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A Unified Review of Deep Learning for Automated Medical Coding [article]

Shaoxiong Ji and Wei Sun and Hang Dong and Honghan Wu and Pekka Marttinen
2022 arXiv   pre-print
However, it lacks a unified view of the design of neural network architectures for medical coding.  ...  This review proposes a unified framework to provide a general understanding of the building blocks of medical coding models and summarizes recent advanced models under the proposed framework.  ...  Acknowledgments This work was supported by the Academy of Finland (grant 336033) and EU H2020 (grant 101016775). H.  ... 
arXiv:2201.02797v1 fatcat:ajl6uq6mkzdo3j2trmfy5ceypq

Automated Clinical Coding: What, Why, and Where We Are? [article]

Hang Dong, Matúš Falis, William Whiteley, Beatrice Alex, Joshua Matterson, Shaoxiong Ji, Jiaoyan Chen, Honghan Wu
2022 arXiv   pre-print
Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding.  ...  Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis.  ...  There has been a surge of articles for automated clinical coding with deep learning (as the current mainstream approach of AI) in the last few years, as reviewed in recent surveys [6] [7] [8] .  ... 
arXiv:2203.11092v2 fatcat:bx6n7hzulnhwbdhlxx6of55hde

NLP Algorithms Endowed for Automatic Extraction of Information from Unstructured Free-Text Reports of Radiology Monarchy

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
A rule-based NLP system is used in most of the automated IE applications in medical domain; whereas some applications are using Random Forest classifier, PageRank Algorithm, clustering algorithm, Conditional  ...  Random Fields (CRF) algorithm, and deep learning-based approaches.  ...  In the paper," Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text", [17] author had presented the deep neural network model for assigning ICD-10 clinical codes  ... 
doi:10.35940/ijitee.l8009.1091220 fatcat:sjth33dnvjfnhn442figt75llq

Automated detection of altered mental status in emergency department clinical notes: a deep learning approach

Jihad S. Obeid, Erin R. Weeda, Andrew J. Matuskowitz, Kevin Gagnon, Tami Crawford, Christine M. Carr, Lewis J. Frey
2019 BMC Medical Informatics and Decision Making  
In an effort to automate the identification of altered mental status (AMS) in emergency department provider notes for the purpose of decision support, we compare the performance of classic bag-of-words-based  ...  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

Standard NER Tagging Scheme for Big Data Healthcare Analytics Built on Unified Medical Corpora

Sarah Shafqat, Hammad Majeed, Qaisar Javaid, Hafiz Farooq Ahmad
2022 Journal of Artificial Intelligence and Technology  
Authors of this paper while searching for possible solutions for medical context learning found that unified corpora tagged with medical nomenclature was missing to train the analytics for medical context  ...  The motivation for this research comes from the gap found in discovering the common ground for medical context learning through analytics for different purposes of diagnosing, recommending, prescribing  ...  Endocrine patients' data contributed for diagnosis of Diabetes and its comorbidities holds a lot of worth to come up with these observations from experimental study.  ... 
doi:10.37965/jait.2022.0127 fatcat:5orjqct5anhjdg7sr54rwmyqjm

Sensor, Signal, and Imaging Informatics in 2017

William Hsu, Thomas Deserno, Charles Kahn
2018 IMIA Yearbook of Medical Informatics  
Conclusion: The growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics.  ...  Each candidate article was reviewed by section editors and at least two other external reviewers.  ...  Holmes for editorial guidance and support. We thank the reviewers for participating in the selection process.  ... 
doi:10.1055/s-0038-1667084 pmid:30157513 fatcat:czexlwnds5c7douwel77cbsz2q

Sensor, Signal, and Imaging Informatics

W. Hsu, S. Park, Charles Kahn
2017 IMIA Yearbook of Medical Informatics  
Conclusion: The growing volume of signal and imaging data provides exciting new challenges and opportunities for research in medical informatics.  ...  Each candidate article was reviewed by the section editors and at least two other external reviewers. The final selection of the six best papers was conducted by the editorial board of the Yearbook.  ...  Holmes for editorial guidance and support. We thank the reviewers for participating in the selection process.  ... 
doi:10.1055/s-0037-1606491 fatcat:oopaffel2nbklprgcc72febsne

Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients

Brihat Sharma, Dmitriy Dligach, Kristin Swope, Elizabeth Salisbury-Afshar, Niranjan S. Karnik, Cara Joyce, Majid Afshar
2020 BMC Medical Informatics and Decision Making  
mapped from the Unified Medical Language System) thus eliminating PHI as inputs to a machine learning model; and (2) training character-based machine learning models that obviate the need for a dictionary  ...  Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in  ...  of data and a contribution to the writing and intellectual content of the article; and acknowledge that they have exercised due care in ensuring the integrity of the work.  ... 
doi:10.1186/s12911-020-1099-y pmid:32349766 pmcid:PMC7191715 fatcat:yr3s4dpcqfaxtjiboawhz76tsm

