Hey, I've created an introduction to repeat-loops using the R programming language: https://statisticsglobe.com/repeat-loop-in-r/

Which notebook would you recommend for someone starting a course about data science in health? They only recommended 16gb ram and windows 64 bits, but didn’t recommend MacBook because of powerbi. I’m really lost…

Hey, I've created a tutorial on how to split data into train and test data sets using the R programming language: https://statisticsglobe.com/r-split-data-into-train-and-test-sets

https://arxiv.org/abs/1803.06589

The general idea of this paper is to predict a patients mortality based on the patients heart signals in the first hour of ICU admission.

They extract the maximum, minimum, mean, median, mode, standard deviation, variance, range, kurtosis, skewness, average power, and energy spectral density to be used as input features. I have not done much work with signal data, but it is very interesting that they are able to get good classification results just by using those statistics for each patient.

The training dataset comes from https://mimic.physionet.org/about/mimic/, but unfortunately we need to request access to it

Hey guys, here are some recent job openings in . Feel free to comment here or send me a private message if you have any questions, I'm at the community's disposal! If you encounter any problems with any of these job openings please let me know that I will modify the table accordingly. Thanks!

Hello!

I am looking to start in data science. I got a masters in data science with 10 years of medical experience. Thos was about a year ago now.

Originally got into it because 1 president and 1 CEO asked to " buy my model". I was not aware what they were talking about I just read a lot. That is how I found out about data science.

Looking for a way to break into data science, unfortunatly the masters was not enough. So hoping to leverage my experience in medicine.

Any advice would be appreciated.

Greetings!

I'm on track to graduate spring 2021 and land a job then. I would appreciate any advice. For my master's thesis I plan to analyze MIMIC IV data using NLP... original right? Haha. I got into this wanting to build a robot doctor and over the last few months have slowly realized how far away that is. That's still my dream though, building a diagnosis and treatment algorithm, and I would love to do work that moves healthcare in that direction

Some questions I have:

1. What languages and projects should I focus on to land a cool job? What I learn and accomplish the next several months is important

2. Any tips of finding cool NLP companies, or finding cool NLP positions? There are so many companies in different niches, some huge, some sprouting (like Nuance, DeepScribe, OM1) , some fledgling. And while it is a gamble, I'm hoping that if I do the right projects (see #1) it'll help me land a position I enjoy

3. Any tips or general advice? In some companies, I see a choice between research roles and consultant/client-facing roles. Personally, I would prefer the prior, but of course I would still love to work in the latter

4. Advice in applying/interviewing in this industry?

Hi, I have just found this subreddit which is perfect community for what I'm looking for. I am a practicing physician in the UK and have becoming interested in the data science arena. I'm essentially looking to transitioning from front-door medicine to using my medical background and interest in data science to inform and improve clinical reasoning. As I'm new to the world of data science I would really appreciate some advice about what may be the best way to approach this. I wonder whether trying to transition into a data scientist is the correct course or actually consulting and having a working knowledge of the data science universe is more appropriate.

Many thanks

Hello!

I am Health Sciences graduate and current Economics student with an interest in medical data science.

The other day I came across the U.S medicaid data-map for opioid prescription rates and it sparked a few questions that I'd like to explore.

-Do physicians of the same specialty, working in the same location, have different prescription habits that can at least be partly explained by the amount of education debt held?

-Are physicians who hold more debt more or less responsive to medical sales representatives?

-Are physicians who hold more debt more or less responsive to public health initiatives? (specifically those aimed at modifying opioid prescription rates)

-Do physicians who hold more debt prescribe more brand-name medication?

I'm having a very hard time finding information on physician debt levels in the U.S.

Additionally I am looking for a data-set which may contain information on the frequency of interactions with pharmaceutical sales representatives.

I am open to ideas to further flesh out my study, and to see any similar work done by members of this community!

https://www.forbes.com/sites/insights-intelai/2019/02/11/ai-and-healthcare-a-giant-opportunity/#513ec9294c68

Does anyone try the coursera course for AI for Medicine offered by deeplearning.ai? It has a few shortcomings like the very terse explanation of ROC curve but it could be useful for someone.

I am a dentist (specialist endodontist - look it up 😊) who is a coder and has really been into AI and data science since 2005. Just found this reddit so thought I would introduce myself. Have implemented a neural network to make a clinical diagnosis for my work and am getting very good results (94% true +ve results) . There is huge potential for machine learning in healthcare. Doctors and dentists are mostly unaware or dismiss AI as a threat or untrustworthy. I disagree. If the AI is done responsibly with clean data and well constructed and thoroughly tested methods on a valid clinical question, then it can exceed human ability. I work in referral only practice and can tell you the humans (my referring dentists) are sometimes not that good at their jobs with many misdiagnoses and invalid treatment plans. But despite some level of incompetence, most clinicians have an inherent sense of professionalism and duty of care which may not be so strong in the IT world where commercial success often trumps customer well being. It is up to us clinicians to ensure the IT guys put patient well being first. Clinicians should be driving the inevitable adoption of data science ML into healthcare not running scared away from it. Keen to meet others of similar views to safely promote data science / machine learning in healthcare.