.
method Frame diagnosis as an open-set learning problem, compare state-of-the-art approaches, and experiment with distributed training data.
result Explicitly modeling unseen conditions leads to consistent gains, but optimal training strategy varies.
CI-GNN uses GNNs to diagnose psychiatric disorders by identifying causally relevant brain regions.
problem Leveraging GNNs for psychiatric diagnosis requires interpretable models to understand decision-making.
method CI-GNN integrates Granger causality into GNNs to identify causally relevant subgraphs.
result CI-GNN provides more reliable and concise explanations of psychiatric diagnoses.