Artificial intelligence (AI) and Machine Learning (ML) are having a profound impact on the way medicine is being practiced. AI/ML algorithms and techniques fit imaging applications easily and can help with automation. Radiology is the specialty that has benefitted the most from the AI/ML revolution. Melanoma detection in Dermatology is another obvious winner.
Many of the machine learning algorithms are reasonably well known. The real challenge is to get the infrastructure to crunch massive amounts of data, getting the ideal dataset for a problem, optimizing the model for performance and deploying the model for use. If you are relatively new to ML, Kaggle is a useful resource for you to start.
I will briefly introduce Kaggle for those who have not used it before. Kaggle is a platform for posting datasets that you have collected. They also provide ‘kernels’ or computational resources (typically Jupyter Notebooks) for collaborative analysis. The datasets can be made private or public under a variety of license options. Organizations post competitions and reward teams that solve them. Solutions are typically posted as predictions on a test dataset or share the kernel code
I recently noticed a good competition on Kaggle that the eHealth community may find interesting. Aravind Eye Hospital in India has posted a dataset consisting of fundoscopic images of diabetic retinopathy with varying degrees of severity. The dataset consists of thousands of images collected in rural areas by the technicians of Aravind hospital from the rural areas of India. The challenge is to develop a model that can predict the severity of diabetic retinopathy from the fundoscopic image. Further, the successful solutions will be shared with other Ophthalmologists through the 4th Asia Pacific Tele-Ophthalmology Society (APTOS) Symposium.
And an (obvious) upfront disclaimer: This is a learning project. This is not for actual use.
DAD is a database consisting of patient demographics, comorbidities, interventions and the length of stay for the de-identified 10% sample of hospital admissions. DAD (2014-15) has an enhanced dataset with variables that were created at Western to act as flags for ICD-10 and CCI groupings, to make using the file easier.
Here is an experiment with the DAD enhanced dataset to create a Random forest model for predicting the total length of hospital stay (TLOS) in less than 100 lines of code. Random forests are an ensemble classifier, that operates by building multiple decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. This is a learning project for Apache Spark and Spark ML using pyspark. The accuracy of the model taking all derived categorical variables is low.
I have access to Apache Spark @ CC. If you are installing Spark in your computer you may have to change the following:
Parts of this material are based on the Canadian Institute for Health Information Discharge Abstract Database Research Analytic Files (sampled from fiscal years 2014-15). However, the analysis, conclusions, opinions and statements expressed herein are those of the author(s) and not those of the Canadian Institute for Health Information.
The identification of disease specific genes is pivotal in clinical informatics. This paper describes an improved algorithm for machine learning in which the negative N is classified more appropriately as Unknown U.
English: Weka Data Mining Open Software in Java (Photo credit: Wikipedia)
Peng Yang, Xiao-Li Li, Jian-Ping Mei, Chee-Keong Kwoh, and See-Kiong Ng. Positive-Unlabeled Learning for Disease Gene Identification Bioinformatics first published online August 24, 2012 doi:10.1093/bioinformatics/bts504
SVMs are an important tool in bioinformaticians armamentarium. Weka is a collection of machine learning algorithms for data mining tasks.