Best Research Poster Award  – Prediction and Intervention of Colorectal Cancer Risk with an Artificial Intelligent System by ESMO 2018 Congress.

Prediction and Intervention of Colorectal Cancer Risk with an Artificial Intelligent System was presented at the ESMO 2018 Congressin Munich, Germany on 19 – 23 October 2018.

A total of 720,182 subjects including 18,406 colorectal cancer patients and 701,776 normal people were involved in the study. The predicting accuracy was 96.1% and the clinical verification rate was 95.7%.

Intelligent Stroke Risk Prediction Platform was presented at the 26th European Stroke Conferencein Berlin, Germany on 24 – 26 May 2017.

A total of 612,962 people including 21,091 stroke patients and 591,861 normal people were involved in the study. The analysis showed that age, Systolic & Diastolic blood pressure, CBC, CMP and Lipids data can significantly distinguish normal people from stroke patients. The predicting accuracy was 98.1% and the clinical verification rate was 95.9%.

Predicting Early Cervical Cancer using Big Data was presented at the 2016 World Cancer Congress in Paris, France on 31 Oct – 3 Nov 2016.

A total of 368,060 females including 2,975 (1,642 in 2010-2013 & 1,333 in 2014) cervical cancer patients and 365,085 (224,357 in 2010-2013 & 140,728 in 2014) normal people were involved in the study. The predicting accuracy was 96.4% and the clinical verification accuracy was 95.8%.

Blood Test Results to Predict Early Esophageal Cancer was presented at the 2016 World Cancer Congressin Paris, France on 31 Oct – 3 Nov 2016.

A total of 449,798 people including 3,621 esophageal cancer patients and 446,177 healthy people were involved in the study. The accuracy of the prediction was 96.2% and the clinical verification accuracy was 96.5%.

Big Data Method for Early Prediction of Liver Cancer was presented at The 40th ESMO European Cancer Congressin Vienna, Austria on 25 – 29 Sep 2015.

A total of 460,627 people including 14,450 liver cancer patients and 446,177 normal people were involved in the study. The predicting accuracy was over 96% and the clinical verification accuracy was over 95%.

Predicting Early Lung Cancer using Big Data was presented at the ELCC 2015 European Lung Cancer Conferencein Geneva, Switzerland on 15 – 18 April 2015.

A total of 345,610 people (from 2010 to 2013) including 9,510 lung cancer patients and 336,100 normal people were involved in the research development stage and a total of 113,677 (in 2014) including 3,600 lung cancer patients and 110,077 normal people were involved in the clinical verification stage. The accuracy of the lung cancer risk prediction models was 95.5% and the prediction model was verified by a total of 113,677 people ( in 2014) including 3,600 lung cancer patients and 110,077 normal people, the accuracy of the verification was 96.8%.

Predicting Early Lung Cancer using Big Data was published in the Annals of Oncology 2015; 26: supplement 1 – i7.