The Impact of an Enterprise Electronic Medical Record (EEMR) Model vs a Clinical Information System (CIS) Model on Usability, Efficiency, and Adaptability.

This paper describes a methodology that engages the clinical community into the design process of creating Clinical Information Systems (CISs) under a Clinical Team-Led Design (CTLD) approach in the context of using Immediately Adaptable (IA) system development technology. The methodology is contrasted against the Enterprise Electronic Medical Record (EEMR) model for usability, efficiency, and adaptability. The methodology was tested in a Breast Cancer setting where the CIS went through 4 rapid agile stages.

Complex analyses on clinical information systems using restricted natural language querying to resolve time-event dependencies.

Purpose: This paper reports on a generic framework to provide clinicians with the ability to conduct complex analyses on elaborate research topics using cascaded queries to resolve internal time-event dependencies in the research questions, as an extension to the proposed Clinical Data Analytics Language (CliniDAL).

Methods: A cascaded query model is proposed to resolve internal time-event dependencies in the queries which can have up to five levels of criteria starting with a query to define subjects to be admitted into a study, followed by a query to define the time span of the experiment. Three more cascaded queries can be required to define control groups, control variables and output variables which all together simulate a real scientific experiment.

Restricted natural language based querying of clinical databases.

Purpose: To elevate the level of care to the community it is essential to provide usable tools for healthcare professionals to extract knowledge from clinical data. In this paper a generic translation algorithm is proposed to translate a restricted natural language query (RNLQ) to a standard query language like SQL (Structured Query Language).

Methods: A special purpose clinical data analytics language (CliniDAL) has been introduced which provides scheme of six classes of clinical questioning templates.

Supervised machine learning and active learning in classification of radiology reports.

Objective: This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry.

Materials And Methods: In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance.

Mapping query terms to data and schema using content based similarity search in clinical information systems.

This paper reports on the issues in mapping the terms of a query to the field names of the schema of an Entity Relationship (ER) model or to the data part of the Entity Attribute Value (EAV) model using similarity based Top-K algorithm in clinical information system together with an extension of EAV mapping for medication names. In addition, the details of the mapping algorithm and the required pre-processing including NLP (Natural Language Processing) tasks to prepare resources for mapping are explained. The experimental results on an example clinical information system demonstrate more than 84 per cent of accuracy in mapping.

A temporal model for Clinical Data Analytics language.

The proposal of a special purpose language for Clinical Data Analytics (CliniDAL) is presented along with a general model for expressing temporal events in the language. The temporal dimension of clinical data needs to be addressed from at least five different points of view. Firstly, how to attach the knowledge of time based constraints to queries; secondly, how to mine temporal data in different CISs with various data models; thirdly, how to deal with both relative time and absolute time in the query language; fourthly, how to tackle internal time-event dependencies in queries, and finally, how to manage historical time events preserved in the patient's narrative.

A knowledge discovery and reuse pipeline for information extraction in clinical notes.

Objective: Information extraction and classification of clinical data are current challenges in natural language processing. This paper presents a cascaded method to deal with three different extractions and classifications in clinical data: concept annotation, assertion classification and relation classification.

Materials And Methods: A pipeline system was developed for clinical natural language processing that includes a proofreading process, with gold-standard reflexive validation and correction.