1 point by aihealthcare 6 months ago flag hide 9 comments
aiexpert 6 months ago next
Exciting to see AI being used to improve healthcare! I'm curious, what specific projects and applications will the Machine Learning Engineers be working on?
startupceo 6 months ago next
Our ML Engineers will work on a variety of data sources, including electronic health records, patient-generated data, and external research data. We're looking for engineers with experience in NLP, image recognition, and predictive analytics to help achieve our goals.
databoss 6 months ago next
Impressive scope! I'd be excited to see how you use NLP to analyze data from electronic health records. Have you considered looking into pre-trained models like BioBERT to do NLP tasks?
startupceo 6 months ago next
We're always evaluating the latest research and technologies to make sure we're using the best tools for each project. Pre-trained models like BioBERT are definitely on our radar, and we appreciate the input!
mlguy 6 months ago prev next
@StartupCEO What's the scale of your data? Are we talking about 10s or 100s of millions of patients? I'm curious how you're handling data preprocessing and feature engineering as the scale goes up.
techguru 6 months ago prev next
As a Machine Learning Engineer, one of the most interesting projects I've worked on involves developing AI models to predict the likelihood of patient readmission. It's great to see this being implemented in a healthcare startup.
medstudent 6 months ago prev next
Predicting patient readmission sounds incredibly useful! How do these models utilize various data sources to generate the predictions?
airesearcher 6 months ago prev next
It's great to see such a diverse set of applications in healthcare using AI. Have any of your ML Engineers published related research on the impact of AI in healthcare, or plans to do so in the future?
startupceo 6 months ago prev next
Currently, we operate in two major cities and serve tens of thousands of patients each year. We handle data preprocessing and feature engineering in a distributed manner using technologies such as Dask and Spark, and we're also exploring ways to streamline these processes using cloud-based solutions.