Search Results for author: Daniele Soria

Found 8 papers, 0 papers with code

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer Tissue Microarray

no code implementations19 Jan 2018 Jingxin Liu, Bolei Xu, Chi Zheng, Yuanhao Gong, Jon Garibaldi, Daniele Soria, Andew Green, Ian O. Ellis, Wenbin Zou, Guoping Qiu

To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score.

Decision Making

Attributes for Causal Inference in Longitudinal Observational Databases

no code implementations3 Sep 2014 Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard

In this paper we investigate potential attributes that can be used in causal inference to identify side effects based on the Bradford-Hill causality criteria.

Causal Inference feature selection +2

Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs

no code implementations2 Sep 2014 Jenna M. Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard

Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.

Pharmacovigilance Specificity

A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery

no code implementations2 Sep 2014 Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard

Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects.

Clustering Marketing +1

Comparison of algorithms that detect drug side effects using electronic healthcare databases

no code implementations2 Sep 2014 Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack Gibson, Richard Hubbard

In this paper we apply four existing electronic healthcare database signal detecting algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the THIN database for a selection of drugs from six chosen drug families.

Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases

no code implementations5 Jul 2013 Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard

Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects.

Marketing

Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification

no code implementations5 Jul 2013 Chris Roadknight, Uwe Aickelin, Alex Ladas, Daniele Soria, John Scholefield, Lindy Durrant

In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours.

Classification Clustering +2

Discovering Sequential Patterns in a UK General Practice Database

no code implementations4 Jul 2013 Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard

The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses.

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