Collaborative residual learners for automatic icd10 prediction using prescribed medications

• URL: http://arxiv.org/abs/2012.11327v1
• Date: Wed, 16 Dec 2020 07:07:27 GMT
• Title: Collaborative residual learners for automatic icd10 prediction using prescribed medications
• Authors: Yassien Shaalan, Alexander Dokumentov, Piyapong Khumrin, Krit Khwanngern, Anawat Wisetborisu, Thanakom Hatsadeang, Nattapat Karaket, Witthawin Achariyaviriya, Sansanee Auephanwiriyakul, Nipon Theera-Umpon, Terence Siganakis
• Abstract summary: We propose a novel collaborative residual learning based model to automatically predict ICD10 codes employing only prescriptions data. We obtain multi-label classification accuracy of 0.71 and 0.57 of average precision, 0.57 and 0.38 of F1-score and 0.73 and 0.44 of accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
• Score: 45.82374977939355
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Clinical coding is an administrative process that involves the translation of diagnostic data from episodes of care into a standard code format such as ICD10. It has many critical applications such as billing and aetiology research. The automation of clinical coding is very challenging due to data sparsity, low interoperability of digital health systems, complexity of real-life diagnosis coupled with the huge size of ICD10 code space. Related work suffer from low applicability due to reliance on many data sources, inefficient modelling and less generalizable solutions. We propose a novel collaborative residual learning based model to automatically predict ICD10 codes employing only prescriptions data. Extensive experiments were performed on two real-world clinical datasets (outpatient & inpatient) from Maharaj Nakorn Chiang Mai Hospital with real case-mix distributions. We obtain multi-label classification accuracy of 0.71 and 0.57 of average precision, 0.57 and 0.38 of F1-score and 0.73 and 0.44 of accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.

Related papers

• Large language models are good medical coders, if provided with tools [0.0]
  This study presents a novel two-stage Retrieve-Rank system for automated ICD-10-CM medical coding. evaluating both systems on a dataset of 100 single-term medical conditions. The Retrieve-Rank system achieved 100% accuracy in predicting correct ICD-10-CM codes.
  arXiv  Detail & Related papers  (2024-07-06T06:58:51Z)
• Automated clinical coding using off-the-shelf large language models [10.365958121087305]
  The task of assigning diagnostic ICD codes to patient hospital admissions is typically performed by expert human coders. Efforts towards automated ICD coding are dominated by supervised deep learning models. In this work, we leverage off-the-shelf pre-trained generative large language models to develop a practical solution.
  arXiv  Detail & Related papers  (2023-10-10T11:56:48Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• Time-dependent Iterative Imputation for Multivariate Longitudinal Clinical Data [0.0]
  Time-Dependent Iterative imputation offers a practical solution for imputing time-series data. When applied to a cohort consisting of more than 500,000 patient observations, our approach outperformed state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2023-04-16T16:10:49Z)
• TrialGraph: Machine Intelligence Enabled Insight from Graph Modelling of Clinical Trials [0.0]
  We introduce a curated clinical trial data set compiled from the CT.gov, AACT and TrialTrove databases (n=1191 trials; representing one million patients) We then detail the mathematical basis and implementation of a selection of graph machine learning algorithms. We trained these models to predict side effect information for a clinical trial given information on the disease, existing medical conditions, and treatment.
  arXiv  Detail & Related papers  (2021-12-15T15:36:57Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Ensemble model for pre-discharge icd10 coding prediction [45.82374977939355]
  We propose an ensemble model incorporating multiple clinical data sources for accurate code predictions. We obtain multi-label classification accuracies of 0.73 and 0.58 for average precision, 0.56 and 0.35 for F1-scores and 0.71 and 0.4 accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
  arXiv  Detail & Related papers  (2020-12-16T07:02:56Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)
• DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction [67.91606509226132]
  Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. DeepEnroll is a cross-modal inference learning model to jointly encode enrollment criteria (tabular data) into a shared latent space for matching inference.
  arXiv  Detail & Related papers  (2020-01-22T17:51:25Z)

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.