Holmusk is a fast-growing startup that is revolutionizing healthcare by integrating Artificial Intelligence (AI) as well as medical domain knowledge into a cohesive platform which will be able to provide information to healthcare practitioners in near-real-time, informed by real-world data and proprietary AI-augmented models developed in-house.
In many projects involving real-world healthcare data, one of the key metrics that are of interest to healthcare practitioners is the time to adverse clinical events, such as mortality, following an admission to the emergency department. When doing such analysis, it is common to encounter data for which different patients had different length of follow-up recorded. Special analysis techniques are needed to prevent statistical bias, which belong to a group of statistical approaches called survival analysis.
Project Title
Developing a Python-based survival analysis tool for real-world data (RWD)
Scope of Work
Survival analysis was originally developed and used by medical researchers and data analysts to measure the lifetimes of a certain population. Unlike most statistical techniques, survival analysis techniques are designed to handle situations where a subject can enter or exit the study at any time.
Despite advancements in such techniques over the last few decades, their implementation in various statistical software remains limited. With the significant increase in volume and complexity of real-world data (RWD) over the last few years, this causes a significant gap in expectations between analysts and decision makers, as most of the existing tools are not designed for large-scale and complex RWD.
The scope of this internship will involve the conceptualization and development of Python-based statistical tool for analysis of survival data. This will include conducting literature reviews on the recent advancements in survival analysis, understanding the limitations of existing tools and formulating the potential use cases of different techniques.
At Holmusk we take pride in our diverse workforce and inclusive culture. We believe it takes all kinds of people to build the best products and bring real change to the healthcare space.
Deliverables for Candidate
Candidates will work with our in-house team to conceptualise and develop Python-based statistical tool for analysis of survival data which can be implemented on large-scale real-world data (RWD).
Requirements
- Currently enrolled in a Singapore university, or Singaporean/PR studying abroad
- Major in Mathematics, Statistics, Physics or any other related discipline
- Coding experience in R and/or Python
- Ability to work as a team. Familiarity with Git would be beneficial
- Highly motivated and fast learner
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Statistics Intern - Profession:
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Queenstown