03 Nov 2021

Career Stories

Jenny Zou: It's all about the data

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How does Jenny Zou, data scientist with IHiS’ Data Analytics and Artificial Intelligence (DNA) team, relieve stress?

For someone whose complex job scope involves data processing, data analysis and modelling, many wouldn’t know that Jenny spends her downtime cleaning her home, decluttering, singing and ballet. She also admits a liking of minimalism.

When she wears her data scientist hat, Jenny and her DNA colleagues support healthcare insights that improve population health and clinical operations efficiency for Public Healthcare Institutions.

In the case of the National Dental Centre, staff previously had to call every patient prior to their appointment to confirm they would show up. Jenny’s team deployed an AI-powered No-Show Predictive Model to flag such patients automatically, so that only half the number of staff were required to do such patient follow-ups.

“I’m motivated by solving the real-life challenges faced by the healthcare industry using innovative technology.”

Now Loading … HealthTech Passion

Jenny discovered her passion for HealthTech after hearing about IHiS from a friend, realising it could provide a platform to realise her skill set as a data scientist. She had graduated with a Mathematics degree from Nanyang Technological University.

On why others should consider joining IHiS, Jenny says IHiS provides a platform where technology can be used in a safe and transparent manner to help build solutions for the healthcare industry.

Since joining IHiS, Jenny says her confidence in HealthTech has grown as she gains more knowledge about Singapore’s healthcare industry and constantly learns how to apply her data science skills in a meaningful way.

“My work has great impact to the healthcare industry and provides solutions to a lot of challenges by understanding, creating and applying innovative technology.”

The CAPE Challenge

One of the projects Jenny works on is the Community Acquired Pneumonia and COVID-19 Artificial Intelligence Predictive Engine (CAPE). This AI tool determines the likelihood of whether a patient has mild or severe pneumonia, based on a chest X-ray image. Patients who are likely to become critically ill will be flagged, which enables prioritisation of treatment resources.

The collaborative nature of the CAPE project, involving different teams within IHiS and Changi General Hospital (CGH), presented many challenges which had to be overcome.

Jenny recalls, “we aimed to speed up the clinical prognostic scoring tool generation to aid clinicians assess the severity and disposition of pneumonia in patients.”

Multitasking and working with different teams at CGH presented another challenge. Jenny worked with the Data Management and Informatics teams for data collection and data anonymization, with radiology teams for model validation and image interpretation, and with IT teams for application installation and deployment.

“I had to quickly learn different domain knowledge in order to collaborate with different teams seamlessly.”

Within IHiS, Jenny’s work on CAPE involves a mix of data pre-processing, model building and UI generation. She explains that as they were building predictive models from three use cases, one member of the data science team was responsible for training each model separately, in parallel. They then packaged the solutions together for the subsequent UI generation.

Jenny’s time management skills were put to the test, as she was also working with doctors at CGH on a paper about CAPE. Jenny notes that she learned to quickly switch between different modes where necessary in order to keep up with the project timeline. She met with CGH teams frequently, both onsite and online to save time.

“When I was back in the office, after closing any daily matters, I switched to “research” mode as writing papers requires a larger block of time and deep thinking. This helped me adapt to both a “fast” and “slow” environment and enabled me to carry out my responsibilities successfully.”

Jenny recalls that convincing radiologists on the reliability of CAPE as a predictive tool presented another challenge, since the AI involved was a black box for them. In other words, the inputs and operations of the AI system used in CAPE was not visible to the users.

“We developed image interpretation techniques based on explainable AI (XAI) methods such as LIME, SHAP and Grad-CAM++ to generate the heat maps which displayed the important areas identified by AI. Radiologists could then understand how the AI interprets the X-ray image and were able to evaluate whether the localisation of the AI model is consistent with radiologists’ annotations. In this way, the AI solution is more transparent and easily accessible to them.”

While Jenny recalls the first half of 2020 being a particularly hectic period for her and the team, she looks back fondly at how they worked together, noting that everyone was passionate about the work and shared a sense of accomplishment when the project was rolled out.

Data Doing Good

Jenny explains that based on the burden of diseases in Singapore, respiratory infection, which includes pneumonia, is one of the leading causes of death in Singapore. She adds that coupled with the current COVID-19 situation, there is an unmet need for automated assessment tools that can rapidly stratify disease severity to aid clinicians in decision making.

“We aim to build predictive models using artificial intelligence, to develop decision support tools that can help doctors quickly detect whether patients have mild or severe pneumonia.”

One of Jenny’s proudest moments at IHiS to date has been collaborating with her colleagues on a paper about CAPE which was published in the Association for Computing Machinery (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), a leading publication on data mining and analysis.


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