Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Physical activity based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks

Serious Mental Illnesses (SMIs) including schizophrenia and bipolar disorder are long term conditions which place major burdens on health and social care services. Locomotor activity is altered in many cases of SMI, and so in the long term wearable activity trackers could potentially aid in the early detection of SMI relapse, allowing early and targeted intervention. To move towards this goal, in this paper we use accelerometer activity tracking data collected from the UK Biobank to classify people as being either in a self-reported SMI group or an age and gender matched control group. Using an ensemble dense neural network algorithm we exploited hourly and average derived features from the wearable activity data and the created model obtained an accuracy of 91.3%

Decision-theoretic planning of clinical patient management

When a doctor is treating a patient, he is constantly facing decisions. From the externally visible signs and symptoms he must establish a hypothesis of what might be wrong with the patient; then he must decide whether additional diagnostic procedures are required to verify this hypothesis, whether therapeutic action is necessary, and which post-therapeutic trajectory is to be followed. All these bedside decisions are related to each other, and the whole task of clinical patient management can therefore be regarded as a form a planning. In Artificial Intelligence, planning is traditionally studied for situations that are highly predictable. An important characteristic of medical decisions is however that they often must be made under conditions of uncertainty; this is due to errors in the results of diagnostic tests, limitations in medical knowledge, and unpredictability of the future course of disease. Decision making under uncertainty is traditionally studied in the field decision theory; in this thesis, we investigate the problem of clinical patient management as action planning using decision-theoretic principles, or decision-theoretic planning for short

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

Analyzing Differences in Operational Disease Definitions Using Ontological Modeling

In medicine, there are many diseases which cannot be precisely characterized but are considered as natural kinds. In the communication between health care professionals, this is generally not problematic. In biomedical research, however, crisp definitions are required to unambiguously distinguish patients with and without the disease. In practice, this results in different operational definitions being in use for a single disease. This paper presents an approach to compare different operational definitions of a single disease using ontological modeling. The approach is illustrated with a case-study in the area of severe sepsis

Instance-based Prognosis in Intensive Care Using Severity-of-illness Scores

Abstract This paper explores the use of instance-based reasoning (IBR) to estimate the probability of hospital death in patients admitted to the Intensive Care Unit (ICU). The predictions are based on severity-of-illness scores that indicate the state of the patient. We have implemented an instance-based reasoning algorithm as an alternative to logistic regression (LR) models to predict hospital mortality. The performance was measured and prospectively validated. Results show that instance-based reasoning is competitive to logistic regression