ai medicine

A.I. and Healthcare – Challenges and Solutions

Artificial Intelligence (AI) refers to a broad umbrella of technologies that enable machines to perform tasks that were formerly only capable of being carried out by humans. AI is a key enabler of what is being referred to as the Fourth Industrial Revolution and as such its usage will become prevalent right across society including the healthcare sector. There are many challenges to applying AI some of which are more salient depending on the area of application. Here I discuss some of the challenges to implementing AI in healthcare specifically and some possible solutions.

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Using R for content analysis of short documents.

Recently I was trying to extract structure from a large corpus of documents. Nearly all the documents were short, many were just notes of one or two lines in length. Regular approaches to clustering do not work so well on this task. Nonetheless after doing some research I found a suitable method that I was able to apply on the data using the statistical programming language R.

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Sentiment Analysis of Web-Scraped Near-Death Experience Narratives

The recent Surviving Death documentary on Netflix is an interesting look at some of the evidence for the survival hypothesis in parapsychology. The first program in the series deals with Near-Death Experiences (NDEs). Accounts of NDEs are of interest to scientists, philosophers and others because of the possible insights they can provide into the nature of the mind-brain relationship. NDEs are also of interest because of their effects. NDEs are often profoundly transformative, having long-lasting and major effects on a persons attitudes and values. There is some research that shows that just learning about NDEs can bring psycho-spiritual benefits.

I have just had a paper published in the Journal of Near-Death studies in which I used a computational technique known as sentiment analysis to measure the sentiment polarity of the words with which people described their NDEs.

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Five must read books for beginning data scientists.

As a fan of lifelong learning, one thing I like about data science and data analytics is that there‚Äôs always new things to learn and most of them have useful practical applications. The explosion of interest in the field in the last few years means there are a bewildering amount of online resources, tutorials and courses for learning data science. For someone taking the first steps on their data science journey it can be difficult to know where to start. The humble book still has it’s place as a source of knowledge and new ideas though. And here are 5 books that everybody interested in data science should read.

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On science and belief in science.

As a sometime Twitter user, it always leaves me a little bemused when I see tweets that exhort people to #BelieveinScience. This slogan is becoming more popular not just in social media but in the media in general and other spheres of daily activity. As of the time of writing the Science Foundation of Ireland carry this hashtag on their homepage and use it in their social media activity. While no doubt the Science Foundation do useful and important work, #believeinscience is a somewhat unfortunate hashtag to choose for an organisation that promotes and funds scientific research. Why so?

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Demoing the stylo package at whyR – Career Zoo

When I was asked to do a workshop for the recent whyR mini conference running alongside Career Zoo at Thomond Park, I had to give some thought as to what to present on. I wanted to demo something that showed the power of R while at the same time being easy to use and something that might be at least somewhat interesting and fun for participants!

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Learning Analytics – Predicting Academic Performance

A recent longitudinal study by the HEA that tracked the progress of more than 34,000 students enrolled in third level education in Ireland in 2007/2008, found that 76% graduated over the following ten years. Completion rates varied somewhat by type of college, subject and gender. Overall 58% of students graduated on time. Although apparently these figures compare well internationally, one can see that more than 40% of third level students in this cohort didn’t graduate on time and nearly a quarter hadn’t graduated in the following ten year period.

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#RugbyWorldCup on Twitter – Part 2

This is the second part of my analysis of tweets containing #RugbyWorldCup based on a dataset collected from Twitter using rtweet between 24th September and 12th October. In this part I will be analysing the tweets themselves, doing some data exploration, sentiment analysis and topic modelling.

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The #RugbyWorldCup so far on the Twitterverse

We’re about halfway through the 2019 Rugby World Cup, the pool stages finished yesterday and I thought it might be interesting to look at what the Twitterverse has made of the tournament so far.

Since just after the beginning of the tournament (24th September to be precise) up til 12th October I have been connecting to the Twitter API every few days using rtweet.

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