PRIME is an automated platform to investigate Online Support Group (a.k.a. health forums, online health groups) discussions for extraction and analysis of patient-reported information.

The java source of PRIME is included in the src folder which needs to be build using JAVA 1.8 and the dependencies included in the pom.xml need to be linked using maven.

The executable version of PRIME is included in the bin folder with the execution instructions.

PRIME was trialled on publicly available, patient-reported information on Online Support Groups. Techniques used for extraction and processing of this data are included in this code repository.

PRIME is a collaborative effort from the machine learning researchers of Research Centre for Data Analytics and Cognition with the guidance and support from the clinicians at Austin Health.

The architecture, algorithms are explained and the results are discussed in the following publications.

1. Ranasinghe W, Bandaragoda T, De Silva D, Alahakoon D (2017) A novel framework for automated, intelligent extraction and analysis of online support group discussions for cancer related outcomes. BJU International 120:59–61. [link]
2. Bandaragoda T, Ranasinghe W, Adikari A, de Silva D, Lawrentschuk N, Alahakoon D, Persad R, Bolton D(2018) The Patient-Reported Information Multidimensional Exploration (PRIME) Framework for Investigating Emotions and Other Factors of Prostate Cancer Patients with Low Intermediate Risk Based on Online Cancer Support Group Discussions. Annals of Surgical Oncology 1–9. [link]
3. Ranasinghe W, De Silva D, Bandaragoda T, Adikari A, Alahakoon D, Persad R, Lawrentschuk N, Bolton D(2018) Robotic-assisted vs. open radical prostatectomy: A machine learning framework for intelligent analysis of patient-reported outcomes from online cancer support groups. Urologic Oncology: Seminars and Original Investigations. [link]
4. De Silva D, Ranasinghe W, Bandaragoda T, Adikari A, Mills N, Iddamalgoda L, Alahakoon D, Lawrentschuk N, Persad R, Osipov E, Gray R, Bolton D(2018) Machine learning to support social media empowered patients in cancer care and cancer treatment decisions. PLOS One. [link]
5. Bandaragoda TR, De Silva D, Alahakoon D, Ranasinghe W, Bolton D (2018) Text mining for personalised knowledge extraction from online support groups. Journal of the Association for Information Science and Technology. [accepted]