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ML for healthcare: Machine learning assists in making fertility treatment decisions

In a collaboration with University of Southampton’s IT Innovation Centre and the University Hospitals Foundation Trust, computer scientists from the institution are finding a way for machine learning to reduce face-to-face contact during the global pandemic. Focusing on fertility treatment, a new decision-making tool is in the works to minimize clinical appointments without sacrificing quality of care.

The team analyzed past treatment records to design new algorithms meant to change patient data collection practices. Expected output from the model includes predictions that inform patients of the chances of success from their fertility treatment cycle.

“Recent developments in machine learning and data science methods mean it has become much easier to interrogate large databases of healthcare data to draw out clinical insights which could be of benefit to patients,” Dr Francis Chmiel, an Enterprise Fellow in Electronics and Computer Science, shares.

“In this study we have developed predictions that allow patients to be better informed about their chances of success throughout their fertility treatment cycle. By providing these predictions, under certain conditions, patients could choose a route that best suits their personal circumstances. This information can also provide more context for the clinical care team to manage patient expectations and support their wellbeing throughout their treatment cycle.”

Additionally, the team seeks to provide better options for patients who want to delay treatment in fear of contracting the infection.

“Our analysis of retrospective cycles has identified days of treatment cycles where the measurements were least predictive and therefore of least use to the clinical care team. This understanding can identify which measurements could be dropped if the clinical care team is required to have less contact with the patient,” continues Chmiel.

“In fact, beyond COVID-19 our results suggest that some measurements may be largely superfluous and do not add significant value to the clinical process and patient care. Clinical trials will have to be performed but if our results translate to clinical practice then measurements could be reduced, making fertility treatment more cost effective and less demanding for the patient.”

About the author


Jelly is a data fanatic! She is a Law graduate, and currently works and focuses her interests at the juncture of digital, marketing and analytics.

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