Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis

• URL: http://arxiv.org/abs/2407.02737v1
• Date: Wed, 3 Jul 2024 01:20:26 GMT
• Title: Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis
• Abstract summary: We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis. Our solution consists of a Myrna (TM) Instrument and embedded TriVerity (TM) classifiers. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration.
• Score: 0.16159460691940983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. Our solution consists of a Myrna (TM) Instrument and embedded TriVerity (TM) classifiers. The instrument measures abundances of 29 messenger RNAs in patient's blood, subsequently used as features for machine learning. The classifiers convert the input features to an intuitive test report comprising the separate likelihoods of (1) a bacterial infection (2) a viral infection, and (3) severity (need for Intensive Care Unit-level care). In internal validation, the system achieved AUROC = 0.83 on the three-class disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on binary prognosis of disease severity. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration (FDA). This engineering manuscript teaches the standard and novel machine learning methods used to translate an academic research concept to a clinical product aimed at improving patient care, and discusses lessons learned.

Related papers

• ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding [67.57715989349471]
  Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric challenge.<n>We introduce ClinFusion, a vision-centric MLLM designed for holistic medical understanding.<n>We show that ClinFusion sets a new state-of-the-art across a comprehensive suite of 2D and 3D multimodal medical benchmarks.
  arXiv  Detail & Related papers  (2026-07-27T17:59:49Z)
• Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography [75.7789001619107]
  Vision-language pre-training holds great promise for general-purpose medical AI.<n>We propose a tailored framework featuring three key components.<n>A diagnosis-aware prompt strategy employs real clinical phrases and aggregated disease prototypes to bridge the pre-training-inference gap.
  arXiv  Detail & Related papers  (2026-06-24T08:24:45Z)
• Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset [0.5277756703318045]
  This study aims to identify clinically strong predictors while achieving state-of-the-art results across three classification tasks.<n>Eosinopenia emerged as a top predictor, highlighting its value as an underutilized marker that is not included in current assessment standards.
  arXiv  Detail & Related papers  (2026-04-06T14:07:23Z)
• WoundNet-Ensemble: A Novel IoMT System Integrating Self-Supervised Deep Learning and Multi-Model Fusion for Automated, High-Accuracy Wound Classification and Healing Progression Monitoring [0.0]
  We present WoundNet-Ensemble, an Internet of Medical Things system leveraging a novel ensemble of three complementary deep learning architectures.<n>System achieves 99.90 percent ensemble accuracy on a comprehensive dataset of 5,175 wound images.<n>We implement a longitudinal wound healing tracker that computes healing rates, severity scores, and generates clinical alerts.
  arXiv  Detail & Related papers  (2025-12-20T22:49:43Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Examining Deployment and Refinement of the VIOLA-AI Intracranial Hemorrhage Model Using an Interactive NeoMedSys Platform [0.6582858408923039]
  The current study describes a radiology software platform called NeoMedSys that can enable efficient deployment and refinements of AI models.<n>We evaluated the feasibility and effectiveness of running NeoMedSys for three months in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-05-14T13:33:38Z)
• Severity Classification of Chronic Obstructive Pulmonary Disease in Intensive Care Units: A Semi-Supervised Approach Using MIMIC-III Dataset [0.0]
  Chronic obstructive pulmonary disease (COPD) represents a significant global health burden. This study introduces an innovative machine learning framework for COPD severity classification.
  arXiv  Detail & Related papers  (2025-04-24T09:37:52Z)
• TBBC: Predict True Bacteraemia in Blood Cultures via Deep Learning [0.0]
  Bacteraemia, a bloodstream infection with high morbidity and mortality rates, poses significant diagnostic challenges. This thesis aims to identify optimal machine learning techniques for predicting bacteraemia and develop a predictive model using data from St. Antonius Hospital's emergency department.
  arXiv  Detail & Related papers  (2024-10-25T05:25:01Z)
• Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models [5.817643726988823]
  This study explores the application of artificial intelligence (AI) and machine learning (ML) to improve the speed and accuracy of abdominal trauma diagnosis. We developed an advanced AI-based model combining 3D segmentation, 2D Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) to enhance diagnostic performance. Our model processes abdominal CT scans to provide real-time, precise assessments, thereby improving clinical decision-making and patient outcomes.
  arXiv  Detail & Related papers  (2024-07-23T04:18:34Z)
• Multimodal Pretraining of Medical Time Series and Notes [45.89025874396911]
  Deep learning models show promise in extracting meaningful patterns, but they require extensive labeled data. We propose a novel approach employing self-supervised pretraining, focusing on the alignment of clinical measurements and notes. In downstream tasks, including in-hospital mortality prediction and phenotyping, our model outperforms baselines in settings where only a fraction of the data is labeled.
  arXiv  Detail & Related papers  (2023-12-11T21:53:40Z)
• Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method [0.0]
  This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of blood cancer prevalent in children and teenagers. It proposes an automated detection approach using computer-aided diagnostic (CAD) models, leveraging deep learning techniques. The proposed method achieved an impressive 98.38% accuracy, outperforming other tested models.
  arXiv  Detail & Related papers  (2023-12-01T10:37:02Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning [72.2614468437919]
  We present a machine learning framework, AutoPrognosis 2.0, to develop diagnostic and prognostic models. We provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank. Our risk score has been implemented as a web-based decision support tool and can be publicly accessed by patients and clinicians worldwide.
  arXiv  Detail & Related papers  (2022-10-21T16:31:46Z)
• Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing [62.9062883851246]
  Machine learning holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems.
  arXiv  Detail & Related papers  (2022-07-21T09:35:38Z)
• COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data [66.43957431843324]
  We introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization.
  arXiv  Detail & Related papers  (2022-04-24T07:38:37Z)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• Using machine learning techniques to predict hospital admission at the emergency department [0.0]
  One of the most important tasks in the Emergency Department (ED) is to promptly identify the patients who will benefit from hospital admission. Machine Learning (ML) techniques show promise as diagnostic aids in healthcare. We investigated the following features seeking to investigate their performance in predicting hospital admission.
  arXiv  Detail & Related papers  (2021-06-23T16:37:37Z)
• Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images [8.103053617559748]
  Recent COVID-19 pandemic has demonstrated the need of developing systems to automatize the diagnosis of pneumonia. CNNs have proved to be an excellent option for the automatic classification of medical images. We propose a multi-level ensemble classification system based on a Bayesian Deep Learning approach.
  arXiv  Detail & Related papers  (2020-11-27T14:06:25Z)
• CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification [45.86448200141968]
  We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight studies of severe patients and direct them to a hospital or provide emergency medical care. We propose a multitask approach to consolidate both triage approaches and propose a convolutional neural network to combine all available labels within a single model. We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in
  arXiv  Detail & Related papers  (2020-06-02T08:05:06Z)

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.