The first is a longitudinal SP600125 cost report, which is intended to provide a quick historical overview of the patient’s illness, whilst preserving the main events (such as diagnoses, investigations and interventions). It presents the events in the patient’s history ordered chronologically and grouped according to type. In this type of report, events are fully described (i.e., an event description includes all the attributes of the event) and aggregation is minimal (events with common attributes are aggregated, but there is no aggregation through generalisation, for example). The second type of report focusses on a given type of event in a patient’s history, such as the history of diagnoses,

interventions, investigations or drug prescription. This allows us to provide a range of reports that are presented from different perspectives. Under this category fall user-defined reports as well, where the user selects classes of interesting

events (e.g., Investigations of type CT scan and Interventions of type surgery). The system design of the Report Generator follows a classical NLG pipeline architecture, with a Content Selector, MicroPlanner and Syntactic Realiser [24]. These roughly correspond to deciding what to say, how to say it and then actually saying it. The MicroPlanner is tightly coupled with the Content Selector, since part of the document structure is already decided in the event selection phase. Aggregation selleck chemicals llc is mostly conceptual rather than syntactic, therefore it is performed in the content planning stage as well. Deciding what

to say: Starting from a knowledge base (the Chronicle) and the user’s instructions (patient ID, time period, focus, etc.), before the Content Selection module typically retrieves a semantic graph comprising a spine of focussed events elaborated by related events, as shown in Fig. 1. The events will have internal structure not shown in this diagram (e.g., the locus of the cancer and biopsy, the content of the transfusion, the dates of the biopsy and transfusion), represented formally as features on the event objects. The content selection takes into account the type and extent of the summary requested. For example, if a summary of the diagnosis is requested, the system will extract from the Chronicle only those events of type diagnostic (creating what we call the spine of a summary) and the events connected to events of type diagnostic up to a depth level indicated by the size of the summary (see Fig. 2). A depth of 0 will only list instance of diagnosis, a depth of 1 will also extract, for example, the consequence of a diagnosis (e.g., surgery), but no further events related to the surgery. The events extracted by this process will form the content of the summary (“what to say”). Deciding how to say it: Starting from a spine-based semantic graph, a sequence of paragraphs is planned — usually, one for each event on the spine (along with the events elaborating it).