Titlebook: Artificial Intelligence in Medicine; 21st International C Jose M. Juarez,Mar Marcos,Allan Tucker Conference proceedings 2023 The Editor(s)

ITCH

Overthrow

Decision Tree Approaches to?Select High Risk Patients for?Lung Cancer Screening Based on?the?UK Primsuch as cough, pain, dyspnoea and anorexia are also present in other diseases. This partly attributes towards the low survival rate. Therefore, it is crucial to screen high risk patients for lung cancer at an early stage through computed tomography (CT) scans. As shown in a previous study, for patie

lanugo

慢跑鞋

Novel Approach for?Phenotyping Based on?Diverse Top-K Subgroup Lists clinicians to understand it. In this paper, we approach this problem by defining the technical task of mining diverse top-k phenotypes and proposing an algorithm called DSLM to solve it. The phenotypes obtained are evaluated according to their quality and predictive capacity in a bacterial infectio

自戀

Patient Event Sequences for?Predicting Hospitalization Length of?Stayations. This paper proposes a novel transformer-based model, termed Medic-BERT (M-BERT), for predicting LOS by modeling patient information as sequences of events. We performed empirical experiments on a cohort of 48. emergency care patients from a large Danish hospital. Experimental results show th

gerrymander

Autoencoder-Based Prediction of ICU Clinical Codesthem in the EHR is tedious, and some clinical codes may be overlooked. Given an incomplete list of clinical codes, we investigate the performance of ML methods on predicting the complete ones, and assess the added predictive value of including other clinical patient data in this task. We used the MI

礦石

Hospital Length of?Stay Prediction Based on?Multi-modal Data Towards Trustworthy Human-AI Collaborat textual radiology reports annotated by humans. Although black-box models predict better on average than interpretable ones, like Cox proportional hazards, they are not inherently understandable. To overcome this trust issue, we introduce time-dependent model explanations into the human-AI decision

Observe

Debate

Federated Learning to?Improve Counterfactual Explanations for?Sepsis Treatment Predictionble artificial intelligence (XAI) improving the interpretability of these models. In turn, this fosters the adoption by medical personnel and improves trustworthiness of CDSSs. Among others, counterfactual explanations prove to be one such XAI technique particularly suitable for the healthcare domai

蠟燭

Explainable AI for?Medical Event Prediction for?Heart Failure Patientsedictions neither understandable nor interpretable by humans. This limitation is especially significant when they contradict clinicians’ expectations based on medical knowledge. This can lead to a lack of trust in the model. In this work, we propose a pipeline to explain AI models. We used a previou