REPORT 04/2018 Health Analytics Makhlysheva A., Budrionis A., Chomutare T., Nordsletta A.T., Bakkevoll P.A., Henriksen T., Hurley J.S., Bellika J.G., Blixgård H., Godtliebsen F., Skrøvseth S.O., Solvoll T., Linstad L. Health Analytics Report number Summary 04-2018 The health analy�cs review focuses on machine learning, natural language processing, data mining and Project manager process mining methods: their usefulness, use cases, Per Atle Bakkevoll tools and relatedness to Norwegian healthcare. Authors The report aims to increase the audience’s beter Alexandra Makhlysheva understanding of health analy�cs and the methods to Andrius Budrionis be u�lized in improving the performance of healthcare. Taridzo Chomutare Keywords Anne Torill Nordsleta Health data, health analy�cs, ar�ficial intelligence, ma- Per Atle Bakkevoll chine learning, deep learning, data mining, process Torje Henriksen mining, natural language processing Joseph Stephen Hurley Johan Gustav Bellika Publisher Håvard Blixgård Norwegian Centre for E-health Research Fred Godtliebsen PO box 35, NO-9038 Tromsø, Norway Stein Olav Skrøvseth Terje Solvoll E-mail: [email protected] Line Linstad Website: www.ehealthresearch.no ISBN 978-82-8242-086-0 Date 11.06.2018 Number of pages 88 1 Table of contents 1. Background ........................................................................................................................... 4 1.1. Project objec�ve ................................................................................................................... 5 1.2. Approach ............................................................................................................................... 5 1.3. Organiza�on of the report .................................................................................................... 5 2. Machine learning .................................................................................................................. 6 2.1. Defini�on of the concept ...................................................................................................... 6 2.1.1 Supervised learning ....................................................................................................... 7 2.1.2 Unsupervised learning ................................................................................................. 13 2.1.3 Semi-supervised learning ............................................................................................ 16 2.1.4 Reinforcement learning ............................................................................................... 17 2.1.5 Neural networks .......................................................................................................... 18 2.1.6 Interpretability of machine learning algorithms .......................................................... 21 2.2. Usefulness – why do we need this technology? ................................................................. 22 2.3. Status of the field ................................................................................................................ 23 2.3.1 Reasons for ML rise ...................................................................................................... 23 2.3.2 Privacy concerns in machine learning ......................................................................... 25 2.4. Use cases ............................................................................................................................. 26 2.4.1 Diagnosis in medical imaging ....................................................................................... 26 2.4.2 Treatment queries and sugges�ons ............................................................................. 27 2.4.3 Drug discovery/drug development .............................................................................. 27 2.4.4 Improved care for pa�ents with mul�ple diagnoses ................................................... 27 2.4.5 Development of clinical pathways ............................................................................... 28 2.4.6 Popula�on risk management ....................................................................................... 28 2.4.6 Robo�c surgery ............................................................................................................ 28 2.4.7 Personalized medicine (precision medicine) ............................................................... 29 2.4.8 Automa�c treatment/recommenda�on ...................................................................... 29 2.4.9 Performance improvement ......................................................................................... 30 2.5. How does this relate to Norway and the needs in Norwegian healthcare? ....................... 30 3. Natural Language Processing (NLP) .......................................................................................32 3.1. Defini�on of the concept .................................................................................................... 32 3.1.1 Tasks NLP can be used for [110] ................................................................................. 33 3.1.2 Example of NLP pipeline .............................................................................................. 33 3.2. Usefulness – why do we need this technology? ................................................................. 34 3.2. What is the knowledge in the field? ................................................................................... 35 3.2.1 Seminal work ............................................................................................................... 35 3.2.2 Current ac�vi�es and expected future trends ............................................................. 35 3.3. Use cases ............................................................................................................................. 36 3.3.1 Norway research and development cases ................................................................... 37 3.4. How does NLP relate to the needs in Norwegian healthcare? ........................................... 37 3.4.1 Elici�ng expert opinion using the Delphi Method ....................................................... 37 3.4.2 Results from expert opinion survey ............................................................................. 38 3.5. Tools and Resources ............................................................................................................ 39 3.6. Acknowledgements ............................................................................................................. 40 2 4. Knowledge discovery in databases (data mining and process mining) ....................................41 4.1. Defini�on of the concept .................................................................................................... 41 4.2. Usefulness – why do we need this technology? ................................................................. 45 4.3. What is the knowledge in the field? ................................................................................... 46 4.4. Use cases ............................................................................................................................. 47 4.5. How does this relate to Norway and the needs in Norwegian healthcare? ....................... 48 5. Tools ....................................................................................................................................49 5.1. Defini�on of the concept .................................................................................................... 49 5.2. Usefulness – Why do we need this? ................................................................................... 49 5.3. What is the knowledge in the field? ................................................................................... 50 5.3.1. Programming language ................................................................................................ 51 5.3.2. Scalability ..................................................................................................................... 51 5.3.3. Enterprise support ....................................................................................................... 51 5.3.4. Installa�on and ease of ge�ng started ....................................................................... 52 5.3.5. License ......................................................................................................................... 52 5.3.6. Impact on IT infrastructure .......................................................................................... 52 5.3.7. Interoperability between models ................................................................................ 52 5.3.8. Hardware dedicated to machine learning ................................................................... 53 5.4. Use cases ............................................................................................................................. 53 5.4.1. TensorFlow ................................................................................................................... 53 5.4.2. H2O.ai .......................................................................................................................... 54 5.4.3. Microso� Azure ........................................................................................................... 54 5.4.4. Gate Developer ...........................................................................................................