Computerized Adaptive Testing (CAT) is becoming increasingly popular in the field of diagnosis, as a method of accurately assessing a patient’s symptoms and conditions. The adaptiveness of the test enables it to dynamically adjust the difficulty of the questions based on the patient’s responses, leading to a more precise and efficient diagnostic process.
In traditional diagnostic methods, healthcare professionals use a fixed set of questions to gather information from the patient. This approach can be time-consuming and may lead to irrelevant or repetitive questions, as well as an incomplete understanding of the patient’s symptoms. With CAT, the computer algorithm selects the questions based on the patient’s previous answers, providing a more personalized and efficient diagnostic experience.
CAT algorithms use statistical models, such as item response theory (IRT), to determine the patient’s condition and adjust the difficulty of the questions accordingly. The algorithm continually updates the estimated condition as the patient answers more questions, leading to a more accurate diagnosis. This also allows for a shorter diagnostic process, as the patient is not asked irrelevant or repetitive questions. For example, If the patient states that she cannot walk unaided, the patient is not asked how long she can run.
CAT can be used in a wide range of diagnostic domains, including mental health, infectious diseases, and chronic conditions. The adaptiveness of the test also provides a more equitable diagnostic experience for patients with different symptoms, as the difficulty of the questions is adjusted to their individual symptoms.
However, CAT is not without limitations. Implementing a CAT system requires significant resources, including the development of a large item pool, data analysis, and the creation of algorithms to drive the adaptiveness of the test. The algorithm used in CAT also requires regular updates and validation to ensure that it remains accurate and fair.
CAT is also limited by the number of questions available in the item pool. As the algorithm adapts to the patient’s symptoms, the pool of available questions can become depleted, leading to a loss of precision in the diagnosis. To address this, some CAT systems use computer-generated questions, but this introduces the challenge of ensuring that the questions are of high quality and free from bias.
In summary, CAT is a highly efficient and effective method of diagnosis, providing a personalized and precise assessment of a patient’s symptoms and conditions. Its adaptiveness provides a more equitable diagnostic experience for patients with different symptoms. However, the development and implementation of a CAT system requires significant resources and ongoing validation to ensure accuracy and fairness.
CAT in PROM Bayesian Networks (BNs)
PROM BN consists of two separate groups of discrete variables: the set of questions and the set of underlying conditions that are being measured through the PROM. After these variables are defined the next step is to construct the structure of the PROM BN. This can be done using a combination of expert knowledge and data, or solely one of the two. Bayesian Networks allow for a more thorough modeling of the questionnaire compared to Item Response Theory models, as they permit both direct relationships between latent constructs and question items and the inclusion of non-linear relationships. Therefore a PROM BN can be used for two purposes. Firstly, it can be used to identify the most informative questions. Secondly, it can be used to determine the value of the latent traits. Given the answers that have already been obtained, a BN inference algorithm such as the Junction Tree algorithm can be employed to calculate the probabilities (posteriors) of both the latent traits and the remaining questions. As more questions are answered, the probabilities (posteriors) can be refined.
CAT can be implemented in a BN by using an information metric on the probabilities (posteriors) of the remaining questions in order to determine the most insightful question based on the patient’s previous answers. The question with the greatest average information gain is then asked. In other words, the question that will provide the most relevant information for the latent factor is the one that has the maximum information gain, taking into account the answers to the questions that have already been answered.
See the project outputs for more details on PROM BNs.