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.
Machine learning constructs problem-based medical records from electronic health records.
ME2Vec learns medical entity vectors from EHR data.
Visual analytics system for comparing medical records using sequence embeddings.
Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that …
AI predicts medical specialty diagnostic choices from EHR records.
Paper uses ensemblers to predict sepsis early from patient records.
XLabel tool reduces medical experts' workload by 40% and explains its decisions.
Method uses semi-supervised learning to estimate optimal treatment regimes from medical records.
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…
MedGraph learns patient visit embeddings from EMRs, capturing both attributes and temporal sequences.
Study predicts colorectal polyp recurrence using medical records and statistical models.
The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. …
Paper tackles missing data in medical records using advanced optimization.
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
Regular medical records are useful for medical practitioners to analyze and monitor patient health status especially for those with chronic disease, but such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based model is suggest…
Paper proposes a new method to handle missing data in medical records using sequential variational autoencoders.
De-identification of clinical records is an extremely important process which enables the use of the wealth of information present in them. There are a lot of techniques available for this but none of the method implementation has evaluated the scalability, which is an important benchmark. We evaluated numerous deep le…
Is it true that patients with similar conditions get similar diagnoses? In this paper we show NLP methods and a unique corpus of documents to validate this claim. We (1) introduce a method for representation of medical visits based on free-text descriptions recorded by doctors, (2) introduce a new method for clustering…
Framework harmonizes EHR data across institutions for better analysis.
Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes. They are preempted by medical co…
Deep learning predicts readmissions from less structured data.
New research finds uncertainty estimation techniques fail to reliably detect abnormal medical cases.
This paper presents an example of how demographical characteristics of patients influence their susceptibility to certain medical conditions. In this paper, we investigate the association of health conditions to age of patients in a heterogeneous population. We show that besides the symptoms a patients is having, the a…
Paper proposes a graph network for EHR data that learns robust representations.
This paper benchmarks privacy-preserving machine learning on medical images.
ODVICE augments EHR cohorts using ontology to improve analysis robustness.
New method uses probabilistic independence to discover disease signatures from medical records.
Paper proposes sharing models instead of data for smart health predictions.
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…
The paper presents a systematic review of state-of-the-art approaches to identify patient cohorts using electronic health records. It gives a comprehensive overview of the most commonly de-tected phenotypes and its underlying data sets. Special attention is given to preprocessing of in-put data and the different modeli…
Model predicts patient trajectories and interventions from EMR data.
MLHO predicts COVID-19 adverse outcomes using past medical records.
Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems. External resources such as medical ontologies are used to bridge the da…
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
Modeling disease progression using irregular time intervals in EHRs.
Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity resea…
Managing patients with chronic diseases is a major and growing healthcare challenge in several countries. A chronic condition, such as diabetes, is an illness that lasts a long time and does not go away, and often leads to the patient's health gradually getting worse. While recent works involve raw electronic health re…
Hi-RES framework extracts medical relations from articles and EHRs.
We study the problem of detecting adverse drug events in electronic healthcare records. The challenge in this work is to aggregate heterogeneous data types involving diagnosis codes, drug codes, as well as lab measurements. An earlier framework proposed for the same problem demonstrated promising predictive performance…
Deep learning predicts opioid use disorder risk in patients.
Paper detects bias in AI medical models using CART.
ConCare personalizes healthcare predictions by capturing EMR features.
New method generates diverse EHR data types while maintaining privacy.
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…
CovidCare uses EMR data to predict patient outcomes in emerging epidemics.
There is growing interest in applying machine learning methods to Electronic Medical Records (EMR). Across different institutions, however, EMR quality can vary widely. This work investigated the impact of this disparity on the performance of three advanced machine learning algorithms: logistic regression, multilayer p…
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.
We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is…