Query-Focused EHR Summarization to Aid Imaging Diagnosis
- URL: http://arxiv.org/abs/2004.04645v2
- Date: Sun, 26 Apr 2020 04:25:30 GMT
- Title: Query-Focused EHR Summarization to Aid Imaging Diagnosis
- Authors: Denis Jered McInerney, Borna Dabiri, Anne-Sophie Touret, Geoffrey
Young, Jan-Willem van de Meent, Byron C. Wallace
- Abstract summary: We propose and evaluate models that extract relevant text snippets from patient records to provide a rough case summary.
We use groups of International Classification of Diseases (ICD) codes observed in 'future' records as noisy proxies for 'downstream' diagnoses.
We train (via distant supervision) and evaluate variants of this model on EHR data from Brigham and Women's Hospital in Boston and MIMIC-III.
- Score: 22.21438906817433
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Electronic Health Records (EHRs) provide vital contextual information to
radiologists and other physicians when making a diagnosis. Unfortunately,
because a given patient's record may contain hundreds of notes and reports,
identifying relevant information within these in the short time typically
allotted to a case is very difficult. We propose and evaluate models that
extract relevant text snippets from patient records to provide a rough case
summary intended to aid physicians considering one or more diagnoses. This is
hard because direct supervision (i.e., physician annotations of snippets
relevant to specific diagnoses in medical records) is prohibitively expensive
to collect at scale. We propose a distantly supervised strategy in which we use
groups of International Classification of Diseases (ICD) codes observed in
'future' records as noisy proxies for 'downstream' diagnoses. Using this we
train a transformer-based neural model to perform extractive summarization
conditioned on potential diagnoses. This model defines an attention mechanism
that is conditioned on potential diagnoses (queries) provided by the diagnosing
physician. We train (via distant supervision) and evaluate variants of this
model on EHR data from Brigham and Women's Hospital in Boston and MIMIC-III
(the latter to facilitate reproducibility). Evaluations performed by
radiologists demonstrate that these distantly supervised models yield better
extractive summaries than do unsupervised approaches. Such models may aid
diagnosis by identifying sentences in past patient reports that are clinically
relevant to a potential diagnosis.
Related papers
- HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis [38.13689106933105]
We present HIST-AID, a framework that enhances automatic diagnostic accuracy using historical reports.
Our experiments demonstrate significant improvements, with AUROC increasing by 6.56% and AUPRC by 9.51% compared to models that rely solely on radiographic scans.
arXiv Detail & Related papers (2024-11-16T03:20:53Z) - MAGDA: Multi-agent guideline-driven diagnostic assistance [43.15066219293877]
In emergency departments, rural hospitals, or clinics in less developed regions, clinicians often lack fast image analysis by trained radiologists.
In this work, we introduce a new approach for zero-shot guideline-driven decision support.
We model a system of multiple LLM agents augmented with a contrastive vision-language model that collaborate to reach a patient diagnosis.
arXiv Detail & Related papers (2024-09-10T09:10:30Z) - A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation [12.617587827105496]
This research aims to bridge the gap by providing publicly accessible datasets and reliable tools for medical diagnosis.
We curated a diverse dataset of lung Computed Tomography (CT) images, comprising 330 annotated nodules (nodules are labeled as bounding boxes) from 95 distinct patients.
These promising results demonstrate that the dataset has a feasible application and further facilitate intelligent auxiliary diagnosis.
arXiv Detail & Related papers (2024-06-26T06:39:11Z) - Towards Reducing Diagnostic Errors with Interpretable Risk Prediction [18.474645862061426]
We propose a method to use LLMs to identify pieces of evidence in patient EHR data that indicate increased or decreased risk of specific diagnoses.
Our ultimate aim is to increase access to evidence and reduce diagnostic errors.
arXiv Detail & Related papers (2024-02-15T17:05:48Z) - Radiology Report Generation Using Transformers Conditioned with
Non-imaging Data [55.17268696112258]
This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information.
The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
arXiv Detail & Related papers (2023-11-18T14:52:26Z) - ChatRadio-Valuer: A Chat Large Language Model for Generalizable
Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations.
The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations.
ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
arXiv Detail & Related papers (2023-10-08T17:23:17Z) - Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis [36.45569352490318]
We introduce Xplainer, a framework for explainable zero-shot diagnosis in the clinical setting.
Xplainer adapts the classification-by-description approach of contrastive vision-language models to the multi-label medical diagnosis task.
Our results suggest that Xplainer provides a more detailed understanding of the decision-making process.
arXiv Detail & Related papers (2023-03-23T16:07:31Z) - Exploring and Distilling Posterior and Prior Knowledge for Radiology
Report Generation [55.00308939833555]
The PPKED includes three modules: Posterior Knowledge Explorer (PoKE), Prior Knowledge Explorer (PrKE) and Multi-domain Knowledge Distiller (MKD)
PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias.
PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias.
arXiv Detail & Related papers (2021-06-13T11:10:02Z) - Variational Knowledge Distillation for Disease Classification in Chest
X-Rays [102.04931207504173]
We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays.
We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
arXiv Detail & Related papers (2021-03-19T14:13:56Z) - BiteNet: Bidirectional Temporal Encoder Network to Predict Medical
Outcomes [53.163089893876645]
We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey.
An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys.
We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
arXiv Detail & Related papers (2020-09-24T00:42:36Z) - Towards Causality-Aware Inferring: A Sequential Discriminative Approach
for Medical Diagnosis [142.90770786804507]
Medical diagnosis assistant (MDA) aims to build an interactive diagnostic agent to sequentially inquire about symptoms for discriminating diseases.
This work attempts to address these critical issues in MDA by taking advantage of the causal diagram.
We propose a propensity-based patient simulator to effectively answer unrecorded inquiry by drawing knowledge from the other records.
arXiv Detail & Related papers (2020-03-14T02:05:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.