BARD: A Structured Technique for Group Elicitation of Bayesian Networks to Support Analytic Reasoning.

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively.

Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources.

OBJECTIVE: Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast Cancer, Colon Cancer, Secondary Malignant Neoplasm of Respiratory and Digestive Organs, Multiple Myeloma and Malignant Plasma Cell Neoplasms, Pneumonia, and Pulmonary Embolism. We specifically examine the effect of linking multiple data sources on text classification performance. METHODS: Support Vector Machine classifiers are built for eight data source combinations, and evaluated using the metrics of Precision, Recall and F-Score. Sub-sampling techniques are used to address unbalanced datasets of medical records. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient and hospital admission data, in order to assess the research question regarding the impact of the value of multiple data sources. Statistical significance is measured using the Wilcoxon signed-rank test. A second set of experiments explores aspects of the system in greater depth, focusing on Lung Cancer. We explore the impact of feature selection; analyse the learning curve; examine the effect of restricting admissions to only those containing reports from all data sources; and examine the impact of reducing the sub-sampling. These experiments provide better understanding of how to best apply text classification in the context of imbalanced data of variable completeness. RESULTS: Radiology questions plus patient and hospital admission data contribute valuable information for detecting most of the diseases, significantly improving performance when added to radiology reports alone or to the combination of radiology and pathology reports. CONCLUSION: Overall, linking data sources significantly improved classification performance for all the diseases examined. However, there is no single approach that suits all scenarios; the choice of the most effective combination of data sources depends on the specific disease to be classified.

In-home monitoring of older adults with vision impairment: exploring patients', caregivers' and professionals' views.

OBJECTIVE: To develop a conceptual framework for the design of an in-home monitoring system (IMS) based on the requirements of older adults with vision impairment (VI), informal caregivers and eye-care rehabilitation professionals. MATERIALS AND METHODS: Concept mapping, a mixed-methods statistical research tool, was used in the construction of the framework. Overall, 40 participants brainstormed or sorted and rated 83 statements concerning an IMS for older adults with VI. Multidimensional scaling and hierarchical cluster analysis were employed to construct the framework. A questionnaire yielded further insights into the views of a wider sample of older adults with VI (n=78) and caregivers (n=25) regarding IMS. RESULTS: Concept mapping revealed a nine-cluster model of IMS-related aspects including affordability, awareness of system capabilities, simplicity of installation, operation and maintenance, system integrity and reliability, fall detection and safe movement, user customization, user preferences regarding information delivery, and safety alerts for patients and caregivers. From the questionnaire, independence, safety and fall detection were the most commonly reported reasons for older adults and caregivers to accept an IMS. Concerns included cost, privacy, security of the information obtained through monitoring, system accuracy, and ease of use. DISCUSSION: Older adults with VI, caregivers and professionals are receptive to in-home monitoring, mainly for fall detection and safety monitoring, but have concerns that must be addressed when developing an IMS. CONCLUSION: Our study provides a novel conceptual framework for the design of an IMS that will be maximally acceptable and beneficial to our ageing and vision-impaired population.