The Influences of Pre-birth Factors in Early Assessment of Child
Mortality using Machine Learning Techniques
- URL: http://arxiv.org/abs/2011.09536v1
- Date: Wed, 18 Nov 2020 20:37:55 GMT
- Title: The Influences of Pre-birth Factors in Early Assessment of Child
Mortality using Machine Learning Techniques
- Authors: Asadullah Hill Galib, Nadia Nahar, and B M Mainul Hossain
- Abstract summary: This study aims at incorporating pre-birth factors, such as birth history, maternal history, reproduction history, socioeconomic condition, etc., for classifying child mortality.
Four machine learning algorithms are evaluated for classifying child mortality.
Results show that the proposed approach achieved an AUC score of 0.947 in classifying child mortality which outperformed the clinical standards.
- Score: 0.4817429789586127
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Analysis of child mortality is crucial as it pertains to the policy and
programs of a country. The early assessment of patterns and trends in causes of
child mortality help decision-makers assess needs, prioritize interventions,
and monitor progress. Post-birth factors of the child, such as real-time
clinical data, health data of the child, etc. are frequently used in child
mortality studies. However, in the early assessment of child mortality,
pre-birth factors would be more practical and beneficial than the post-birth
factors. This study aims at incorporating pre-birth factors, such as birth
history, maternal history, reproduction history, socioeconomic condition, etc.
for classifying child mortality. To assess the relative importance of the
features, Information Gain (IG) attribute evaluator is employed. For
classifying child mortality, four machine learning algorithms are evaluated.
Results show that the proposed approach achieved an AUC score of 0.947 in
classifying child mortality which outperformed the clinical standards. In terms
of accuracy, precision, recall, and f-1 score, the results are also notable and
uniform. In developing countries like Bangladesh, the early assessment of child
mortality using pre-birth factors would be effective and feasible as it avoids
the uncertainty of the post-birth factors.
Related papers
- SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing [67.8991481023825]
Sepsis is the leading cause of in-hospital mortality in the USA.
Existing predictive models are usually trained on high-quality data with few missing information.
For the potential high-risk patients with low confidence due to limited observations, we propose a robust active sensing algorithm.
arXiv Detail & Related papers (2024-07-24T04:47:36Z) - I-SIRch: AI-Powered Concept Annotation Tool For Equitable Extraction And Analysis Of Safety Insights From Maternity Investigations [0.8609957371651683]
Most current tools for analysing healthcare data focus only on biomedical concepts, overlooking the importance of human factors.
We developed I-SIRch, using artificial intelligence to automatically identify and label human factors concepts.
I-SIRch was trained using real data and tested on both real and simulated data to evaluate its performance in identifying human factors concepts.
arXiv Detail & Related papers (2024-06-08T16:05:31Z) - ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models [53.00812898384698]
We argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking.
We highlight how cognitive biases can conflate fluent information and truthfulness, and how cognitive uncertainty affects the reliability of rating scores such as Likert.
We propose the ConSiDERS-The-Human evaluation framework consisting of 6 pillars -- Consistency, Scoring Criteria, Differentiating, User Experience, Responsible, and Scalability.
arXiv Detail & Related papers (2024-05-28T22:45:28Z) - Towards an educational tool for supporting neonatologists in the
delivery room [0.26999000177990923]
We propose a machine learning approach for identifying risk factors and their impact on the birth event from real data.
Our final goal will be the one of designing a user-friendly mobile application, able to improve the recognition rate and the planning of the appropriate interventions on high-risk patients.
arXiv Detail & Related papers (2024-03-11T16:03:21Z) - Unveiling the Unborn: Advancing Fetal Health Classification through Machine Learning [0.0]
This research paper presents a novel machine-learning approach for fetal health classification.
The proposed model achieves an impressive accuracy of 98.31% on a test set.
By incorporating multiple data points, our model offers a more holistic and reliable evaluation.
arXiv Detail & Related papers (2023-09-30T22:02:51Z) - Predicting Adverse Neonatal Outcomes for Preterm Neonates with
Multi-Task Learning [51.487856868285995]
We first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem.
In particular, the MTL framework contains shared hidden layers and multiple task-specific branches.
arXiv Detail & Related papers (2023-03-28T00:44:06Z) - Early prediction of the risk of ICU mortality with Deep Federated
Learning [0.0]
We evaluate the ability of deep Federated Learning to predict the risk of Intensive Care Unit mortality at an early stage.
We show that the prediction performance is higher when the patient history window is closer to discharge or death.
arXiv Detail & Related papers (2022-12-01T15:01:27Z) - LAE : Long-tailed Age Estimation [52.5745217752147]
We first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on.
Compared with the standard baseline, the proposed one significantly decreases the estimation errors.
We propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification.
arXiv Detail & Related papers (2021-10-25T09:05:44Z) - Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration [55.88616573143478]
Outcome prediction from clinical text can prevent doctors from overlooking possible risks.
Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets.
We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
arXiv Detail & Related papers (2021-02-08T10:26:44Z) - Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials.
We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
arXiv Detail & Related papers (2020-06-14T01:15:00Z) - Quantifying Community Characteristics of Maternal Mortality Using Social
Media [12.265295793821931]
We examine the role that social media language can play in providing insights into community characteristics.
We find that rates of mentioning pregnancy-related topics on Twitter predicts maternal mortality rates with higher accuracy than standard socioeconomic and risk variables.
We then investigate psychological dimensions of community language, finding the use of less trustful, more stressed, and more negative affective language is significantly associated with higher mortality rates.
arXiv Detail & Related papers (2020-04-14T04:57:51Z)
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.