Clinical medicine is biomedical engineering in practice. Through my MD/PhD I had the unique opportunity of training as both a physician and an engineer. Almost always I found myself seeing that the two were actually the exact same - with only formal math marking a difference. My clinical training not only helped me directly understand my PhD project, increasing its chances of success, the training also let me better apply my engineering eye towards developing a system’s level understanding spanning medicine.
Clinical medicine is, in many respects, applied control theory. The approaches in control theory, combined with the power of machine learning in deriving complex models from data, are perfectly suited for clinical research.
- Blogposts here
Engineer’s Guide to Medicine
How would we teach medicine if we were doing it in the style of a ‘car manual’?
The heart is an electrical+mechanical system and there’s no reason the same tools used to understand neuronal networks can’t provide a unique understanding of cardiac systems.
Clinical medicine is a complicated thing. Training in it has given me an important perspective into how all the theory, all the math that goes into research gets completely warped by the real-world. Most importantly, the training in clinical medicine I received at Emory made me a better engineer, more capable of understanding the many facets of patient care that influence our ability to implement and study DBS.
In 2020 a group of us started a workshop series to help medical students learn machine learning (ML) through interactive Python notebooks: for medical students by medical students. The goal was to have more medical students, inspired by the workshops and the opensource starter code, engage with ML projects during their ‘Discovery project’, a research project done in the final year of medical school.
- Workshop Notebooks - head on over to the notebook series at the repo
- Videos - Workshops are scheduled for May+June 2020. Stay tuned, we’ll try to have the videos up ASAP here
- Connect - keep up to date with workshops, resources, and medicine-related ML news @MedicalML
Coming out of my PhD I felt like a competent neuroengineer, but there were still so many things about the patient, as a whole, that I had no clue about. Being a ‘hands-on’ type, direct medical training was critical for me to become the type of neuroengineer I wanted to be. There’s a lot to say about medical training, and a lot of hearsay has certanly been hear-said, but the actual experience is ineffable. But I’ll try nonetheless…
- Blogposts - in progress
- Medium posts -
The Clinical Machine
An engineer’s guide to medicine and (patho)physiology.