DISCUSIÓN DE RESULTADOS

To sum up, different time-based BNs have been proposed over the years. DBNs are the most applied as they are easy to build, train and use. However, they are based on two strong assumptions; time discretisation and stationary transitions. These assumptions are not true in many applications. Several approaches have been proposed to overcome these assumptions. However, most of the described methods, use data to estimate either fixed or flexible granularities. This can be useful when we have rich and high-quality data, with a clear passing of time, which is not always the case. Thus, using data to estimate the granularity may lead to unnecessary time slices that are not relevant with how the model is intended to be used. CTBNs can be adjusted to the decision making timeline but learning and interpreting them, especially the intensity matrices, is a difficult task. Many methods try to overcome the assumption of stationarity by allowing the structure and/or the parameters of the model to change over time. Most of these methods allow one or more edges to change among a predefined set of variables. The fixed set of variables represents the available data. However, in many problems, especially in medicine, information that should be included in the model may not be captured. In the following chapter, we review the application of the above techniques in medical problems and we investigate the barriers that they may face in assisting clinical decision making.

Chapter 3

Useful Clinical Decision Support Models

This chapter focuses on how CDS models can assist clinical decision making. First, we present some medical BN applications, as well as their limitations, and then we investigate why many of the developed models have not been used in clinical practice. The limitations described in this chapter lead to the contributory Chapters 5 and 7.

3.1

Introduction

Clinical decisions can be grouped into three main categories: (1) diagnosis, (2) prognosis and (3) treatment. During diagnosis, clinicians evaluate the patient’s symptoms and signs and decide which disease explains them better. In prognosis, clinicians use their knowl- edge, experience and patient’s characteristics to predict the outcome of the disease and the frequency with which it is expected to occur. Finally, clinicians evaluate patient’s char- acteristics and history to decide the appropriate treatment. Clinicians take thousands of decisions of all types during their career.

In recent years, advances in diagnostic tests and in understanding both the causes of dis- eases and the benefits of different treatment options have generated more and more evidence that needs to be considered in clinical decisions. However, clinicians may have difficulties combining all the available evidence to make an appropriate decision [27]. In some types of care, clinicians need to take a decision very quickly [79]. For instance, a surgeon in the ED takes several decisions under time pressure, such as whether to wait for the computed tomography scan results or to go straight to theatre as the patient’s condition is critical. When time is pressuring, clinicians rely mainly on a hunch and on a limited amount of

evidence. In both cases, clinicians reason under uncertainty by combining appropriately all the available information.

Many ways to assist clinical decision making have been suggested [251]. One way to assist clinical decision making is to use CDS models [1], [239], [61], [18], [205], [2], [45], [147]. A CDS model can be a scoring system, a rule-based system, a regression model, a neural network or a decision tree [2]. Although each type offers advantages (e.g. simplicity or ease of inference), not all are able to capture the uncertain nature of medicine in contrast to BNs, which as explained in Chapter 2, offer a natural way to represent the uncertainties involved in medicine when dealing with diagnosis, prognosis and treatment. In addition, one of the main advantages of BNs is that they can represent both the data and clinician’s knowledge and reasoning. Most of the other specified CDS models are largely data-driven, a fact that can be problematic when developing medical models. Another recently proposed graph- ical CDS model is the chain event graph, which is a compact form of event tree model [247], [15] that has also a dynamic counterpart [16]. Despite its potential advantages in many staged-care medical problems, a drawback is that a chain event graph becomes hard to understand in large problems with more than 20 states, this may explain why chain event graphs have not yet become as widely known or supported as BNs. For all the described reasons, we will focus from now on only on CDS BN models.

In this chapter, we review different types of BNs that have been developed in medicine. We investigate their benefits, but also their limitations in practice for improving decision making. The remainder of this chapter is organised as follows: in Section 3.2, we present various medical BN applications, as well as their limitations. In Section 3.3, we give rea- sons why some models are not as useful for practical decision support as might be hoped.