Healthcare has many challenges in form of monitoring and predicting adverse events as healthcare associated infections or adverse drug events. All this can happen while treating a patient at the hospital for her disease. The research question is: When and how many adverse events have occurred, how can one predict them? Nowadays all information is contained in the electronic patient records and are written both in structured form and in unstructured free text.
This talk will describe the data used for our research in HEALTH BANK – Swedish Health Record Research Bank containing over 2 million patient records from 2007-2014.
Topics are detection of symptoms, diseases, body parts and drugs from Swedish electronic patient record text, including deciding on the certainty of a symptom or disease and detecting adverse (drug) events. Future research are detecting early symptoms of cancer and de-identification of electronic patient records for secondary use.
Hercules Dalianis, Master of Science in engineering (civilingenjör) with speciality in electrical engineering, graduated in 1984 at the Royal Institute of Technology, KTH, Stockholm, Sweden, and received his PhD/Teknologie doktor in 1996 also at KTH. Since 2011 he is professor in Computer and Systems Sciences at Stockholm University, Sweden.
Dalianis was post doc researcher at University of Southern California/ISI in Los Angeles 1997-98. Dalianis was also post doc researcher (forskarassistent) at NADA KTH 1999-2003, moreover Dalianis held a three year guest professorship at CST, University of Copenhagen during 2002-2005, founded by Norfa, the Nordic Council of Ministers. Dalianis were on a sabbatical stay at CSIRO/Macquire University, Sydney, Australia 2016-17 compiling a text book with the title Clinical text mining: Secondary use of electronic patient records, that will be published open access at Springer in April 2018.
Dalianis works in the interface between industry and university and with the aim to make research results useful for society. Dalianis has specialized in the area of human language technology, to make computer to understand and process human language text, but also to make a computer to produce text automatically. Currently Dalianis is working in the area of clinical text mining with the aim to improve healthcare in form of better electronic patient record systems, presentation of the patient records and extraction of valuable information both for clinical researchers but also for lay persons as for example patients.