Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userModeling Diagnostic Label Correlation for Automatic ICD Coding
نمذجة العلاقة التسمية التشخيصية لترميز ICD التلقائي
269
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
بالنظر إلى الملاحظات السريرية المكتوبة في السجلات الصحية الإلكترونية (EHRS)، فمن الصعب التنبؤ برموز التشخيص التي يتم صياغة مهمة تصنيف متعدد العلامات.مجموعة كبيرة من الملصقات، والاعتماد الهرمي، والبيانات غير المتوازنة تجعل مهمة التنبؤ هذه صعبة للغاية.قامت معظم الأعمال الحالية ببناء التنبؤ الثنائي لكل تسمية بشكل مستقل، مما يتجاهل التبعيات بين الملصقات.لمعالجة هذه المشكلة، نقترح إطارا من مرحلتين لتحسين ترميز ICD التلقائي عن طريق التقاط ارتباط التسمية.على وجه التحديد، ندرب مقدر توزيع مجموعة التسمية لإنقاش احتمالية كل مرشح تعيين كل ملصق تم إنشاؤه بواسطة مؤشر أساسي.هذه الورقة هي المحاولة الأولى لتعلم توزيع مجموعة التسمية كوحدة Reranking لترميز ICD.في التجارب، يكون إطار عملنا المقترح قادرا على تحسين تنبؤات أفضل أداء لتنبؤ الكود الطبي في مجموعات البيانات المقلدة القياسية.
Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical dependency, and the imbalanced data make this prediction task extremely hard. Most existing work built a binary prediction for each label independently, ignoring the dependencies between labels. To address this problem, we propose a two-stage framework to improve automatic ICD coding by capturing the label correlation. Specifically, we train a label set distribution estimator to rescore the probability of each label set candidate generated by a base predictor. This paper is the first attempt at learning the label set distribution as a reranking module for ICD coding. In the experiments, our proposed framework is able to improve upon best-performing predictors for medical code prediction on the benchmark MIMIC datasets.
References used
https://aclanthology.org/
rate research
Read More
ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient's diagnosis and treatments are annotated with multiple ICD-9 codes. Automated ICD-9 coding is an active research field, where CNN- and RNN-bas
ed model architectures represent the state-of-the-art approaches. In this work, we propose a description-based label attention classifier to improve the model explainability when dealing with noisy texts like clinical notes.
The International Classification of Diseases (ICD) is a system for systematically recording patients' diagnoses. Clinicians or professional coders assign ICD codes to patients' medical records to facilitate funding, research, and administration. In m
ost health facilities, clinical coding is a manual, time-demanding task that is prone to errors. A tool that automatically assigns ICD codes to free-text clinical notes could save time and reduce erroneous coding. While many previous studies have focused on ICD coding, research on Swedish patient records is scarce. This study explored different approaches to pairing Swedish clinical notes with ICD codes. KB-BERT, a BERT model pre-trained on Swedish text, was compared to the traditional supervised learning models Support Vector Machines, Decision Trees, and K-nearest Neighbours used as the baseline. When considering ICD codes grouped into ten blocks, the KB-BERT was superior to the baseline models, obtaining an F1-micro of 0.80 and an F1-macro of 0.58. When considering the 263 full ICD codes, the KB-BERT was outperformed by all baseline models at an F1-micro and F1-macro of zero. Wilcoxon signed-rank tests showed that the performance differences between the KB-BERT and the baseline models were statistically significant.
The objective of this research is to insist on the most important symptoms and signs
of appendicitis and diagnosis in a timely manner to avoid complications and deaths. The
aim of the study was to establish diagnostic criteria for acute appendiciti
s in children in the
light of the data obtained from the study of a large number of patients with appendicitis
and to determine the surgical indication to reduce complications and negative abdominal
opening and to include the studied criteria in a spec
ial scale in our patients to evaluate children with acute appendicitis depending On
this scale. The study included 104 children who visited the Pediatric emergency
department at Al-Assad University and University Hospital and Tishreen university
Hospital with a complaint of abdominal pain between 2014 and 2016. The number of male
patients was 76 patients and 28 females, abdominal pain was present in all patients, The
following criteria were studied (abdominal pain, abdominal tenderness, rebound
tenderness, ultra sound, leukocytosis, shift to left, fever,crp). These criteria were studied to
diagnose acute appendicitis and to put them in a special scale for use in diagnosis or denial
acute appendicitis. In comparison with international standards, we observed that the scale
studied in our study is more objective and eliminates the subjective factor in the diagnosis
of appendicitis.
Item categorization is an important application of text classification in e-commerce due to its impact on the online shopping experience of users. One class of text classification techniques that has gained attention recently is using the semantic in
formation of the labels to guide the classification task. We have conducted a systematic investigation of the potential benefits of these methods on a real data set from a major e-commerce company in Japan. Furthermore, using a hyperbolic space to embed product labels that are organized in a hierarchical structure led to better performance compared to using a conventional Euclidean space embedding. These findings demonstrate how label-guided learning can improve item categorization systems in the e-commerce domain.
Recent metaphor identification approaches mainly consider the contextual text features within a sentence or introduce external linguistic features to the model. But they usually ignore the extra information that the data can provide, such as the cont
extual metaphor information and broader discourse information. In this paper, we propose a model augmented with hierarchical contextualized representation to extract more information from both sentence-level and discourse-level. At the sentence level, we leverage the metaphor information of words that except the target word in the sentence to strengthen the reasoning ability of our model via a novel label-enhanced contextualized representation. At the discourse level, the position-aware global memory network is adopted to learn long-range dependency among the same words within a discourse. Finally, our model combines the representations obtained from these two parts. The experiment results on two tasks of the VUA dataset show that our model outperforms every other state-of-the-art method that also does not use any external knowledge except what the pre-trained language model contains.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
