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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="Falis, M"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.13512">arXiv:2401.13512</a> <span> [<a href="https://arxiv.org/pdf/2401.13512">pdf</a>, <a href="https://arxiv.org/format/2401.13512">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1093/jamia/ocae132">10.1093/jamia/ocae132 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Can GPT-3.5 Generate and Code Discharge Summaries? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Falis%2C+M">Mat煤拧 Falis</a>, <a href="/search/cs?searchtype=author&query=Gema%2C+A+P">Aryo Pradipta Gema</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+H">Hang Dong</a>, <a href="/search/cs?searchtype=author&query=Daines%2C+L">Luke Daines</a>, <a href="/search/cs?searchtype=author&query=Basetti%2C+S">Siddharth Basetti</a>, <a href="/search/cs?searchtype=author&query=Holder%2C+M">Michael Holder</a>, <a href="/search/cs?searchtype=author&query=Penfold%2C+R+S">Rose S Penfold</a>, <a href="/search/cs?searchtype=author&query=Birch%2C+A">Alexandra Birch</a>, <a href="/search/cs?searchtype=author&query=Alex%2C+B">Beatrice Alex</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.13512v2-abstract-short" style="display: inline;"> Objective: To investigate GPT-3.5 in generating and coding medical documents with ICD-10 codes for data augmentation on low-resources labels. Materials and Methods: Employing GPT-3.5 we generated and coded 9,606 discharge summaries based on lists of ICD-10 code descriptions of patients with infrequent (generation) codes within the MIMIC-IV dataset. Combined with the baseline training set, this f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.13512v2-abstract-full').style.display = 'inline'; document.getElementById('2401.13512v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.13512v2-abstract-full" style="display: none;"> Objective: To investigate GPT-3.5 in generating and coding medical documents with ICD-10 codes for data augmentation on low-resources labels. Materials and Methods: Employing GPT-3.5 we generated and coded 9,606 discharge summaries based on lists of ICD-10 code descriptions of patients with infrequent (generation) codes within the MIMIC-IV dataset. Combined with the baseline training set, this formed an augmented training set. Neural coding models were trained on baseline and augmented data and evaluated on a MIMIC-IV test set. We report micro- and macro-F1 scores on the full codeset, generation codes, and their families. Weak Hierarchical Confusion Matrices were employed to determine within-family and outside-of-family coding errors in the latter codesets. The coding performance of GPT-3.5 was evaluated both on prompt-guided self-generated data and real MIMIC-IV data. Clinical professionals evaluated the clinical acceptability of the generated documents. Results: Augmentation slightly hinders the overall performance of the models but improves performance for the generation candidate codes and their families, including one unseen in the baseline training data. Augmented models display lower out-of-family error rates. GPT-3.5 can identify ICD-10 codes by the prompted descriptions, but performs poorly on real data. Evaluators note the correctness of generated concepts while suffering in variety, supporting information, and narrative. Discussion and Conclusion: GPT-3.5 alone is unsuitable for ICD-10 coding. Augmentation positively affects generation code families but mainly benefits codes with existing examples. Augmentation reduces out-of-family errors. Discharge summaries generated by GPT-3.5 state prompted concepts correctly but lack variety, and authenticity in narratives. They are unsuitable for clinical practice. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.13512v2-abstract-full').style.display = 'none'; document.getElementById('2401.13512v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages; 250 words in abstract; 4,152 words in main body; 4 figures (1 black and white, 3 colour); 4 tables; 34 references; Accepted and published by the Journal of the American Medical Informatics Association</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Journal of the American Medical Informatics Association, 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2203.11092">arXiv:2203.11092</a> <span> [<a href="https://arxiv.org/pdf/2203.11092">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Automated Clinical Coding: What, Why, and Where We Are? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Dong%2C+H">Hang Dong</a>, <a href="/search/cs?searchtype=author&query=Falis%2C+M">Mat煤拧 Falis</a>, <a href="/search/cs?searchtype=author&query=Whiteley%2C+W">William Whiteley</a>, <a href="/search/cs?searchtype=author&query=Alex%2C+B">Beatrice Alex</a>, <a href="/search/cs?searchtype=author&query=Matterson%2C+J">Joshua Matterson</a>, <a href="/search/cs?searchtype=author&query=Ji%2C+S">Shaoxiong Ji</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Jiaoyan Chen</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+H">Honghan Wu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2203.11092v3-abstract-short" style="display: inline;"> 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. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.11092v3-abstract-full').style.display = 'inline'; document.getElementById('2203.11092v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2203.11092v3-abstract-full" style="display: none;"> 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. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019 - early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. 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. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2203.11092v3-abstract-full').style.display = 'none'; document.getElementById('2203.11092v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 March, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted for npj Digital Medicine</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07 (Primary); 68T50 (Secondary) <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7; J.