Automated lead toxicity prediction using computational modelling framework.
Priyanka ChaurasiaSally I McCleanAbbas Ali MahdiPratheepan YogarajahJamal Akhtar AnsariShipra KunwarMohammad Kaleem AhmadPublished in: Health information science and systems (2023)
The built prediction model can be beneficial in improving the point of care and hence reducing the cost and the risk involved. It is envisaged that in future, the proposed methodology will become a part of a screening process to assist healthcare experts at the point of evaluating the lead toxicity level in pregnant women. Women screened positive could be given a range of facilities including preliminary counselling to being referred to the health centre for further diagnosis. Steps could be taken to reduce maternal lead exposure; hence, it could also be possible to mitigate the infant's lead exposure by reducing transfer from the pregnant woman.
Keyphrases
- pregnant women
- healthcare
- pregnancy outcomes
- oxidative stress
- public health
- machine learning
- mental health
- high throughput
- polycystic ovary syndrome
- type diabetes
- health information
- risk assessment
- deep learning
- social media
- insulin resistance
- birth weight
- body mass index
- weight loss
- human health
- single cell
- climate change
- weight gain