Deep learning method extracts medical knowledge from YouTube videos.
problem Improving healthcare information dissemination through machine learning.
method Developed a deep learning method to classify YouTube videos by medical knowledge level.
result Preliminary results show satisfactory performance in extracting medical knowledge from videos.
A new algorithm discovers causal factors between T2DM and bone mineral density.
problem Discovering causal factors between T2DM and bone mineral density from clinical data.
method Prior-Knowledge-driven local Causal structure Learning (PKCL) algorithm.
result PKCL achieves more reliable results without long-standing medical experiments.
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.
Unified deep learning predicts Parkinson's disease from medical images.
problem Diagnosing Parkinson's disease accurately from medical images.
method Transfer learning and domain adaptation using deep convolutional and recurrent neural networks.
result The approach effectively predicts Parkinson's disease across different medical environments.
This study evaluates a health knowledge graph for robustness in EHRs.
problem Evaluate robustness of a health knowledge graph in EHRs.
method Analyze sample size, unmeasured confounders, and non-linear functions.
result Identify sample size and unmeasured confounders as major sources of error.
GAMENet uses graph memory networks to recommend safe medication combinations.
problem Lack of personalized and safe medication combination recommendations considering patient history and drug interactions.
method Integrates drug-drug interactions knowledge graph into a memory module using graph convolutional networks and models longitudinal patient records.
result GAMENet outperformed state-of-the-art methods in effectiveness and safety, reducing DDI rate by 3.60%.
Two large medical dialogue datasets for improving healthcare.
problem Improving healthcare through better dialogue systems.
method Building two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN.
result The datasets are the largest medical dialogue datasets to date.
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.
CovidCare uses EMR data to predict patient outcomes in emerging epidemics.
problem Intelligent prognosis for patients with emerging infectious diseases during rapid epidemics.
method Transfer learning and knowledge distillation from existing EMR data.
result CovidCare outperforms baseline methods in predicting patient length of stay.
Paper models Alzheimer's disease using genotypic, phenotypic, and cognitive data.
problem Early detection and risk factor identification for Alzheimer's disease.
method Probabilistic generative subspace learning from multi-view medical data.
result Proposes a method to model Alzheimer's disease that combines genotypic, phenotypic, and cognitive data.
RL algorithms with medical integration improve personalized treatment recommendations.
problem Developing effective personalized treatment strategies for chronic diseases.
method Integrating medical knowledge into RL algorithms for DTR.
result Enhanced treatment recommendations with increased confidence.
Hi-RES framework extracts medical relations from articles and EHRs.
problem Manual annotation bottleneck in relation extraction.
method Labeling sentences, creating improved negative samples, using pretrained language models, and combining EHR embeddings.
result Significant accuracy increases in relation extraction, up to 0.998 for disorder-location relations.
The majority of medical documents and electronic health records (EHRs) are in text format that poses a challenge for data processing and finding relevant documents. Looking for ways to automatically retrieve the enormous amount of health and medical knowledge has always been an intriguing topic. Powerful methods have b…
Paper proposes embedding medical concepts from claims data for better risk adjustment models.
problem Lack of efficient representation of medical histories in risk adjustment models.
method Semantic embeddings of medical concepts from diagnostic, procedure, and prescription codes.
result Embedding-based models outperform commercial risk adjustment models in prospective risk score prediction.
The paper proposes a deep generative model for complex disease trajectories.
problem Modeling and analyzing complex disease trajectories.
method Deep generative time series approach with semi-supervised latent processes.
result The model can discover novel aspects of diseases and cluster them into new sub-types.
Enhances machine learning with background knowledge through feature generation.
problem Machine learning struggles with small datasets.
method Generates features from auxiliary datasets not similar to the original task.
result Significant improvement in various learning tasks.
