If you have seen my other projects, please feel free to mention your interests here!

Our team is conducting a systematic review focused on Atrial Fibrillation (a series of 5 studies) and we're currently seeking individuals with significant experience in machine learning to contribute to the final stages of our project.

The current stages of the project:

Reviewing/proofreading the search strategy (We have developed several to ensure coverage).

We use net adverse clinical events (NACE) to quantify/categorize the Afib prognosis models.

We are focusing on clinical outcomes (ischemic and hemorrhagic adverse events, we aimed to include: recurrent myocardial infarction, revascularization, ischemic stroke, hemorrhagic stroke, and gastrointestinal bleeding) instead of diagnosis of Afib.

Currently we have N = 2500 for screenings on Covidence.Endnote automation was conducted to limit the number of studies requiring manual screening.

We are very fast paced, with people completing screenings in 1 or 2 days.

We are looking for people who have:

Extensive experience in machine learning, with a strong understanding of relevant algorithms and techniques (e.g., NLP, text classification, clustering, regression).

Proven ability to apply machine learning to real-world data, ideally including experience with text-based data.Strong analytical and critical thinking skills to interpret data and contribute meaningfully to the discussion.

Excellent writing and communication skills to contribute to the manuscript.

A commitment to timely completion of tasks.

Timeline: May 5th > June 5th

Compensation:

No monetary compensations are available, but in recognition of your contribution, you will be acknowledged with full authorship on the resulting publication.

Communication:

Please feel free to include your email address and/or WhatsApp contact information in your application for me to reach you quickly.