Preface to the Special Issue on Artificial Intelligence for Business Process Management 2019

Fabrizio Maria Maggi, Andrea Marrella
2021 Journal on Data Semantics  
The paper "Towards Automated Support of Complaint Handling Processes: An Application in the Medical Technology Industry," authored by Hake et al., examines a data set from a large manufacturer of medical  ...  In addition, the paper shows how partial process automation can be achieved in practice by designing, implementing, and evaluating a deep-learningbased prototype for automatically suggesting a likely error  ... 
doi:10.1007/s13740-021-00131-0 fatcat:d4ja7bbnfnduljkrtv3kgkqcvm

Hybrid intelligent framework for automated medical learning

Asma Belhadi, Youcef Djenouri, Vicente Garcia Diaz, Essam H. Houssein, Jerry Chun‐Wei Lin
2021 Expert systems  
The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data.  ...  Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data.  ...  We present a new framework, called Hybrid Automated Medical Learning (HAML), which adopts distributed deep learning, multi agents systems, and knowledge graph for automated medical learning.  ... 
doi:10.1111/exsy.12737 fatcat:alshb2hrejak3nhpcqgtbxyphq

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Kristiina Rannikmäe, Honghan Wu, Steven Tominey, William Whiteley, Naomi Allen, Cathie Sudlow, the UK Biobank
2021 BMC Medical Informatics and Decision Making  
Background Better phenotyping of routinely collected coded data would be useful for research and health improvement.  ...  For example, the precision of coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) may be as poor as < 50%.  ...  Acknowledgements UK Biobank participants; UK Biobank scientific, project and data management teams in Oxford and Stockport; expert adjudicators and medical students involved in adjudicating the ground-truth  ... 
doi:10.1186/s12911-021-01556-0 pmid:34130677 pmcid:PMC8204419 fatcat:rzrps6picjdttkajpfk5aktm2a

Automatic Classification of Cancer Pathology Reports: A Systematic Review

Thiago Santos, Amara Tariq, Judy Wawira Gichoya, Hari Trivedi, Imon Banerjee
2022 Journal of Pathology Informatics  
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  ...  To automate the extraction of ICD codes from cancer pathology reports, Kavuluru et al. 30 used Unified Medical Language System (UMLS) 34 entities to map the text and utilized Concept Unique Identifiers  ... 
doi:10.1016/j.jpi.2022.100003 pmid:35242443 pmcid:PMC8860734 fatcat:c5hve3ottnb6hbcalimya6u3ae

Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis

Omar Kouli, Ahmed Hassane, Dania Badran, Tasnim Kouli, Kismet Hossain-Ibrahim, J Douglas Steele
2022 Neuro-Oncology Advances  
We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI.  ...  Results Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis.  ...  Hierarchical receiving operating curves (ROC) of (A) deep learning (DL) and (B) traditional machine learning (TML) studies included in detection meta-analysis.  ... 
doi:10.1093/noajnl/vdac081 pmid:35769411 pmcid:PMC9234754 fatcat:nekrrqhilzetthi3ki3xywh4mm

Machine learning approaches for electronic health records phenotyping: A methodical review [article]

Siyue Yang, Paul Varghese, Ellen Stephenson, Karen Tu, Jessica Gronsbell
2022 medRxiv   pre-print
In terms of methodology, supervised learning is the most prevalent ML paradigm (n = 64, 60.4%), with half of the articles employing deep learning.  ...  ObjectiveAccurate and rapid methods for phenotyping are a prerequisite to realizing the potential of electronic health records (EHRs) data for clinical and translational research.  ...  In reviewing the literature, we found that deep learning and federated learning appeared in the articles.  ... 
doi:10.1101/2022.04.23.22274218 fatcat:bnbwld7tefe7vm74rql4wuub2q

Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey?

Neil Mehta, Murthy V. Devarakonda
2018 Journal of Allergy and Clinical Immunology  
The National Library of Medicine maintains a comprehensive medical metathesaurus (Unified Medical Language System [UMLS]; see the Appendix E2 in this article's Online Repository at www.jacionline.org)  ...  But is it necessary for humans to decode cognitive tasks for automation to work?  ...  of Diseases and MeSH (medical subheadings for classification of journal articles and books) and are mapped to a single terminology.  ... 
doi:10.1016/j.jaci.2018.02.025 pmid:29518424 fatcat:4y2deasihjdmdbzvm4jjdqbugi
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