Paper tackles dataset bias in medical imaging studies, proposing a method to unlearn study membership.
problem Dataset bias limits model generalization across different medical imaging studies.
method Proposes a framework to unlearn study membership by transforming data to an intermediate space indistinguishable by study membership.
result Models learn general properties of etiology under study, not dataset-specific peculiarities.
OMTL uses ontology to learn from imbalanced EHR data.
problem Imbalanced and small usable data for phenotypes in EHRs.
method Ontology-driven multi-task learning framework.
result Improved learning performance on phenotypes.
Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworth…
Proposes MEDIC to improve incremental learning by preventing forgetting and intransigence.
problem Challenges of forgetting old knowledge and intransigence on new knowledge in incremental learning.
method Maximum Entropy Regularizer (MER) and DropOut Sampling (DOS) to penalize uncertain knowledge and reduce class imbalance.
result Proposed method 'MEDIC' outperforms state-of-the-art algorithms in accuracy, forgetting, and intransigence.
This chapter introduces reproducibility in machine learning for medical imaging.
problem Lack of reproducibility in machine learning for medical imaging.
method Distinguishes and defines types of reproducibility, outlines requirements, and discusses utility.
result Discussion on benefits and a plea for a non-dogmatic approach to reproducibility.
KD-Net transfers knowledge from multi-modal to mono-modal segmentation networks.
problem Limited acquisition of multiple imaging modalities in clinical settings.
method Generalized distillation framework adapted for mono-modal networks.
result The student network outperforms baseline mono-modal networks in brain tumor segmentation.
Machine vision-guided 3D medical image compression improves segmentation accuracy.
problem High data traffic and computation costs in cloud-based medical image analysis.
method Developed a machine vision-oriented 3D image compression framework for medical segmentation.
result Significantly higher segmentation accuracy at the same compression rate or better compression rate under the same accuracy.
A new rotation invariant method for 3D medical imaging classification.
problem Computational expense and lack of rotation invariance in 3D medical image processing.
method Proposes a rotation invariant convolution operator using hypersphere topology.
result Demonstrates improved classification accuracy and rotation invariance.
Bayesian method transfers knowledge between brain tumor datasets.
problem Applying deep learning to small medical datasets.
method Generative Bayesian Prior network for knowledge transfer.
result Best results in Dice Similarity Coefficient for BRATS2018.
Study shows federated learning can segment brain tumors without data sharing.
problem Lack of sufficient medical data for deep learning models.
method Federated learning for multi-institutional collaboration without sharing patient data.
result Federated semantic segmentation models perform similarly to those trained with shared data.
Bayesian method transfers knowledge between brain tumor datasets for MRI segmentation.
problem Limited applicability of deep learning methods for small medical datasets.
method Generative Bayesian Prior network for knowledge transfer.
result Best results in Dice Similarity Coefficient metric for BRATS2018.
Deep learning predicts ICU mortality with enhanced interpretability.
problem Improving mortality prediction accuracy and clinician trust in AI.
method Trained a deep learning model on MIMIC-III to interpret nursing notes.
result Model reaches ROC of 0.8629, outperforming SAPS-II.
System exposes study population descriptions in clinical guidelines.
problem Challenges in understanding applicability of clinical guidelines.
method Developed an ontology-enabled prototype system using SIO.
result Allows medical practitioners to better understand study populations.
Active learning improves medical image segmentation by selecting optimal samples.
problem Limited manual annotations in medical datasets.
method Maximizing information at network abstraction layer and using Borda-count for sample selection.
result Improved segmentation performance with active learning.
Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events usi…
Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: -Data insufficiency:Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results. -Interpretation:The representations lear…
The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems. In this paper, we demonstrate that adversarial examples are capable of manipulating deep learning systems across three clinical domains. For each of our representative medical deep learning classifiers, bo…
A new SSL method improves medical image classification using global latent mixing.
problem Costly annotation of large-scale medical image data sets.
method Linear mixing of labeled and unlabeled data in both input and latent space.
result Improved performance in semi-supervised classification of thoracic disease and skin lesion.
