A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method to estimate doctors' effort in annotating medical images.
Doctor2Vec learns doctor representations from EHRs for better clinical trial recruitment.
We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care. We define the matchmaking process for several distinct use cases given different levels of available information about patients. Then, we adopt a hybrid recommender system to present each…
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.
Population attributes are essential in health for understanding who the data represents and precision medicine efforts. Even within disease infection labels, patients can exhibit significant variability; "fever" may mean something different when reported in a doctor's office versus from an online app, precluding direct…
CLARA generates clinical reports from raw inputs, improving accuracy and efficiency.
Automated system extracts medication regimens from medical conversations.
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…
Doctors often rely on their past experience in order to diagnose patients. For a doctor with enough experience, almost every patient would have similarities to key cases seen in the past, and each new patient could be viewed as a mixture of these key past cases. Because doctors often tend to reason this way, an efficie…
Method screens similar capsule endoscopic images, reducing doctor workload and improving accuracy.
AI virtual doctor predicts diabetes from non-invasive data.
Paper uses NLP to cluster patient visits for diagnosis validation.
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 …
It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. More…
Circle packings on compact surfaces simplified.
Paper uses AI to improve medical diagnosis accuracy.
We develop a model using deep learning techniques and natural language processing on unstructured text from medical records to predict hospital-wide -day unplanned readmission, with c-statistic . 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.
We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning pap…
The paper investigates deep neural networks for medical imaging applications, providing interpretable results.
Ph.D. thesis on complex Brunn-Minkowski theory using Hilbert bundles.
Brain cancer can be very fatal, but chances of survival increase through early detection and treatment. Doctors use Magnetic Resonance Imaging (MRI) to detect and locate tumors in the brain, and very carefully analyze scans to segment brain tumors. Manual segmentation is time consuming and tiring for doctors, and it ca…
Paper introduces methods to automatically generate SOAP notes from patient-physician conversations.
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.
Survey on curvature bounds and isoperimetric inequalities.
A hybrid deep learning model improves ESD diagnosis accuracy.
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.
Develops panoramic gastroscopy for automatic polyp detection.
System converts 3D lung nodule images into embeddings for retrieval.
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…
The paper shows how expert knowledge can improve treatment effect estimation.
The paper proposes a method to measure fairness through equality of effort using algorithmic recourse.
Paper assesses holistic risks of inference attacks on ML models.
Unsupervised anomaly detection aids doctors in evaluating X-ray images of hands.
Study proves curvature estimates for Kerr spacetime's linearized perturbations.
In recent years, besides the medical treatment methods in medical field, Computer Aided Diagnosis (CAD) systems which can facilitate the decision making phase of the physician and can detect the disease at an early stage have started to be used frequently. The diagnosis of Idiopathic Pulmonary Fibrosis (IPF) disease by…
Firefly algorithm improves software effort estimation models.
Computer graphics techniques improve art pricing by measuring painting effort.
ME2Vec learns medical entity vectors from EHR data.
Reduces test set maintenance effort by 80-100%.
Chern-Simons invariants of closed oriented Riemannian -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…
Replication study shows Deep-SE still not as effective as previously thought for agile effort estimation.
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations ( hr), designed the task to represent the real word scenario, and explored several alignment approaches to iteratively improve data qua…
Survey on minimal surface equation on Cartan-Hadamard manifolds.