This is my first datathon and this blog just summarises some of the themes/discussions at the conference. As a declaration of interest, I believe in the collaborative use of healthcare data to improve patient care BUT I am NOT a data scientist and barely write a Python/R script.
Do you know the difference??
There is just too much data in the ICU – you need to understand it.
Data by themselves are uselss. To be useful, data must be analysed, interpreted and acted upon. Thus, it is the algorithms – not data set – that will prove transformative.
The transformation will be in the form of:
– Decision support, prognostication and diagnostics
– Personalised medicine
– Continuous learning
– Knowledge discovery
By freeing physicians from the tasks that interfere with human connection, AI will create space for the real healing that takes place between a doctor who can listen and a patient who needs to be heard.
Geert has a team of data scientist working with the clinical team. One should not try to be the other.
- Lack of standards
- Missing or incomplete data
- Can be unbiased or random
- Most often biased (eg. lactate measurements in sickest pts)
- Will influence the performance of Machine Learning models
- Access to data, privacy and regulatory issues
- Who owns shared data?
- Who oversees the correct use
Data analysts: why invest in ICM? (M. Flechet @FlechetMarine)
*I love her slideset
The Vs of Big Data
Healthcare Big Data and the Promise of Value-Based Care
The data scientist as part of the medical team and the doctor as an information coach (L. Celi @MITcriticaldata)
Healthcare is a failed business model
- Under-reported and under-appreciated degree of medical errors
- Inequalities in care delivery
- Enormous waste of resources: over-testing, over-diagnosis, oer-treatment
- Large information gaps from imperfect medical knowledge system
- Inefficiencies in workflow
- High level of workforce burnout
Opportunities in AI in healthcare
- Classification: image recognition, risk stratification
- Prediction: disease trajectory and prognosis, clinical events for triaging, treatment response
- Optimisation aka precision medicine: diagnostic and screening strategies, defining therapeutic targets
Challenges for AI in healthcare
- Labelling, a requirement for classification and prediction, is not straightforward
- Model validity is limited by time and space
- Machine bias
- Optimal outcomes may vary across different stakeholders
- Short term gais may not translate to long term benefits
- Over-diagnosis (and over-treatment) will surge
Using machine learning, the degree of uncertainty may actually increase
The new smart will be determind not by what or how we know, but by the quality of our thinking, listening, relating, collaborating and learning.
* I would highly recommend the following links as a good starting point if you are interested in database research
MIMICIII is arguably the most well known freely accessible critical care database.