3D dataset for intracranial aneurysms aids deep learning applications.
problem Lack of 3D medical datasets for deep learning.
method Developed an open-access 3D dataset, IntrA, for intracranial aneurysms.
result Demonstrated the challenges and performance of 3D medical datasets.
The paper explores how neural networks generalize differently from natural and medical images.
problem Discrepancies in generalization error between natural and medical images.
method Established and empirically validated a generalization scaling law with respect to intrinsic dataset properties.
result Higher intrinsic 'label sharpness' of medical images leads to higher adversarial vulnerability.
RetainVis enhances RNNs for medical record analysis, making predictions more understandable.
problem Limited interpretability and user interaction in RNN models for medical records.
method Developed RetainVis, a visual analytics tool that integrates an improved RNN model (RetainEX) with interactive visualizations.
result Users can explore EMR data and understand how individual medical codes affect predictions.
Paper proposes a method to generate instance labels from weakly supervised data.
problem Weakly supervised instance labeling in medical image analysis.
method Uses multiple instance learning (MIL) and knowledge distillation to generate instance-level predictions.
result Significantly outperforms state-of-the-art MIL methods in instance-level prediction.
CNNs improve medical image classification with few samples.
problem Classifying medical images with limited training data.
method Transfer learning using CNNs, representation extraction, and a novel metric for performance prediction.
result CNN-based transfer learning outperforms feature-based methods with high correlation to test set performance.
Proposes first method for continuously indexed domain adaptation.
problem Challenges of transferring knowledge between continuously indexed domains.
method Combines adversarial adaptation with a novel discriminator.
result Outperforms state-of-the-art methods on synthetic and real-world datasets.
Enhanced deep learning model improves tumor segmentation in ultrasound images.
problem Challenges in integrating patient-specific medical priors into deep learning models.
method Integrates visual saliency into a U-Net architecture with attention blocks.
result Achieved a Dice similarity coefficient of 90.5 percent on a dataset of 510 images.
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: (1) identification of novel regulatory variants located in noncoding domains and their function as applied to pharmacoepigenomics; (2) patient stratification from medical records; and (3) prediction of…
AI generates a sequence of death causes from hospital records.
problem Accurate death reporting for vital statistics and policy formulation.
method Neural machine translation models to generate causal chains, incorporating medical domain knowledge.
result Achieved 16.04 BLEU score for generating accurate causal chains.
Patient journeys are compared to find clusters of similar disease trajectories.
problem Discovering shared health outcomes among patient journeys.
method Comparing longitudinal health data to identify clusters of similar patient trajectories.
result Clusters of patient journeys with similar health outcomes can be identified.
Study uses multi-task Bayesian optimization to speed up SVM hyperparameter tuning for nodules diagnosis.
problem Redundant and time-consuming hyperparameter tuning for SVM classifiers in medical imaging.
method Employed multi-task Bayesian optimization to accelerate hyperparameter search.
result Multi-task Bayesian optimization significantly accelerates hyperparameter search.
This research predicts diabetes mellitus using machine learning techniques.
problem Early prediction of diabetes mellitus to control and save human life.
method Exploring various risk factors related to diabetes using four machine learning algorithms (SVM, NB, KNN, C4.5 Decision Tree) on adult population data.
result C4.5 decision tree achieved higher accuracy in predicting diabetic mellitus.
Dynamic memory prevents forgetting in continuous learning of medical images.
problem Catastrophic forgetting in machine learning models over time due to domain shifts.
method Dynamic memory to store and replay diverse training data subsets.
result Dynamic memory mitigates forgetting without knowing when shifts occur.
New DAM method improves AUC scores in medical image classification.
problem Maximizing AUC in large-scale medical image classification.
method Proposes AUC margin loss for robust optimization, conducts extensive empirical studies.
result Improves performance on four medical image classification tasks, achieving 1st place on Stanford CheXpert.