A Day in the life of a Clinical Scientist

A blog post written by Akhila Naz

Embarking on a journey into the realm of clinical data science as a PhD student is an exciting and intellectually stimulating experience. Tucked away in the picturesque city of Graz, I find myself immersed in the world of natural language processing (NLP) – a field that harnesses the power of language and data to revolutionize healthcare. In this blog, I will take you through a typical day in my life as a clinical data science PhD student, shedding light on the challenges, triumphs, and the overall impact of my work.


Morning: The Quest for Knowledge

The day begins with the serenity of a morning prayer and the invigorating rhythm of jogging through the picturesque streets of Graz. After this refreshing start, I settle into my workspace, accompanied by the soothing aroma of freshly brewed green tea. With a quick glance at my calendar, I embark on my first task: a thorough literature review. Staying abreast of the latest developments in NLP and clinical data science is paramount. I delve into research papers, scientific articles, and conference proceedings, meticulously extracting insights that hold the potential to enrich my own work. This phase is more than just knowledge absorption; it's a platform for me to critically evaluate methodologies and dissect results, further refining my analytical acumen.


Late Morning: Unraveling Data Patterns

As the morning progresses, I transition to the fascinating realm of data analysis. At the core of clinical data science lies the ability to unravel intricate patterns concealed within the data. Today's endeavor involves delving into a dataset brimming with electronic health records (EHRs), each document encapsulating the dynamic interactions between patients and medical practitioners. These EHRs represent a goldmine of unstructured text, laden with invaluable insights waiting to be unearthed. My current mission revolves around enhancing entity recognition and establishing robust linkages for interoperability.

In this pursuit, my task is to meticulously preprocess, tokenize, and cleanse the text utilizing advanced Natural Language Processing (NLP) techniques. The goal is to transform the nuanced intricacies of human language into a structured format that computers can seamlessly interpret. This pivotal process often entails fine-tuning algorithms, tweaking parameters, and experimenting with various methodologies to achieve optimal results.

As the morning unfolds, I navigate through lines of code, leveraging sophisticated algorithms to empower the system to distinguish entities with precision. This process is underpinned by an iterative approach – experimenting, refining, and optimizing to ensure the highest accuracy levels.

Incorporating green tea breaks and moments of contemplation, I approach this phase with a mix of determination and curiosity, understanding that every stride I take in refining entity recognition and data interoperability contributes to the broader goal of enriching healthcare systems with seamlessly interconnected information.


Afternoon: Designing Experiments

After relishing a hearty lunch, I convene virtually with my esteemed research cohort, driven by the foundational principle of collaboration in academia. The exchange of ideas amongst fellow researchers consistently ushers in groundbreaking innovations. Through vibrant discussions, we propel ourselves towards novel breakthroughs in our field.

Within these dynamic meetings, we delve into a rich tapestry of topics, spanning experimental designs to methodological intricacies. These dialogues serve as a crucible for diverse perspectives, fostering an environment that nurtures innovation as we collectively steer the course of our research. Venturing into unexplored avenues is our norm, while also addressing challenges that may arise, capitalizing on the collective expertise to unveil solutions that might elude us in isolation.


Late Afternoon: Coding and Coffee

As the afternoon sun casts a warm glow on my workspace, I delve into coding. NLP demands a deep understanding of programming languages, libraries, and frameworks like Python, TensorFlow, and spaCy. My current project involves building a model that can extract relevant medical information from clinical notes, contributing to the development of more accurate diagnosis and treatment strategies. Lines of code are written, tested, debugged, and optimized, inching closer to a solution that aligns with both scientific rigor and practical application.


Evening: Reflection and Planning

As the day winds down, I take a moment to reflect on the progress made. The journey of a clinical data science PhD student is paved with challenges and eureka moments alike. The iterative process of hypothesis formulation, experimentation, and refining methods is the backbone of scientific progress. I update my to-do list, outline the tasks for tomorrow, and jot down any new ideas that emerged during the day. 


Conclusion: Nurturing the Nexus of Science and Healthcare

Being a PhD student in clinical data science with a focus on natural language processing is both intellectually exhilarating and humbling. Each day offers a chance to contribute to the evolution of healthcare by bridging the gap between linguistic nuances and data-driven insights. From deciphering patient narratives to designing innovative models, the journey is one of continuous learning, collaboration, and pushing the boundaries of what's possible. As I close my laptop, I'm filled with a sense of purpose – knowing that the work I do today could shape the healthcare landscape of tomorrow, not just in Graz but beyond.