Deep learning predicts hospital readmission risk from notes.
problem Predicting hospital-wide readmission risk from doctors' notes.
method Deep learning and natural language processing on unstructured text.
result Model predicts readmission with c-statistic 0.70.
Graphical model predicts rare disease physicians, improving accuracy.
problem Identifying rare disease physicians from imbalanced patient data.
method Factor Graph Approach modeling physician and patient features.
result Graphical model outperforms existing targeting methodologies.
Neurology-as-a-Service uses deep neural networks to assist non-specialist doctors in EEG analysis.
problem TeleEEG requires expensive infrastructure and expertise, limiting its use in developing countries.
method Cloud-based deep neural network approach for non-specialist EEG analysis.
result Deep neural network achieves 63.4% accuracy in classifying EEG activity, significantly higher than shallow approaches.
Deep learning predicts one-year mortality from ECGs, even in 'normal' cases.
problem Predicting mortality from 12-lead ECGs using deep learning.
method Deep neural network model trained on 1,775,926 ECGs, validated on 297,548 'normal' ECGs.
result Deep learning model predicts one-year mortality with AUC of 0.85, and Cox Proportional Hazard model reveals a significant hazard ratio.
Deep Learning improves end-of-life care by predicting patient mortality.
problem Misalignment between patient wishes and actual care at the end of life.
method Deep Neural Network trained on EHR data to predict mortality and identify patients in need of palliative care.
result Proactive approach to reaching out to patients in need of palliative care.
The paper extracts structured data from physician-patient conversations, reducing clerical burden.
problem Mining insights from physician-patient conversations for electronic health record documentation.
method Created a dataset of transcripts and summaries, extracted noteworthy utterances, and improved model performance.
result Extracting noteworthy utterances significantly boosts model performance for recognizing diagnoses and RoS abnormalities.
Deep learning models interpret patient outcomes better with time aggregation.
problem Chronic ambulatory care data challenges for deep learning models.
method Time-distributed-dense layers combined with GRUs for generalization, clinical interpretation framework.
result Time-distributed-dense layers with GRUs produce the most generalizable models.
The study identifies patient subgroups with enhanced or diminished opioid treatment effects.
problem Lack of prescribing guidelines for opioids leading to adverse outcomes.
method Generative model using mixture distribution and sparsity to discover subgroups with treatment effects.
result Human-interpretable insights on subgroups with enhanced or diminished treatment effects.
Paper introduces methods to automatically generate SOAP notes from patient-physician conversations.
problem Burden of creating digital SOAP notes by physicians.
method Cluster2Sent algorithm for summarizing patient-physician conversations.
result Cluster2Sent algorithm outperforms existing methods by 8 ROUGE-1 points.
Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their dec…
The paper integrates AI and expert knowledge to optimize radiotherapy decisions.
problem Optimizing radiation dose planning considering patient-specific information.
method Integrating Gaussian process models with deep neural networks to quantify uncertainty.
result Improves AI model performance and guides clinical decision making.
Model improves diagnosis from incomplete lab test data.
problem Incomplete and longitudinal lab test data challenges diagnosis accuracy.
method Joint training of generative VRNN and discriminative NN models.
result VRNN+NN significantly outperforms baseline models in diagnosis accuracy.
Simplifies decision-making during medical exams with cost-efficient feature acquisition.
problem Guiding physicians during examination acquisition for accurate and efficient diagnosis.
method Dropout at input layer and integrated gradients at test-time for dynamic feature importance.
result More cost- and feature-efficient than prior approaches, achieving higher overall accuracy.
A neural network predicts medical codes from clinical notes with high accuracy and interpretability.
problem Lack of automated and accurate annotation of medical codes from clinical notes.
method Attentional Convolutional Network that aggregates and selects relevant segments of clinical notes.
result Achieved precision@8 of 0.71 and Micro-F1 of 0.54, better than prior methods.
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.
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.
Deep learning model predicts CXR findings from patient scans.
problem Insufficient radiologists for accurate CXR interpretation.
method Tagged 959k studies, trained deep learning model on 2M reports.
result Model performs similarly to radiologists on 12 findings.
ProtoPNet uses deep learning to classify images by identifying prototypical parts.
problem Challenging image classification tasks where understanding reasoning is important.
method ProtoPNet architecture that reasons by finding prototypical parts and combining evidence.
result ProtoPNet achieves comparable accuracy to non-interpretable models and provides interpretability.
Study predicts blood pressure response to fluid bolus therapy with high accuracy.
problem Predicting successful response to fluid bolus therapy in hypotensive ICU patients.
method Used attention-based LSTM and GRU neural networks on a large ICU database.
result Stacked LSTM with attention mechanism achieved highest accuracy of 0.852.
Bayesian model helps doctors diagnose by mixing past cases.
problem Doctors rely on past experience for diagnosis.
method Developed a novel mathematical model to mimic case-based reasoning.
result Yields predictive accuracy and insight into medical data.
Study uses multimodal machine learning to predict ICD-10 codes.
problem Improving accuracy and interpretability of ICD-10 code predictions.
method Developed separate models for text and tabular data, integrated using an ensemble method.
result Best-performing model achieved micro-F1 of 0.7633 and micro-AUC of 0.9541.
This paper interprets neural network ECG models by breaking them into understandable components.
problem Lack of model interpretability in deep learning for medical applications.
method Factorizes neural network models into interpretable black box components using hierarchical equations.
result Demonstrates interpretable component models for ECG waveforms, improving model understanding and predictability.
Develops a test to assess if human experts add value to predictions.
problem Detecting if human expertise adds value to predictions.
method Statistical framework and hypothesis test to assess independence of expert predictions from outcomes.
result Physicians' decisions for AGIB patients incorporate information not available to a screening tool.
