1,721,021 research outputs found
Extending the Event Calculus with Temporal Granularity and Indeterminacy*
No full textIn many real-world applications, temporal information is often imprecise about the temporal location of events (indeterminacy) and comes at different granularities (Dyreson and Snodgrass 1995). Temporal granularity and indeterminacy are thus emerging as crucial requirements for the advancement of intelligent information systems which have to store, manage, and reason about temporal data. Consider, for example, these events taken from the application-a temporal database for cardiological patients- we are considering in our research (Combi and Chittaro 1999): '‘between 2 p.m. and 4 p.m. on May 5, 1996, the patient suffered from a myocardial infarction’', '‘he started the therapy with thrombolytics in July 1995'', '‘on October 12, 1996, he had a follow-up visit’'. The three events happened at the hours, months, and days timelines, respectively
Arcadia. Un sistema per la gestione di dati ed immagini di PTCA realizzato con un DBMS orientato agli oggetti
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Clinical Information Systems and Artificial Intelligence: Recent Research Trends
Full text linkThis survey aims at reviewing the literature related to Clinical Information Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) systems, and how collected data can be analyzed by Artificial Intelligence (AI) techniques
Improving the management of PTCA data and images by OODBMS
No full textUse of rotational models play an important role in implementing time-oriented medical records (TOMR) management systems. The authors describe the first steps in implementing TOMR with object-oriented methods and instruments. They focus attention on needs of data and image integration in a haemodynamic lab where PTCA (percutaneous transluminal coronary artery angioplasty) patients are dealt with. The features of object-oriented database management systems are discussed. The design considerations and system description are outlined
A Key Performance Indicator to Analyze Swarm Learning Performances with EHR
Full text linkSwarm Learning (SL) has been recently proposed for distributed learning, where a group of individual centers perform a synchronized training. Unlike traditional machine learning models that rely on a central server, swarm learning distributes the learning process across multiple nodes. Each node independently processes data and contributes to the overall learning task. This collaboration allows the swarm to benefit from individual nodes' different data. Unlike federated learning, here model parameters are not handled by a central server but are randomly handled across each individual node. The intrinsic attention of swarm learning to data privacy makes it suitable for distributed health care analysis, where a clinical center wants to benefit from all the other ones in the swarm network. However, the benefit for a single center or for the whole network could vary depending on data distribution. In this paper, we want to analyze the performance of the swarm learning in a network with multiple nodes, where different data distribution scenarios are taken into account. This analysis will show the gain of the whole swarm network and a specific (reference) node, focusing on scenarios where this node has a different amount of data with respect to the other nodes. To perform a more analytical analysis, we introduce a new Key Performance Indicator (KPI) to measure such gain. We then applied this method using I CU data extracted from the MIMIC EHR database and discussed the results obtained by analyzing 5 nodes with different data distribution scenarios
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