Pobreza e informalidad urbana

The work presented in this thesis is an initial platform for the DSI, DSF and Pre- dictAD tool. There are many avenues of research that can be explored to take them further.

The most pressing consideration for future research is a limitation mentioned above: addressing how these methods are best applied when multiple diseases are under consideration. Since the DSI algorithm is computationally inexpensive, several hypotheses can be evaluated quickly. Thus, reducing the multi-class prob- lem into multiple binary classification problems is a valid strategy. Building binary classifiers allows distinguishing between one disease and the rest (one versus all) or between every pair of diseases (one versus one). In one versus all, the disease getting the highest DSI value is the one that patient data resembles most closely. In the one versus one approach, every classifier assigns the patient to one of the two diseases and a composite classification is produced using voting or some other ensemble method. In addition to extensions to the data analysis with the DSI, the DSF visualization requires further development to allow quick interpretation of data produced in multi-class classification problems.

In the future, the DSI should provide support also for features for which values can both increase and decrease in the case of pathology. For example, sleep of eight hours per day can be considered normal but both four hours and twelve hours may be indicative of dementia. As described in Section 4.3.2, the DSI cur- rently requires such features to be split into two. The DSI method would benefit from automatic detection of such features and from a fitness function that produces

increasing DSI values when deviating from the normal range, irrespective of the direction of the deviation.

The DSI was designed to be used with unprocessed raw patient data collected at clinics in routine investigations. Accordingly, in the original publications comprising this thesis, using raw heterogeneous patient data without any pre-processing did not adversely affect accuracy or performance. The hierarchical evaluation of the DSI appears to alleviate issues in data correlation as described in Section 4.3.7, but correlations between features and their impact on the optimal organization of the DSI tree hierarchy should be studied more carefully. Generally, developing methods for constructing, optimizing and validating the DSI tree hierarchies is considered an important future research path. As new data sets arrive, there should be tools that propose a hierarchy suitable for data analysis with the DSI method instead of the current ad-hoc approach. Similarly, feature selection methods and the need to apply them should be studied more closely.

With the extensions to the DSI and DSF methods described above, the methods should be able to handle most clinical decision support problems for which they are intended. To verify this, the methods must be tested extensively, using as many data sets as possible. It is expected that in terms of accuracy, the DSI method will not be the best possible classifier for every problem. For decision support and data visualization, it is nevertheless important that the method con- stantly achieves good accuracies compared with other classifiers, so that it is known to perform robustly in a wide range of problems.

To really see whether the PredictAD tool is able to improve the diagnostics of AD, it must be evaluated with unselected prospective patients at several memory clinics. As was already mentioned, there are several studies in the planning phase that have this agenda. The goal of these studies is to verify that when clinicians analyse patient data with the help of the DSI and the DSF, they are able to make AD diagnoses earlier and more accurately. When support for differential diagnos- tics is added to the DSI and DSF, the clinical evaluations will also include consid- eration of multiple possible dementias. In clinical diagnostics, connecting the DSI to the updated hypothetical model of the AD progression should also be taken into account [Jack 2013]. Since the DSI produces results normalized to a range be- tween zero and one, it should be relatively easy to provide additional visualizations of the data overlaid on the hypothetical model. In the clinical evaluations, it is also important to make sure that clinicians can use the CDSS easily, so that it is not dismissed because of usability issues.

Lastly, to simplify deployment of the PredictAD tool to various clinics, tighter in- tegration with HISs should be explored. Although the current implementation can receive brain MRIs from the hospital’s picture archiving and communication sys- tem (PACS), it is still considered a stand-alone system (category 1) as defined in Section 2.2. The PredictAD tool could be a better fit to clinics as an integrated system (category 2) or as a service model (category 4). Making the switch is mostly a technical and financial issue, due to the fact that HISs are complex software systems and integrations with them are expensive to implement. Standards, like the ones presented in Section 2.2 should help in integration work, but as Brooks

[1987] has said, there is no silver bullet. One option to reduce the need for exten- sive integrations is to consider business models for bringing a subset of the tools available to clinicians. There may be ways to provide some of the DSI, DSF, and PredictAD tool functionalities to clinicians in a way that would still provide benefits while keeping integrations to existing systems narrow in scope.