Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity

• URL: http://arxiv.org/abs/2502.17484v1
• Date: Tue, 18 Feb 2025 12:01:55 GMT
• Title: Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity
• Authors: Kexin Fan, Alexander Capstick, Ramin Nilforooshan, Payam Barnaghi,
• Abstract summary: Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD)<n>This study builds on previous work that utilised machine learning (ML) to detect UTIs in PLWD.
• Score: 43.108040967674185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD), as they can lead to severe complications if not detected and treated early. This study builds on previous work that utilised machine learning (ML) to detect UTIs in PLWD by analysing in-home activity and physiological data collected through low-cost, passive sensors. The current research focuses on improving the performance of previous models, particularly by refining the Multilayer Perceptron (MLP), to better handle variations in home environments and improve sex fairness in predictions by making use of concepts from multitask learning. This study implemented three primary model designs: feature clustering, loss-dependent clustering, and participant ID embedding which were compared against a baseline MLP model. The results demonstrated that the loss-dependent MLP achieved the most significant improvements, increasing validation precision from 48.92% to 72.60% and sensitivity from 27.44% to 70.52%, while also enhancing model fairness across sexes. These findings suggest that the refined models offer a more reliable and equitable approach to early UTI detection in PLWD, addressing participant-specific data variations and enabling clinicians to detect and screen for UTI risks more effectively, thereby facilitating earlier and more accurate treatment decisions.

Related papers

• Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms [0.0]
  Diabetic Retinopathy (DR) is a serious and common complication of diabetes. Traditional diabetes diagnosis methods often utilize convolutional neural networks (CNNs) to extract visual features from retinal images. This study investigates the application of transfer learning (TL) to enhance ML model performance in DR detection.
  arXiv  Detail & Related papers  (2025-04-04T13:30:21Z)
• Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database [0.9055332067000195]
  Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality.<n> predictive models for lung metastasis inHCC remain limited in scope and clinical applicability.<n>We develop and validate an end-to-end machine learning pipeline using data from the Surveillance, Epidemiology, and End Results (SEER) database.
  arXiv  Detail & Related papers  (2025-01-20T20:06:31Z)
• On the challenges of detecting MCI using EEG in the wild [6.505818939553856]
  Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) using Electroencephalogram (EEG) data.<n>We investigate the potential limitations and challenges in developing a robust MCI detection method using two contrasting datasets.
  arXiv  Detail & Related papers  (2025-01-15T15:20:11Z)
• Towards Equitable ASD Diagnostics: A Comparative Study of Machine and Deep Learning Models Using Behavioral and Facial Data [2.6353853440763113]
  Autism Spectrum Disorder (ASD) is often underdiagnosed in females due to gender-specific symptom differences. This study evaluates machine learning models, particularly Random Forest and convolutional neural networks, for enhancing ASD diagnosis.
  arXiv  Detail & Related papers  (2024-11-08T05:26:04Z)
• Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques [0.0]
  Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk. Timely detection and prognostication of VAP in TBI patients are crucial to improve patient outcomes and alleviate the strain on healthcare resources. We implemented six machine learning models using the MIMIC-III database.
  arXiv  Detail & Related papers  (2024-08-02T09:44:18Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• An attention model to analyse the risk of agitation and urinary tract infections in people with dementia [0.3392372796177108]
  Behavioural symptoms and urinary tract infections (UTI) are among the most common problems faced by people with dementia. Using in-home sensing technologies and machine learning models for sensor data integration and analysis provides opportunities to detect and predict clinically significant events and changes in health status. We have developed an integrated platform to collect in-home sensor data and performed an observational study to apply machine learning models for agitation and UTI risk analysis.
  arXiv  Detail & Related papers  (2021-01-18T11:15:15Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

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.