Related papers: Identification, explanation and clinical evaluation of hospital patient subtypes

Identification, explanation and clinical evaluation of hospital patient subtypes

URL: http://arxiv.org/abs/2301.08019v1
Date: Thu, 19 Jan 2023 11:42:09 GMT
Title: Identification, explanation and clinical evaluation of hospital patient subtypes
Authors: Enrico Werner, Jeffrey N. Clark, Ranjeet S. Bhamber, Michael Ambler, Christopher P. Bourdeaux, Alexander Hepburn, Christopher J. McWilliams, Raul Santos-Rodriguez
Abstract summary: We present a pipeline in which unsupervised machine learning techniques are used to identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning. In parallel, clinicians assessed intra-cluster similarities and inter-cluster differences of the identified patient subtypes within the context of their clinical knowledge.
Score: 50.591267188664666
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning. In parallel, clinicians assessed intra-cluster similarities and inter-cluster differences of the identified patient subtypes within the context of their clinical knowledge. By confronting the outputs of both automatic and clinician-based explanations, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.

Related papers

Named Clinical Entity Recognition Benchmark [2.9332007863461893]
This report introduces a Named Clinical Entity Recognition Benchmark. It addresses the crucial natural language processing (NLP) task of extracting structured information from clinical narratives. The leaderboard provides a standardized platform for assessing diverse language models.
arXiv Detail & Related papers (2024-10-07T14:00:18Z)
How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation [6.547981908229007]
We introduce a novel classification framework for time-series imputation using deep learning. By identifying conceptual gaps in the literature and existing reviews, we devise a taxonomy grounded on the inductive bias of neural imputation frameworks.
arXiv Detail & Related papers (2024-07-11T12:33:28Z)
A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics [63.106382317917344]
We report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases.
arXiv Detail & Related papers (2023-06-01T16:23:47Z)
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text [56.32427751440426]
In clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines.
arXiv Detail & Related papers (2022-10-16T10:12:07Z)
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy [43.55994393060723]
gaitXplorer is a visual analytics approach for the classification of CP-related gait patterns. It integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications.
arXiv Detail & Related papers (2022-08-10T09:21:28Z)
Cross-Lingual Knowledge Transfer for Clinical Phenotyping [55.92262310716537]
We investigate cross-lingual knowledge transfer strategies to execute this task for clinics that do not use the English language. We evaluate these strategies for a Greek and a Spanish clinic leveraging clinical notes from different clinical domains. Our results show that using multilingual data overall improves clinical phenotyping models and can compensate for data sparseness.
arXiv Detail & Related papers (2022-08-03T08:33:21Z)
Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing [9.77063694539068]
Recently, some states within the United States of America require patients to have open access to their clinical notes. This research investigates methods for identifying security/privacy risks within clinical notes.
arXiv Detail & Related papers (2022-03-24T00:36:59Z)
Enriching Unsupervised User Embedding via Medical Concepts [51.17532619610099]
Unsupervised user embedding aims to encode patients into fixed-length vectors without human supervisions. Medical concepts extracted from the clinical notes contain rich connections between patients and their clinical categories. We propose a concept-aware unsupervised user embedding that jointly leverages text documents and medical concepts from two clinical corpora.
arXiv Detail & Related papers (2022-03-20T18:54:05Z)
An NLP Solution to Foster the Use of Information in Electronic Health Records for Efficiency in Decision-Making in Hospital Care [0.26340862968426904]
The project aimed to define the rules and develop a technological solution to automatically identify attributes within free-text clinical records written in Portuguese. The project's goal was achieved by a multidisciplinary team that included clinicians, epidemiologists, computational linguists, machine learning researchers and software engineers.
arXiv Detail & Related papers (2022-02-24T15:52:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.