# Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
## 4 Discussion
However, this can be a delicate exercise in dealing with hospital procedures and is well beyond the scope of ML automation, where one expects to fast-automate aspects of work which will motivate interdisciplinary teams (medical experts, data scientists, and hospital management) to look into the matter of further investigating the findings and not simply to adopt them as such. For example, in our analysis, one might consider examining whether the final disposition variable actually has a valid value, to possibly infer something about the validity of the medical diagnosis, by checking how many cases refer to a patient returning to the ED within 30 days and whether he or she had the same symptoms as before; however, this analysis is not possible without resorting to some Natural Language Processing technology, since the _chiefcomplaint_ variable from the _'triage'_ table is a string-type variable which includes a free text description for the symptoms, and this hardly amounts to AutoML at the present state of the practice. These findings demonstrate the potential of using triage data and automated ML techniques, such as H2 OAutoML, to aid in hospital admission prediction. However, significant challenges exist. The complexity and heterogeneity of medical data, as well as the critical need for interpretability and reliability in this domain, can make the application of AutoML challenging. Additionally, privacy and security concerns around the use of sensitive medical data in machine-learning models are another significant issue that must be addressed [26]. The choice of admitting an emergency department patient to the hospital or releasing him/her with medical advice is a non-trivial, multifactorial challenge. In this work, several models have been trained to predict the decision based on limited attributes. Real-life data enclose human biases and hidden secondary factors that affect doctors' decisions. For example, according to the defined contribution health insurance options, a patient insurance capacity could plausibly affect, at a certain point, the institution's capability of providing services. Apparently, due to privacy issues, this sensitive information is not available for analysis to train a decision-support system such as the proposed one. However, this factor, along with several other hidden parameters (medical staff's fatigue, the seriousness of the patient's condition relative to other patients, the level of doctor'sexperience, the doctor's ability to stay focused in high-pressure circumstances, etc.) are implicitly embedded in the black box of machine learning. Therefore, the proposed analysis does not provide an optimal decision for the doctors to rely on rather than a second opinion based on previous decisions of the average colleague. In discussing the ethical use of AI in healthcare, we stress the importance of transparency and accountability. The integration of XAI techniques serves not only to enhance trust among end-users but also to uphold our ethical obligation to provide clear, understandable, and justifiable predictions. By ensuring that our AutoML-based predictions are explainable, we aim to contribute positively to clinical outcomes while respecting the crucial human elements of medical practice. Despite its achievements, our study acknowledges several limitations that future research should address:  
1. Data Preprocessing and Model Applicability: The exclusion of extremely atypical cases during data preprocessing may restrict the model's applicability across diverse clinical scenarios. These cases, though rare, represent complex challenges that could benefit from predictive modeling. Future iterations will aim to expand the model's scope to incorporate a wider spectrum of cases and explore advanced modeling techniques to handle the greater variability in patient presentations without sacrificing accuracy. 2.