Background: Undetected Intellectual Disability (ID) can lead to chronic stress due to overestimation by society. Chronic stress can cause stress-related health issues, like hypertension, chronic fatigue and abdominal complaints. When a physician (General Practitioner (GP) or medical specialist) does not recognize that a patient has ID, the relation with stress may go unnoticed. In that case, the complaint is often treated as a purely somatic problem, while the underlying cause (overestimation due to unrecognized ID) remains untreated. This can increase healthcare consumption and impair the patient’s quality of life. While physicians with ID-expertise can recognize subtle signs of mild ID, physicians without extensive experience will easily overlook the ID. To improve medical care for patients with ID, we aim to improve ID detection among physicians. As it is not feasible to give all individual doctors an ‘ID-recognition training’, we study the possibility of using AI to improve ID detection. In the past years, we have been working on an ‘ID Alert’ (IDA) using ML. In previous phases of the IDA project, structured Electronic Health Record (EHR) data was used for the creation of an IDA. In addition, in the current study, we investigate the use of unstructured EHR data (clinical text).
Methods: We analyzed unstructured correspondence files of 200 ID-adults and 200 non-ID adults of Novicare, an organization that provides multidisciplinary care to clients with complex and chronic conditions in intra- and extramural settings. Structured clinical data was unavailable. Therefore, we used an automated method of text extraction, de-identification and two types of feature extraction (bag-of-words and clinical concept extraction). Features were compared between ID-adults and non-ID adults. Significant features that were unlikely to be intrinsically different between ID- and non-ID adults were excluded. The remaining significant features were used for the training and evaluation (10-fold stratified cross-validation) of two Gradient Boosting Classifiers.
Results: Most features differed significantly between ID- and non-ID adults due to confounders such as differences in age, type of care and the doctor’s word choice (which is inherent to the specialty and training of the doctor). Significant ‘unbiased’ features identified by both types of feature extraction methods are epilepsy, emotional disturbance (tension, arousal, agitation), visual or hearing problems and the presence of family members during consult. The developed ML models showed Areas Under the Curve (AUCs) of 0.98 and 0.89 for bag-of-words and clinical concepts, respectively.
Conclusion: This is the first study to investigate the use of unstructured correspondence files for developing an IDA. The developed models show a very high performance. Despite efforts to mitigate the effect of confounders, limitations may have influenced generalizability. Therefore, external validation of the proposed methods is necessary in future research.

HER2+ breast cancer patients, as observed by oncologist Agnes Jager from Erasmus Medical Centre (EMC), often achieve radiologic complete response (rCR) earlier than expected under standard treatments. To address this, Jager has partnered with Delft University of Technology to develop a computational model aimed at personalizing treatment schedules, potentially reducing chemotherapy cycles and minimizing side effects.

Building on previous MSc theses by Nathalie Oudhof, Eva Slingerland, and Rutger Engelberts, this thesis aims to improve the predictive capability of the Drug-Induced Mechanically Coupled Reaction-Diffusion (DI-MRCD) model and test its performance on a larger dataset consisting of 13 patients. The DI-MRCD model combines dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) data with patient-specific parameters to simulate the reaction of breast cancer tumours on chemotherapy. Key improvements include optimizing and generalizing the pre-processing pipeline for a larger patient cohort and enhancing input reliability by independently computing apparent diffusion coefficients (ADC).

Further refinements to the DI-MRCD model include updates to chemotherapy and shear modulus parameters, switching to a Trust Region Reflective (TRF) optimization method, and introducing a tissue-specific proliferation rate and natural cell death term. Despite these enhancements providing more insight, control, and making the model biologically more realistic, the model struggled to converge, highlighting the challenge of fitting patient-specific parameters with limited data points.

Future improvements could include resolving the convergence issues, incorporating additional calibration parameters, allowing the proliferation rate to travel with tumour cells as they diffuse, incorporating chemotherapy doses into the chemotherapy term, using Bayesian optimization for better parameter estimation, and making the results more explanatory by integrating other patient data.

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.

Introduction Intracranial hypertension (IH) is a harbinger of secondary brain injury in patients suffering from traumatic brain injury (TBI), can be mitigated at the Intensive Care Unit (ICU) and is associated with a poor prognosis. Current clinical practice consists of treating IH once it has occurred, by medical or surgical interventions. This is later than desired, as secondary injury has already been initiated. A pre-emptive approach may be preferable, and seems possible since many physiological variables that may aggravate IH are known and can be managed clinically. The aim of this research is to develop a machine learning method that is able to predict whether or not a patient will develop IH in the near future during ICU stay. Methods A cohort of 114 patients with TBI admitted to the ICU of Erasmus MC was selected. Long Short Term Memory (LSTM) models were trained and evaluated with 26 clinical variables to predict IH. The effect of the length of the minimal IH period, the length of the prediction window and the number of included variables was evaluated. Primary outcome measures were the model loss, accuracy, and Area Under the receiver operating characteristic Curve (AUC). Results We achieved a mean AUC of 0,83 [95% CI: 0,68-0,98] with a model predicting periods of ICP≥20mmHg lasting at least 15 minutes, using a prediction window of 30 minutes and using only the ICP and mean arterial blood pressure (MAP). All models showed decreasing training and validation loss values during the first few epochs of model training. Thereafter, the training loss continued to decrease while the validation loss started to increase. Conclusion We developed a LSTM model that was able to predict, with a mean AUC of 0.83 [95% CI: 0,68-0,98], the occurrence of IH after half an hour based on the ICP and MAP. Adding more clinical variables resulted in overtrained models.