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.04853">arXiv:2109.04853</a> <span> [<a href="https://arxiv.org/pdf/2109.04853">pdf</a>, <a href="https://arxiv.org/format/2109.04853">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Falis%2C+M">Mat煤拧 Falis</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+H">Hang Dong</a>, <a href="/search/cs?searchtype=author&query=Birch%2C+A">Alexandra Birch</a>, <a href="/search/cs?searchtype=author&query=Alex%2C+B">Beatrice Alex</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.04853v1-abstract-short" style="display: inline;"> Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of models in prior art is evaluated with standard precision, recall, and F1 measures without regard for the rich hierarchical structure. In this work we argue for hierarchical evaluation of the predictions of neural LMTC m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.04853v1-abstract-full').style.display = 'inline'; document.getElementById('2109.04853v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.04853v1-abstract-full" style="display: none;"> Large-Scale Multi-Label Text Classification (LMTC) includes tasks with hierarchical label spaces, such as automatic assignment of ICD-9 codes to discharge summaries. Performance of models in prior art is evaluated with standard precision, recall, and F1 measures without regard for the rich hierarchical structure. In this work we argue for hierarchical evaluation of the predictions of neural LMTC models. With the example of the ICD-9 ontology we describe a structural issue in the representation of the structured label space in prior art, and propose an alternative representation based on the depth of the ontology. We propose a set of metrics for hierarchical evaluation using the depth-based representation. We compare the evaluation scores from the proposed metrics with previously used metrics on prior art LMTC models for ICD-9 coding in MIMIC-III. We also propose further avenues of research involving the proposed ontological representation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.04853v1-abstract-full').style.display = 'none'; document.getElementById('2109.04853v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 2 figures, EMNLP 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.16152">arXiv:2007.16152</a> <span> [<a href="https://arxiv.org/pdf/2007.16152">pdf</a>, <a href="https://arxiv.org/format/2007.16152">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Paying Per-label Attention for Multi-label Extraction from Radiology Reports </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Schrempf%2C+P">Patrick Schrempf</a>, <a href="/search/cs?searchtype=author&query=Watson%2C+H">Hannah Watson</a>, <a href="/search/cs?searchtype=author&query=Mikhael%2C+S">Shadia Mikhael</a>, <a href="/search/cs?searchtype=author&query=Pajak%2C+M">Maciej Pajak</a>, <a href="/search/cs?searchtype=author&query=Falis%2C+M">Mat煤拧 Falis</a>, <a href="/search/cs?searchtype=author&query=Lisowska%2C+A">Aneta Lisowska</a>, <a href="/search/cs?searchtype=author&query=Muir%2C+K+W">Keith W. Muir</a>, <a href="/search/cs?searchtype=author&query=Harris-Birtill%2C+D">David Harris-Birtill</a>, <a href="/search/cs?searchtype=author&query=O%27Neil%2C+A+Q">Alison Q. O'Neil</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.16152v3-abstract-short" style="display: inline;"> Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a rich source of information. In this paper, we tackle the automated extraction of structured labels from head CT reports for imaging of suspected stroke patients, using deep learning. Firstly… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.16152v3-abstract-full').style.display = 'inline'; document.getElementById('2007.16152v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.16152v3-abstract-full" style="display: none;"> Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a rich source of information. In this paper, we tackle the automated extraction of structured labels from head CT reports for imaging of suspected stroke patients, using deep learning. Firstly, we propose a set of 31 labels which correspond to radiographic findings (e.g. hyperdensity) and clinical impressions (e.g. haemorrhage) related to neurological abnormalities. Secondly, inspired by previous work, we extend existing state-of-the-art neural network models with a label-dependent attention mechanism. Using this mechanism and simple synthetic data augmentation, we are able to robustly extract many labels with a single model, classified according to the radiologist's reporting (positive, uncertain, negative). This approach can be used in further research to effectively extract many labels from medical text. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.16152v3-abstract-full').style.display = 'none'; document.getElementById('2007.16152v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to MICCAI 2020 LABELS workshop</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1910.04519">arXiv:1910.04519</a> <span> [<a href="https://arxiv.org/pdf/1910.04519">pdf</a>, <a href="https://arxiv.org/format/1910.04519">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Language Transfer for Early Warning of Epidemics from Social Media </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Appelgren%2C+M">Mattias Appelgren</a>, <a href="/search/cs?searchtype=author&query=Schrempf%2C+P">Patrick Schrempf</a>, <a href="/search/cs?searchtype=author&query=Falis%2C+M">Mat煤拧 Falis</a>, <a href="/search/cs?searchtype=author&query=Ikeda%2C+S">Satoshi Ikeda</a>, <a href="/search/cs?searchtype=author&query=O%27Neil%2C+A+Q">Alison Q O'Neil</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1910.04519v1-abstract-short" style="display: inline;"> Statements on social media can be analysed to identify individuals who are experiencing red flag medical symptoms, allowing early detection of the spread of disease such as influenza. Since disease does not respect cultural borders and may spread between populations speaking different languages, we would like to build multilingual models. However, the data required to train models for every langua… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.04519v1-abstract-full').style.display = 'inline'; document.getElementById('1910.04519v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1910.04519v1-abstract-full" style="display: none;"> Statements on social media can be analysed to identify individuals who are experiencing red flag medical symptoms, allowing early detection of the spread of disease such as influenza. Since disease does not respect cultural borders and may spread between populations speaking different languages, we would like to build multilingual models. However, the data required to train models for every language may be difficult, expensive and time-consuming to obtain, particularly for low-resource languages. Taking Japanese as our target language, we explore methods by which data in one language might be used to build models for a different language. We evaluate strategies of training on machine translated data and of zero-shot transfer through the use of multilingual models. We find that the choice of source language impacts the performance, with Chinese-Japanese being a better language pair than English-Japanese. Training on machine translated data shows promise, especially when used in conjunction with a small amount of target language data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1910.04519v1-abstract-full').style.display = 'none'; document.getElementById('1910.04519v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop (AI+HADR) at NeurIPS 2019</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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