Text mining pathology reports

Background

Within the department nephrology, a biopsy is taken from patients for further research. This biopsy can be analyzed with three different methods:

  • Light microscopy
  • Immunofluorescence
  • Electron microscopy

The analysis and the final diagnosis of the biopsy are documented in a pathology report. However, there is a suspicion that only light microscopy and immunofluorescence are required in making a diagnosis. In some cases, electron microscopy adds nothing to the process of making a diagnosis. There is demand for a decision supporting algorithm, which state a diagnosis, based on the text. Furthermore, this explainable algorithm makes the process of concluding such a diagnosis clear. In this way, certain characteristics of the kidneys can be extracted, which have led to a particular diagnosis. It can also show abnormalities in the decision making, in which human beings would decide otherwise.

Research question and tasks

The main research question is: “Is it possible for a predictive algorithm to get to the same diagnosis as a human being when analyzing nephrology pathology reports?”

Tasks

  • Design a text mining algorithm which states a diagnosis based on text
  • Compare the text mining algorithm with the performance of nephrologists
  • Exclude some medical details (e.g. electron microscopy) to see whether the algorithm still performs the same

Innovation

The goal of this project is to design a text mining algorithm, which makes (with a fairly high accuracy) the same diagnosis as a nephrologist would when analyzing pathology reports.

People

Josien Visschedijk

Josien Visschedijk

Master student

Computer Science, Radboud University

Jan van den Brand

Jan van den Brand

Postdoctoral researcher

Nephrology, Radboudumc

Wynand Alkema

Wynand Alkema

Head of RTC Bioinformatics

Radboudumc

Jack Wetzels

Jack Wetzels

Professor

Nephrology, Radboudumc

Djoerd Hiemstra

Djoerd Hiemstra

Professor

Computer Science, Radboud University