Hybrid system matches patients with family doctors based on trust and history.
problem Matching patients with suitable family doctors in primary care.
method Hybrid recommender system combining patient trust from consultation histories and temporal dynamics.
result Predictive accuracy is higher than heuristic and collaborative filtering approaches, and trust measure improves performance.
Doctor2Vec learns doctor representations from EHRs for better clinical trial recruitment.
problem Identifying the right doctors for clinical trials based on EHR data and trial descriptions.
method Dynamic Memory Network with attention mechanism to learn doctor and trial representations.
result Improved performance by up to 8.7% in PR-AUC on real-world trials and EHR data.
New method to estimate doctors' effort in annotating medical images.
problem High effort and expense in annotating medical images.
method Proposes a new criterion to evaluate effort, uses active learning and U-shape network for annotation strategy, and fine annotation platform to reduce effort.
result State-of-the-art segmentation performance achieved with only 60% annotation candidates, reducing effort by 44-47%.
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We per…
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.
In his 1990 doctoral thesis, Todd Drumm showed that proper affine deformations of free Fuchsian groups could be constructed as Schottky groups using a new family of hypersurfaces called "crooked planes." The existence of proper affine deformations of Fuchsian Schottky groups was demonstrated by Margulis in the early 19…
CLARA generates clinical reports from raw inputs, improving accuracy and efficiency.
problem Generating accurate and detailed clinical reports from raw inputs is time-consuming and error-prone.
method Interactive method that generates reports sentence by sentence based on doctors' anchor words and partially completed sentences.
result CLARA achieves significant improvements in report generation accuracy and efficiency.
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.
Automated system extracts medication regimens from medical conversations.
problem Extract relevant medication information from medical conversations.
method QA task approach, combined QA and Information Extraction, data augmentation, public embeddings, pretraining.
result Improved accuracy in extracting dosage and frequency from 54.28 and 37.13 to 89.57 and 45.94.
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…
Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.
problem Time-consuming and high error rate in manual inspection of large numbers of similar capsule endoscopic images.
method Structural similarity analysis of visually salient areas and hierarchical clustering.
result 76% reduction in similar images, 100% lesion recall, 18-minute average play time.
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.
Paper uses NLP to cluster patient visits for diagnosis validation.
problem Validating if similar patients receive similar diagnoses.
method Representation of medical visits using word embeddings, clustering patients' visits.
result Stable and separated segments of visits positively validated against diagnoses.
Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We …
Circle packings on compact surfaces simplified.
problem Simplifying circle packings on complex surfaces.
method Uniformisation of weighted maps.
result Unified approach to circle packings.
Paper uses AI to improve medical diagnosis accuracy.
problem Improving accuracy of medical diagnoses.
method Heuristic frequentist and Bayesian approaches applied to a nationwide dataset.
result Algorithm outperforms human doctors in detecting abnormal births.
We develop a model using deep learning techniques and natural language processing on unstructured text from medical records to predict hospital-wide 30-day unplanned readmission, with c-statistic .70. Our model is constructed to allow physicians to interpret the significant features for prediction.
A method for data encryption makes data look identical to humans but misleading to machine learning.
problem Data leakage in data sharing for medical purposes.
method Proposes a method inspired by adversarial attacks for data encryption.
result Encrypted data look identical to humans but misleading to machine learning methods.
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.
The paper investigates deep neural networks for medical imaging applications, providing interpretable results.
problem Uninterpretable decisions made by deep neural networks in medical imaging applications.
method Investigation of deep neural networks for malaria, diabetic retinopathy, brain tumor, and tuberculosis detection in various imaging modalities. Visualization of class activation mappings provided.
result Visualization of class activation mappings enhances understanding of deep neural networks and aids doctors in decision-making.
Ph.D. thesis on complex Brunn-Minkowski theory using Hilbert bundles.
problem Complex Brunn-Minkowski theory.
method Hilbert bundles approach.
result New insights into complex Brunn-Minkowski theory.
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.
Deep neural network predicts ECG abnormalities from short-duration exams.
problem Improving accuracy of ECG diagnosis from short-duration exams.
method Residual neural network with 9 convolutional layers trained on large dataset.
result Model outperformed medical doctors on ECG abnormalities.
Maryam Mirzakhani (in her doctoral dissertation) has proved the author's conjecture that the number of simple curves of length bounded by L on a hyperbolic surface S is assymptotic to a constant times L to the power d, where d is the dimension of the Teichmuller space of S. In this note we clarify and simplify Mirzakha…
Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforc…
Dr. of Crosswise reduces over-parametrization in neural networks.
problem Reduction of over-parametrization in neural networks.
method Introduces a new operand for rapid computation in neural networks framework.
result Reduces over-parametrization in neural networks.
Survey on curvature bounds and isoperimetric inequalities.
problem Interplay between curvature bounds and isoperimetric problems.
method Survey of classical and recent results.
result Recent developments on curvature bounds and isoperimetric problems.
