PhD Position: Fairness of NLP-supported Clinical Decision-making Process in Healthcare
Text mining and Natural Language Processing (NLP) systems are proving very useful for clinical care and research. Several decisions on patient inclusion/exclusion and coding of key study variables in clinical studies are taken out of the hands of clinicians and put into the care of NLP systems. However, clinical care is not always equitable; for example, women present very different stroke symptoms from men, leading to underdiagnosis and undertreatment of female stroke victims. These inequities are inevitably encoded in the electronic health records (EHR) corpora mined by clinical data scientists. The aim of this project is to study the extent to which existing healthcare inequities are propagated by the application of text mining/NLP to EHR records and to develop methods to assess and reduce the impact of the encoded inequities.
Profile of the candidate
The successful candidate is a talented and enthusiastic upcoming researcher with a strong interest in health inequalities in clinical NLP systems. As a PhD student you will do research at the highest scientific level in a nice and open environment. Do you want to contribute to studying health inequalities in clinical NLP systems? Then apply as a PhD student to this project.
Furthermore you have:
A master degree in Data Science, Computer Science, Computational Linguistics, or a related field with interest in medical applications;
or A master degree in technical medicine or similar and experience in NLP;
A background in machine learning and deep learning is a plus;
Strong programming skills;
Proven academic interest in questions regarding algorithmic applications and digital society;
Interest in working in a very interdisciplinary, cross-university environment;
Interest in contributing to and helping to create the broader algosoc research community;
An excellent written and spoken command of English (written and spoken command of other languages, including Dutch, French, and/or German is a plus);
Affinity with organising workshops, lecture series, and similar events.
If the vacancy appeals to you, but you are doubting whether you might be THE person we are looking for, please do apply. We encourage all qualified applicants, including minorities, women, people with disabilities, and members of other groups underrepresented in academia to apply. We wish to create a consortium that consists of persons who each contribute in their unique way to the team. Complementarity and not homogeneity is what we are looking for.
Developing a societal vision on automated decision making concerns us all. We believe that a diversity of perspectives in our consortium will be important in developing an inclusive societal vision and strive therefore also in our hiring policy for encouraging applicants from diverse backgrounds. We are committed to creating an environment of mutual respect, inclusiveness, equal opportunities with room for situated experiences, diverse perspectives, and ideas to flourish. This commitment applies to our research, organisation, room for flexibility, training, and community activities as well as our hiring strategy.