Myeloproliferative Neoplasms (MPNs) are a group of bone marrow diseases with potentially lethal cardio-vascular complications. Two sub-diseases of MPN are Essential Thrombocytosis (ET) and Polycythemia Vera (PV), which are recognised by an abnormal blood count of respectively thrombocytes and red blood cells.

If an MPN is treated appropriately, complications for patients are reduced, leading to a relative increase of patients life expectancy. However, MPN is often recognised long after the first clinical signs. 1/4 of MPN patients already had abnormal blood measurements for longer than 1 year in advance of their diagnosis.

Therefore, there is the call for methods for earlier recognition of MPN. Screening like methods could be useful to alert clinicians in case of a suspected case. Although genetic testing is conclusive in recognising MPN, high costs make that they are only applied in case of already clinically suspected MPN.

In this thesis, the outlines of a method are proposed for early detection of MPN patients based on blood measurements in the general hospital laboratory workflow. A two stage solution is proposed:
• Stage 1: Filter on regular blood measurements (combined with demographic data);
• Stage 2: Filter based on microscopy imaging of blood.

The primary scope of this thesis is the development of the first stage for ET and PV subtypes of MPN. A machine learning algorithm called XGBoost is utilized to develop classification algorithms for ET and PV in this stage. Patients with elevated blood platelet counts (ET marker) or elevated red blood cell indicators (PV marker) were separately included in a nested cross validation setup for training and testing of the algorithms. For ET vs control classification, mean metrics obtained during cross validation are AUC: 0.87, recall (sensitivity): 0.74 and specificity: 0.84. For PV vs control corresponding metrics are respectively 0.86, 0.66 and 0.87.

Regarding the development of methods for stage 2, a first step is set. A XGBoost model using cell counts from microscopy images as features results in mean AUC, recall and specificity scores of 0.67, 0.78 and 0.80 respectively when trained and tested using nested cross validation. Training a Convolutional Neural Network (CNN) to take microscopy images as input and return MPN vs control classification resulted in an algorithm which only predicted control cases. These results give an indication of the potential of microscopy for automated MPN recognition, calling for further development of the stage 2 filter.

With this proposed laboratory population screening method and the developed blood measurement based filtering, a next step is set toward early detection of MPN in order to prevent (lethal) MPN related complications.

Many machine learning models have been developed to aid in the diagnosis of dementia, to predict dementia risk and to determine cognitive performance. While it is well known that vascular pathology is a critical contributing factor to dementia, cerebral small vessel disease is often not addressed by these methods, possibly causing impeded generalizability of the models from research setting to clinical practice. The aim of this thesis is to evaluate whether vascular pathology, as represented by white matter hyperintensities (WMHs), influences the outcome of machine learning prediction models for dementia and as such hampers generalizability of these models from the research setting to clinical practice. To answer this research question, we developed a convolutional neural network (CNN) for prediction of cognitive performance measured by MMSE and ADAS13. We used ADNI data and included all subject timepoints that adhered to our inclusion criteria (N = 4846). Next, we derived gray matter density maps (GMDMs) and WMH density maps (WMHDMs) from T1 and FLAIR MRI scans using voxel-based morphometry. Six CNN models were implemented, differing in input: model 1 (GMDM), model 2 (GMDM, age, sex), model 3 (WMHDM), model 4 (WMHDM, age, sex), model 5 (GMDM, WMHDM, age, sex) and model 6 (GMDM, log(WMH ratio), age, sex). WMH ratio was defined as WMH volume corrected for intracranial volume. We split the data into a low, middle and high WMH load group based on WMH ratio tertiles and performed two sets of experiments. First, we trained all models on the low WMH load group with the intention of testing on the high WMH load group as a measure of generalizability. Second, we trained model 2 and 5 on data from all WMH load groups. For these sets of experiments, we split both the low WMH load group and all WMH load groups into a train, validation and test set. The models trained on the low WMH load group obtained a similar performance to prediction of a constant value and prediction by a model developed on randomized cognitive scores. These models likely suffered from the underrepresentation of lower cognitive performance data (i.e. high ADAS13 scores and low MMSE scores). This is supported by the fact that model 2 and 5 outperformed constant value prediction when trained on data from all WMH load groups. The current models did in general not benefit from adding WMHDM input to the model, while they did benefit slightly from log(WMH ratio) information. However, we were unable to answer our research question due to the suboptimal performance of the models. Our future research will focus on revisiting our research question through brain age prediction in order to improve generalizability of ML methods for AD to clinical populations with prominent vascular pathology.