First Experiences with the Identification of People at Risk for Diabetes in Argentina using Machine Learning Techniques

• URL: http://arxiv.org/abs/2403.18631v2
• Date: Fri, 28 Jun 2024 22:21:45 GMT
• Title: First Experiences with the Identification of People at Risk for Diabetes in Argentina using Machine Learning Techniques
• Authors: Enzo Rucci, Gonzalo Tittarelli, Franco Ronchetti, Jorge F. Elgart, Laura Lanzarini, Juan José Gagliardino,
• Abstract summary: This article describes the development and assessment of predictive models to identify people at risk for T2D and PD specifically in Argentina. The results obtained show that a very good performance was observed for two datasets with some of these models.
• Score: 0.27488316163114823
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Detecting Type 2 Diabetes (T2D) and Prediabetes (PD) is a real challenge for medicine due to the absence of pathogenic symptoms and the lack of known associated risk factors. Even though some proposals for machine learning models enable the identification of people at risk, the nature of the condition makes it so that a model suitable for one population may not necessarily be suitable for another. In this article, the development and assessment of predictive models to identify people at risk for T2D and PD specifically in Argentina are discussed. First, the database was thoroughly preprocessed and three specific datasets were generated considering a compromise between the number of records and the amount of available variables. After applying 5 different classification models, the results obtained show that a very good performance was observed for two datasets with some of these models. In particular, RF, DT, and ANN demonstrated great classification power, with good values for the metrics under consideration. Given the lack of this type of tool in Argentina, this work represents the first step towards the development of more sophisticated models.

Related papers

• How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers? [49.35105290167996]
  Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
  arXiv  Detail & Related papers  (2023-08-17T20:40:30Z)
• Diagnosis Uncertain Models For Medical Risk Prediction [80.07192791931533]
  We consider a patient risk model which has access to vital signs, lab values, and prior history but does not have access to a patient's diagnosis. We show that such all-cause' risk models have good generalization across diagnoses but have a predictable failure mode. We propose a fix for this problem by explicitly modeling the uncertainty in risk prediction coming from uncertainty in patient diagnoses.
  arXiv  Detail & Related papers  (2023-06-29T23:36:04Z)
• Artificial Intelligence-Based Methods for Precision Medicine: Diabetes Risk Prediction [0.3425341633647624]
  This scoping review analyzes existing literature on AI-based models for T2DM risk prediction. Traditional machine learning models were more prevalent than deep learning models. Both unimodal and multimodal models showed promising performance, with the latter outperforming the former.
  arXiv  Detail & Related papers  (2023-05-24T14:45:54Z)
• SACDNet: Towards Early Type 2 Diabetes Prediction with Uncertainty for Electronic Health Records [0.951828574518325]
  This study proposes a novel neural network architecture for early T2DM prediction using multi-headed self-attention and dense layers. The proposed technique is called the Self-Attention for Comorbid Disease Net (SACDNet), achieving an accuracy of 89.3% and an F1-Score of 89.1%. A T2DM prediction dataset is also built as part of this study which is based on real-world routine Electronic Health Record (EHR) data comprising 4,124 diabetic and 181,767 non-diabetic examples.
  arXiv  Detail & Related papers  (2023-01-12T07:14:47Z)
• Textual Data Augmentation for Patient Outcomes Prediction [67.72545656557858]
  We propose a novel data augmentation method to generate artificial clinical notes in patients' Electronic Health Records. We fine-tune the generative language model GPT-2 to synthesize labeled text with the original training data. We evaluate our method on the most common patient outcome, i.e., the 30-day readmission rate.
  arXiv  Detail & Related papers  (2022-11-13T01:07:23Z)
• SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification [0.5551483435671848]
  Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable timely therapeutic interventions and lifestyle modifications. We show that image-derived phenotypes and physical examination data together can accurately predict diabetes risk.
  arXiv  Detail & Related papers  (2022-09-20T23:39:52Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Enhancing the Interpretability of Deep Models in Heathcare Through Attention: Application to Glucose Forecasting for Diabetic People [4.692400531340393]
  We evaluate the RETAIN model on the type-2 IDIAB and the type-1 OhioT1DM datasets. We show that the RETAIN model offers a very good compromise between accuracy and interpretability.
  arXiv  Detail & Related papers  (2020-09-08T13:27:52Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability [76.64661091980531]
  People with diabetes are at risk of developing diabetic retinopathy (DR) Computer-aided DR diagnosis is a promising tool for early detection of DR and severity grading. This dataset has 1,842 images with pixel-level DR-related lesion annotations, and 1,000 images with image-level labels graded by six board-certified ophthalmologists.
  arXiv  Detail & Related papers  (2020-08-22T07:48:04Z)
• GLYFE: Review and Benchmark of Personalized Glucose Predictive Models in Type-1 Diabetes [4.17510581764131]
  GLYFE is a benchmark of machine-learning-based glucose-predictive models. The results of nine different models coming from the glucose-prediction literature are presented.
  arXiv  Detail & Related papers  (2020-06-29T11:34:41Z)

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.