A multistage method with mixed computational strategies using a combination of rule-based classifiers and statistical classifiers seems to provide a near-optimal strategy for automated extraction of medication information from clinical records.

Family function was assessed in 102 families (342 members) of palliative care patients and grouped into classes by a computer‐based taxonomic program. Five classes were defined through the dimensions of cohesiveness, conflict and expressiveness of the Family Environment Scale (FES). One third of families we named supportive for their high cohesiveness; a further 21% resolved conflict effectively; both of these classes contained low psychological morbidity. Two classes (15%) were clearly dysfunctional: hostile families (6%) were distinguished by high conflict while sullen families (9%) displayed moderate conflict, poor cohesion and limited expressiveness. These two classes had significantly higher levels of psychological morbidity and poorer social functioning. The remaining class (31%) had intermediate levels of cohesion, expressiveness and conflict (termed ordinary) yet more moderate psychosocial morbidity. Screening of families with the FES would facilitate a more family‐centred approach to treatment, with relatively early identification of families at‐risk; preventive interventions would also then be feasible.

BackgroundData, particularly ‘big’ data are increasingly being used for research in health. Using data from electronic medical records optimally requires coded data, but not all systems produce coded data.ObjectiveTo design a suitable, accurate method for converting large volumes of narrative diagnoses from Australian general practice records to codify them into SNOMED-CT-AU. Such codification will make them clinically useful for aggregation for population health and research purposes.MethodThe developed method consisted of using natural language processing to automatically code the texts, followed by a manual process to correct codes and subsequent natural language processing re-computation. These steps were repeated for four iterations until 95% of the records were coded. The coded data were then aggregated into classes considered to be useful for population health analytics.ResultsCoding the data effectively covered 95% of the corpus. Problems with the use of SNOMED CT-AU were identified and protocols for creating consistent coding were created. These protocols can be used to guide further development of SNOMED CT-AU (SCT). The coded values will be immensely useful for the development of population health analytics for Australia, and the lessons learnt applicable elsewhere.

In this paper, a comprehensive study on clinical questions has been completed. A major outcome of this work is the multilayer classification model. It serves as a major component of a patient records based clinical question and answering system as our studies continue. As well, the question collections can be reused by the research community to improve the efficiency of their own question and answering systems.

A complete pipeline system for constructing language processing models that can be used to process multiple practical detection tasks of language structures of clinical records is presented.

Diagnosis autocoding is intended to both improve the productivity of clinical coders and the accuracy of the coding. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters for setting up a convolutional neural network for autocoding with comparable results to that of conventional methods.

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Background: A great challenge in sharing data across information systems in general practice is the lack of interoperability between different terminologies or coding schema used in the information systems. Mapping of medical vocabularies to a standardised terminology is needed to solve data interoperability problems.