The classification of the care pathway is able to define the clinical evolution of patients during hospitalization. It can also find the departments that are more stressed in terms of number of events or patients with...
The classification of the care pathway is able to define the clinical evolution of patients during hospitalization. It can also find the departments that are more stressed in terms of number of events or patients with complex clinical pictures. By analysing these aspects, it is possible to suggest improvements to the healthcare setting, making the health service provided more efficient. By applying the techniques of Process Mining it is possible to model the evolution of the complexity of inpatient management. In this paper, we present an application of some techniques of Process Mining starting from the data collected at Policlinico Universitario Campus Bio-Medico di Roma composed of anonymized records of patients in the period between 01/01/2016 and 31/12/2017. The patients and thus their hospitalization are described through a patient status grid, according to their level of autonomy, cognitive stability and clinical stability. This measure is used as an indirect measure of the complexity of inpatient management. The data were made compliant with the typical structure of an event log, and then the complexity of care of patients admitted to the facility was modelled, analyzing even the most stressed departments. The proposed approach suggests important information for the healthcare setting, ensuring an improvement of the services provided.
Non-small cell lung cancer (NSCLC) remains a major global health challenge, with high post-surgical recurrence rates underscoring the need for accurate pathological response predictions to guide personalized treatment...
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Background: Biomedical natural language processing (NLP) increasingly relies on large language models and extensive datasets, presenting significant computational challenges. Methods: We propose Blue5, a multi-task mo...
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We present algorithms to discriminate good quality PPG signals, i.e., free of artefacts and with suitable morphologies, which is fundamental for a correct medical diagnosis. We have investigated two different approach...
We present algorithms to discriminate good quality PPG signals, i.e., free of artefacts and with suitable morphologies, which is fundamental for a correct medical diagnosis. We have investigated two different approaches of unsupervised and supervised learning. The first method is a Self Organizing Map (SOM), trained on entropic and morphological features extracted by BUT PPG signal windows. We then add three new features related to signal quality, namely the Kurtosis Index, Skewness Index and Signal to Noise Ratio (SNR), that we have shown to improve the performances. For the second approach, we have implemented a Multi Layer Perceptron (MLP) neural network. We have compared the results obtained with those in the literature showing superior performance, especially with the MLP approach, achieving an $F_{1} -$score of 96.15%.
The widespread adoption of electronic health records (EHRs) offers a valuable opportunity to support clinical research by containing crucial patient information, including diagnoses, symptoms, medications, lab tests, ...
The widespread adoption of electronic health records (EHRs) offers a valuable opportunity to support clinical research by containing crucial patient information, including diagnoses, symptoms, medications, lab tests, and more. Despite the success of deep learning for biomedical Named Entity Recognition (NER), the literature in this field still presents a gap regarding applications focused on lung cancer for the Italian language. Hence, this paper presents a transformer-based approach to extract named entities from Italian clinical notes related to Non-Small Cell Lung Cancer (NSCLC). We introduce a novel set of 25 clinical entities related to NSCLC building a corpus annotated for NER. We apply a state-of the-art model pre-trained on Italian biomedical texts to the manually annotated clinical reports of a cohort of 257 patients suffering from NSCLC, successfully dealing with class-imbalance problems and obtaining promising performance (average F1-score of 84.3%). We also compared our method with two other pre-trained state-of-the-art models showing that the domain specific knowledge offered by the proposed approach is necessary to achieve higher performance. These findings also showcase the feasibility of using transformers to extract biomedical information in the Italian language.
Artificial Intelligence is revolutionizing medical practice, enhancing diagnostic accuracy and healthcare delivery. However, its adaptation in medical settings still faces significant challenges, related to data avail...
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Chronic Low Back Pain (LBP) is one of the most prevalent musculoskeletal conditions and is the leading cause of disability worldwide. The morphology and composition of lumbar paraspinal muscles, in terms of infiltrate...
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Recently, there has been increased interest in ma-chine learning explainability. Understanding the complex relationship between input features of a model and their respective outputs is of increased relevance, especia...
Recently, there has been increased interest in ma-chine learning explainability. Understanding the complex relationship between input features of a model and their respective outputs is of increased relevance, especially in biological science. In this paper, we introduce Optimal Brain Dissection (OBD), an innovative methodology designed to examine the importance of first-layer connections in a biology-inspired autoencoder. We incorporated regulator-target interactions within the first autoencoder layer, representing biological regulatory networks, and identified their importance to the reconstruction error, a critical aspect in navigating the complexity of high-dimensional omics data. Through a combination of pruning techniques and counterfactual reasoning, OBD offers a method to quantify feature importance, factoring in both weight magnitude and time-to-laziness. To implement this method, we propose a Dense Autoencoder (DAE) architecture, aiming for increased efficiency and reduced computation. Tailored for omics data, the DAE employs skip concatenations and circumvents non-existent target-target interactions. Our approach aims to understand the relative importance of connections for autoencoder performance, a critical step towards better counter-factual reasoning for neural networks.
Brain Imaging Data Structure (BIDS) provides a valuable tool to organise brain imaging data into a clear and easy standard directory structure. Moreover, BIDS is widely supported by the scientific community and has be...
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Generative models have revolutionized Artificial Intelligence (AI), particularly in multimodal applications. However, adapting these models to the medical domain poses unique challenges due to the complexity of medica...
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