Start date: 22-03-2021
End date: 22-09-2021
General practitioners (GPs) work with probabilities of diagnoses to head their diagnostic and therapeutic decisions. To a large extent, this is an implicit process, controlled by prior knowledge and so-called pattern recognition. Less is known about the concreteness and preciseness of the used probabilities. Uncertainty may lead to an overestimation of the probability of a rare disease, and thus lead to overuse of diagnostic facilities, unnecessary costs, and ultimately patient harm. Underestimating probabilities may lead to late diagnoses and detrimental consequences. The process of coming to a diagnosis starts when the patient tells his first complaint. This first utterance of a complaint during the consultation is called reason for encounter (RFE; for example cough, back pain, headache). The RFE itself is related to probabilities of diagnoses.
For common reasons to visit a doctor, context variables influence probabilities of diagnoses. Deepening of our understanding of how diversity, context, multimorbidity and symptoms influence probabilities of final diagnoses will help doctors to work more secure and evidence based. It will lead to the development of a diagnostic support tool to use in everyday practice.
We aim to analyze how GPs can be supported in early diagnosis through AI support. For all patients with new episodes (2010-2020) we want to define the predictive value of the RFE for diagnoses. More specifically we want to study which data influence predictive values. This knowledge is crucial to build ICT tools to support the GP.
The AI challenge is to use machine learning (eg Bayesian network approach and other methods) to calculate probabilities of diagnoses based on the reason for encounter, modified for other personal and context variables, based on the data in our database. For 15 common reasons to visit the GP, we want to develop an algorithm that is able to show a physician realtime for any unique patient the probabilities of diagnoses.
We will use data from the research network FaMeNet (www.famenet.nl) covering over 300.000 patient years, and over 1 million patient contacts. Structured data on contextual factors are available for more than 50% of the adult population. Contextual factors include chronic comorbidity, sex, age, ethnicity, educational level, and all symptoms and diagnoses in the two years preceding the diagnosis.
The data are stored in a data warehouse at the department of Primary and Community care of the Radboud University Nijmegen Medical Center (Radboud Technology Center Health Data).
For the purpose of this research we will only consider the top 15 most common RFEs. That is, we only consider patients that have at least 1 episode starting with one of the top 15 RFEs. Each data point consists of the following patient information: RFE, age, sex, start date and diagnosis. We augment this data by modelling the patient history up to the current RFE and add it as a feature to the input. By doing this we are trying to simulate the way in which general practitioners use a patient's history, combined with current demographics to find the correct diagnosis. Important to note is that it is possible for a patient to appear multiple times in the data, at different moments in time. In those cases there is some overlap in patient history in the data, but the demographics will differ (such as RFE and age).
To be able to model the patient history, we will use the BERT architecture which is based on the Transformer architecture which is widely adopted in NLP applications. This network is already used once in the same domain for a similar problem, where it was renamed BEHRT: Transformer for Electronic Health Records. The method is visualized in the image below.
First off, we need to pre-train BEHRT, which is done by applying Masked Language Modeling (MLM). This approach is pretty straightforward, in that we replace a percentage of the input tokens with '
The next step is to fine-tune pre-trained BEHRT. More specifically, we want to experiment with Mult-Task Learning (MTL) to see if we can achieve better performance than without MTL. Inspired by human learning abilities, the MTL learning paradigm aims to jointly learn multiple related tasks so that the knowledge contained in one task can be leveraged by other tasks, hoping to improve generalization performance of all tasks at hand. In a sense MTL can be seen as a form of data augmentation, where the labeled data in all tasks is aggregated. With more data, MTL should be able to learn more robust representations for all tasks, leading to better knowledge sharing among tasks, better performance of each task and a reduced risk of overfitting. Tasks that are considered at the time of writing are diagnoses prediction as 'main' task, and episode duration and next visit prediction as source tasks.