V-Net speeds up brain tumor segmentation in MRI scans.
problem Manual tumor segmentation is time-consuming and inaccurate.
method Applied a volumetric, fully convolutional neural network (V-Net) to MRI scans.
result Achieved a whole tumor dice score of 0.89.
In this short note, we compute the Betti numbers of the moduli stack of flat SU(3)-bundles over a Klein bottle. We also handle the general compact group case over RP^2. In all cases the cohomology is found to be equivariantly formal, supporting a conjecture from the author's doctoral thesis. Our results also verify con…
Explains Bernstein theorems for various geometric PDEs.
problem Bernstein problem for minimal surface, Monge-Ampère, and special Lagrangian equations.
method Expository review of existing theorems and systems.
result Discussion of Bernstein theorems for different geometric PDEs.
Develops panoramic gastroscopy for automatic polyp detection.
problem Missed diagnosis of gastric polyps during endoscopy.
method Panoramic reconstruction method and end-to-end multi-object detection.
result Average error of panorama less than 2 mm, polyp detection accuracy 95%, recall rate 99%.
System converts 3D lung nodule images into embeddings for retrieval.
problem Retrieving similar 3D lung nodule images for radiologist decision support.
method 3D deep learning, semantic representation, transfer learning, similarity score.
result System can measure similarity between nodule annotations and CBIR results.
Paper proposes methods to handle missing values in clinical data.
problem Missing Not At Random (MNAR) data in clinical studies.
method Model-based and surrogate estimation strategies for low-rank matrix completion.
result Proposed methods can handle different missing value mechanisms robustly.
The paper shows how expert knowledge can improve treatment effect estimation.
problem Lack of leveraging expert knowledge in treatment effect estimation.
method Formally defining two types of expertise (predictive and prognostic) and demonstrating their influence on treatment effect estimation methods.
result Expertise type significantly influences treatment effect estimation methods, and can be predicted from a dataset.
Paper assesses holistic risks of inference attacks on ML models.
problem Lack of comprehensive risk assessment of inference attacks on ML models.
method Presented a threat model taxonomy for four inference attacks on five model architectures and four image datasets.
result Complexity of training dataset influences attack performance; model stealing and membership inference attacks are negatively correlated.
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.
Study proves curvature estimates for Kerr spacetime's linearized perturbations.
problem Proving elliptic L2(S2)-estimates for linearised curvature quantities in Kerr spacetime. method Applies linearised system from doctoral thesis, covers full sub-extremal range of Kerr parameters.
result Elliptic L2(S2)-estimates for linearised curvature quantities in the full sub-extremal range of Kerr parameters. ME2Vec learns medical entity vectors from EHR data.
problem Learning structured information from EHR data.
method Graph-based medical entity embedding using diverse graph embedding techniques.
result ME2Vec outperforms baselines in disease diagnosis prediction.
Chern-Simons invariants of closed oriented Riemannian 3-manifolds are introduced and studied from the basics. Their first-order variation is the Cotton tensor. The properties of the Cotton tensor: symmetry, conformal covariance, trace- and divergence-freedom, are recovered as corollaries of the Chern-Simons invariant…
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations (14,000 hr), designed the task to represent the real word scenario, and explored several alignment approaches to iteratively improve data qua…
Project aims to diagnose and track IPF disease using deep learning.
problem Diagnosing and tracking IPF disease in lung images.
method Developed a deep learning model for identifying honeycombing and ground glass patterns in HRCT lung images.
result Deep learning model achieved high accuracy in identifying specific lung regions.
Survey on minimal surface equation on Cartan-Hadamard manifolds.
problem Asymptotic Dirichlet problem for minimal surface equation.
method Introduction of Cartan-Hadamard manifolds, discussion of results and methods used in proofs.
result Discussion of results and methods used in proofs.
Geometrically revisits and models homogeneous spaces of compact Lie group G2.
problem Classifying homogeneous reductive spaces of compact Lie group G2. method Geometrical approach to revisit and model the spaces.
result Explicit relations among geometric models of the spaces.
Study analyzes factors influencing healthcare providers' engagement with SMS campaigns.
problem Understanding what drives healthcare providers to engage with SMS campaigns.
method Used logistic regression, random forest, and neural network models to analyze data.
result Identified key factors influencing engagement with SMS campaigns.
Introduces a new equivalence for singular foliations and their groupoids.
problem Preserving transverse geometry in singular foliations.
method Introduces a new equivalence relation for singular foliations and connects it to holonomy groupoids.
result Establishes a connection between singular foliations and their associated holonomy groupoids.
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.
The paper analyzes how incentives and information affect ROC curves in medical diagnosis.
problem Understanding how incentives and information asymmetry influence ROC curves in medical diagnosis.
method Theoretical analysis and illustration with a real-world dataset.
result Different models of incentives and information asymmetry affect the relationship between human decisions and ROC curves.
A clinical Meta-Dataset from TCGA for multi-task learning.
problem Clinical decision making requires considering multiple factors; current benchmarks lack consistency and variety.
method Developed a Meta-Dataset with 174 tasks from TCGA, using regression and neural networks.
result Demonstrated the feasibility of predicting multiple clinical variables from gene expression data.