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Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks
2021
npj Digital Medicine
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Natural language processing together with machine learning allows automated structuring of diagnoses using ICD-10 codes, but the limited performance of machine learning models, the necessity of gigantic ...
We aimed to create a high performing pipeline for automated classification of reliable ICD-10 codes in the free medical text in cardiology. ...
Natural language processing (NLP) together with machine learning allows automating ICD-10 coding for discharge letters 2 . ...
doi:10.1038/s41746-021-00404-9
pmid:33637859
fatcat:wm7w7omchffevng2ynnx6u2re4
Identifying Acute Low Back Pain Episodes in Primary Care Practice from Clinical Notes (Preprint)
2019
JMIR Medical Informatics
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ICD-10 code. ...
We used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as acute LBP and 2973 were generally associated with LBP via the recorded ...
, for their funding (grant #T42 OH 008422). ...
doi:10.2196/16878
pmid:32130159
pmcid:PMC7068466
fatcat:wcfv5snhsnaxbanziqskjltra4
Classifying Unstructured Clinical Notes via Automatic Weak Supervision
[article]
2022
arXiv
pre-print
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Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. ...
Prior work demonstrated potential utility of Machine Learning (ML) methodology in automating this process, but it has relied on large quantities of manually labeled data to train the models. ...
Acknowledgments This work was partially supported by a fellowship from Carnegie Mellon University's Center for Machine Learning and Health to M.G. ...
arXiv:2206.12088v2
fatcat:tu4xqyaalffw7mzdvv7ulwolke
Configuring a federated network of real-world patient health data for multimodal deep learning prediction of health outcomes
[article]
2021
bioRxiv
pre-print
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In this work, we present a modular, semi-automated end-to-end machine and deep learning pipeline designed to interface with a federated network of structured patient data. ...
As such, pipelines that are able to algorithmically extract huge quantities of patient data from multiple modalities present opportunities to leverage machine learning and deep learning approaches with ...
Jennifer Emond (Department of Epidemiology, Dartmouth College Geisel School of Medicine) and the Clinical Sciences team at TriNetX, Inc., particularly Seth Kuranz, Jennifer Stacy, Rutendo Kashwamba, Josh ...
doi:10.1101/2021.10.30.466612
fatcat:32yvougtlzh47ar3iav6ld6mp4
Identifying Acute Low Back Pain Episodes in Primary Care Practice from Clinical Notes
[article]
2019
medRxiv
pre-print
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ICD-10 code. ...
We used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as "acute LBP" and 2,973 were generally associated with LBP via the recorded ...
Recent applications of deep learning to clinical NLP have classified clinical notes according to diagnosis or disease codes [39] [40] [41] , predicted disease onset [32, 42] , and extracted primary cancer ...
doi:10.1101/19010462
fatcat:synog3u3rjbm3m7ilu3rmgxlku
A human-interpretable machine learning approach to predict mortality in severe mental illness
[article]
2021
medRxiv
pre-print
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This approach combines clinical knowledge, health data, and statistical learning, to make predictions interpretable to clinicians using class-contrastive reasoning. ...
Machine learning (ML), one aspect of artificial intelligence (AI), involves computer algorithms that train themselves. They have been widely applied in the healthcare domain. ...
Acknowledgements We thank Jenny Nelder and Jonathan Lewis for all their support during this project. This work is dedicated to the memory of Patrick Winston.
Funding statement ...
doi:10.1101/2021.04.05.21254684
fatcat:vkxf73t64vesvev4ofr53leezy
Automated Reconciliation of Radiology Reports and Discharge Summaries
2015
AMIA Annual Symposium Proceedings
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We study machine learning techniques to automatically identify limb abnormalities (including fractures, dislocations and foreign bodies) from radiology reports. ...
Using radiology reports from three different hospitals, we show that extracting detailed features from the reports to train Support Vector Machines can effectively automate the identification of limb fractures ...
(The judgements were primary provided by the clinical author, KC.) ...
pmid:26958213
pmcid:PMC4765582
fatcat:cdnhasm7evcctlydqgpsieoqhq
A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy
2021
Nature Communications
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It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. ...
Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. ...
of Health (R01 HL140731, R01 HL120728, R01 HL107577, and R01 HL149423); the American Heart Association (#16SFRN28780016, #15CVGPSD27260148, One Brave Idea, Apple Heart and Movement Study); the Agency for ...
doi:10.1038/s41467-021-22876-9
pmid:33976166
pmcid:PMC8113237
fatcat:nebdhmuzfzakdbmc5savb7iv3m
BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining
[article]
2020
arXiv
pre-print
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In this paper, we propose a machine learning model, BERT-XML, for large scale automated ICD coding from EHR notes, utilizing recently developed unsupervised pretraining that have achieved state of the ...
We adapt the BERT architecture for ICD coding with multi-label attention. ...
Figure 4 : The attention weights of each head for each head in the last layer of the BERT encoder. Brighter color denotes higher attention score. ...
arXiv:2006.03685v1
fatcat:c3plcv7lvzcrbdv2awfry7isei
Automated detection of altered mental status in emergency department clinical notes: a deep learning approach
2019
BMC Medical Informatics and Decision Making
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Machine learning has been used extensively in clinical text classification tasks. Deep learning approaches using word embeddings have been recently gaining momentum in biomedical applications. ...
machine learning classifiers and novel deep learning approaches. ...
Jean Craig in the Biomedical Informatics Center at the Medical University of South Carolina for sharing her expertise and extracting clinical notes and other data from the electronic health records and ...
doi:10.1186/s12911-019-0894-9
pmid:31426779
pmcid:PMC6701023
fatcat:iz7i2a2gljan3kstp5h2olskdm
Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records
2019
BMC Medical Informatics and Decision Making
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This study seeks to identify cases of undiagnosed dementia by developing and validating a weakly supervised machine-learning approach that incorporates the analysis of both structured and unstructured ...
This underdiagnosis decreases quality of life, reduces opportunities for interventions, and increases health-care costs. ...
Data source For both cases and controls, we obtained structured data (i.e., diagnosis [ICD codes], procedures [CPT codes], medications, and clinical document types) and unstructured data (i.e., clinical ...
doi:10.1186/s12911-019-0846-4
pmid:31288818
pmcid:PMC6617952
fatcat:frx7rbdsvbgsrpqzlgqfh54w5y
JLAN: medical code prediction via joint learning attention networks and denoising mechanism
2021
BMC Bioinformatics
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Results In this paper, a new joint learning model is proposed to extend our attention model for predicting medical codes from clinical notes. ...
Therefore, machine learning has been utilized to perform automatic diagnoses. ...
Acknowledgements All authors would like to thank the reviewers for the valuable comments.
Authors' contributions Both YZ and XL designed the method and experiments. ...
doi:10.1186/s12859-021-04520-x
pmid:34903164
pmcid:PMC8667397
fatcat:77ivej6crnd6letjdc26ucsz7y
A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries
[article]
2021
arXiv
pre-print
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This systematic literature review provides a comprehensive overview of automated clinical coding systems that utilises appropriate NLP, ML and DL methods and techniques to assign ICD codes to discharge ...
Lastly, future research directions are provided to scholars who are interested in automated ICD code assignment. ...
Transfer learning approach for automated ICD coding: In many machine learning methods, the training and testing data are drawn from the same feature space with the same distribution. ...
arXiv:2107.10652v1
fatcat:5tyrtj5y4zgslbfwcm3hyztr2q
Automatic Classification of Cancer Pathology Reports: A Systematic Review
2022
Journal of Pathology Informatics
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Multiple natural language processing (NLP) techniques have been proposed to automate the coding of pathology reports via text classification. ...
We benchmarked the systems based on methodology, complexity of the prediction task and core types of NLP models: i) Rule-based and Intelligent systems, ii) statistical machine learning, and iii) deep learning ...
for classifying ICD codes from topography and et al. 32 Machine database morphology classes. ...
doi:10.1016/j.jpi.2022.100003
pmid:35242443
pmcid:PMC8860734
fatcat:c5hve3ottnb6hbcalimya6u3ae
Neural Machine Translation-Based Automated CPT Classification System Using Procedure Text: Development and Validation Study (Preprint)
2020
JMIR Formative Research
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Although this system aims to enhance medical billing coding accuracy to reduce administrative costs, we compare its performance with that of previously developed machine learning algorithms. ...
With advanced deep learning techniques, developing advanced models to predict hospital and professional billing codes has become feasible. ...
for contributing hospitals in the state of Michigan. ...
doi:10.2196/22461
pmid:34037526
pmcid:PMC8190648
fatcat:6z63gt2kvvezpdlsaq3ntssjju
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