New dataset from clinicians improves sepsis prediction models.
problem Circularity in previous sepsis prediction models.
method Developed an independent dataset from clinical judgments, avoiding circularity.
result Achieved state-of-the-art AUROC scores.
This paper provides a ML framework for diabetes prediction and care management.
problem Diabetes prediction and care management challenges in real-world healthcare.
method Illustrates a Machine Learning framework for T2DM prediction and risk stratification.
result ML models align with physician's disease management steps.
Model predicts VKA dosage for Indian patients, aiding in safe medication.
problem Safe and accurate dosing of VKA drugs for Indian patients.
method Support Vector Machine (SVM) Regression model trained on patient data.
result Predicted dosages closely match actual dosages.
Study generates synthetic MR images to improve glioma segmentation.
problem Scarce annotated medical image data for glioma patients.
method Used generative adversarial networks (GANs) to create synthetic MR images.
result Synthetic images improved U-Net segmentation performance.
Special curves and their characterizations are one of the main area of mathematicians and physicians. As a special curve we will mainly focus on Mannheim curve which has the following relation: k1=β(k1^2+k2^) where k1 and k2 are curvature and torsion, respectively. In the present paper we define Mannheim curves for 4-d…
Paper presents an ensemble model for predicting readmission using clinical notes.
problem Limited use of clinical notes in predicting readmission due to their unstructured nature.
method Ensemble model combining vector space modeling and topic modeling.
result Improves readmission prediction by 0.0211 in c-statistics.
Framework uses human judgment to distinguish algorithmically indistinguishable cases.
problem Clarifying human-AI collaboration in prediction and decision tasks.
method Integrates human judgment to distinguish algorithmically indistinguishable cases.
result Improves performance of any feasible algorithmic predictor.
Detects physiological patterns to hemodynamic stress using unsupervised deep learning.
problem Identify and characterize physiological responses to hemorrhage in raw vital sign data.
method Transform vital sign time series into latent space using unsupervised deep learning, identify clusters, and evaluate latent embeddings.
result Clusters in latent embeddings correspond to physiological response patterns matching physicians' intuition.
CLIP dataset helps extract action items from hospital discharge notes.
problem Lost action items in long discharge notes hinder information sharing.
method Created CLIP dataset of annotated discharge notes, used multi-aspect extractive summarization, trained models on pre-trained language models and context.
result Best models improved by incorporating context and pre-trained language models.
Probabilistic network aids in diagnosing acute cardiopulmonary diseases.
problem Improving accuracy in diagnosing acute cardiopulmonary diseases.
method Developed a probabilistic network using a directed acyclic graph and Bayesian paradigm.
result The probabilistic network provided satisfactory Concordance Index values for acute diseases and reasonable inference on patient cases.
Meta-learners estimate CATE from multiple environments with partial identification.
problem Estimating CATE from observational data across multiple environments with violations of causal assumptions.
method Adapt IV literature for partial identification, propose model-agnostic meta-learners.
result Meta-learners effectively estimate CATE bounds across various experiments.
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.
AI virtual doctor predicts diabetes from non-invasive data.
problem Limited access to primary medical care in rural areas.
method Interactive AI system with speech recognition and synthesis, deep neural networks.
result System accurately predicts type 2 diabetes.
A GAN variant synthesizes missing MRI sequences from available ones.
problem Missing MRI sequences due to various constraints.
method Multi-modal Generative Adversarial Network (GAN) that combines multiple available sequences to synthesize missing ones.
result The proposed GAN method outperforms competing approaches in synthesizing missing MRI sequences.
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.
This guide introduces machine learning for medical data analysis.
problem Handling large, complex datasets in healthcare.
method Explains common machine learning algorithms and tasks.
result Illustrates machine learning applications in healthcare.
MedGCN uses graph convolutional networks to recommend medications and estimate lab tests.
problem Automatically recommend medications and estimate lab tests for cost savings and better patient care.
method Integrates heterogeneous graph relations, learns node embeddings with graph convolutional networks, and uses cross regularization for multi-task learning.
result MedGCN outperforms state-of-the-art models in both medication recommendation and lab test imputation on real-world datasets.
PHI Scrubber removes sensitive patient info from medical notes.
problem Protecting patient privacy in electronic health records.
method Deep learning model with de-convolutional neural network and bi-directional LSTM-CNN.
result Eliminates individually identifiable information from physician notes.
Deep reinforcement learning improves medical test suggestions.
problem Improving accuracy of disease diagnosis through better medical test recommendations.
method Formulated as a stage-wise Markov decision process, trained using reinforcement learning with a new action policy representation and exploration scheme.
result Significant improvement in disease diagnosis accuracy with better medical test suggestions.
Paper uses a mix of deep and kernel learning to personalize sepsis treatment.
problem Managing sepsis in ICU patients due to individual variability.
method A mixture-of-experts framework combining kernel-based and deep reinforcement learning.
result The mixture-based approach outperforms individual methods on a large sepsis patient cohort.
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.
Deep neural network improves EEG-based epilepsy diagnosis.
problem Automated identification of seizure onset zones in epilepsy patients.
method Residual deep convolutional neural network trained on raw EEG data.
result State-of-the-art performance on epilepsy classification benchmarks.
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.
We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We com…
CRC method provides tighter uncertainty intervals for CT images.
problem Expressing uncertainty in CT images in clinically meaningful terms.
method Semantically adaptive CRC procedure leveraging length minimization.
result Valid coverage of ground-truth images with tighter uncertainty intervals.