CAT/2: Big Data Content Analytics with particular reference to Probabilistic Neural Topic Models

公共和私营部门都迫切需要一种新的、具有成本效益的方法，通过这种方法可以将非结构化数据呈现给专业用户。特别是，随着时间的推移，更迅速、更容易地从业务线（LOB）数据系统的“自由文本框”（FTB）内容中获得意义。我们还迫切需要更好地获得医疗、社会护理和教育等领域的“联合”相关和预测情报（例如，风险儿童的早期预警信号、人口老龄化的风险评估、痴呆症和精神健康等长期衰弱的早期发作风险）。问题是关键的“案例说明”信息隐藏在ftb（非结构化数据）的内容中，而不是隐藏在LOB系统的结构化数据输入中并保存在LOB系统中。该项目将从FTB收集中揭示隐藏的含义，并提供预测分析和创新的用户体验，以更好地支持基于证据的理解和决策。因此，我们将使目前在医院PAS的临床医生笔记、执业系统中的全科医生笔记、委员会护理管理系统中的成人和儿童笔记、病例管理系统中的法律保证金笔记、投诉系统中的治理问题、客户关系管理系统中的客户问题等中基本上看不见的信息及其相对重要性变得更有意义和更易理解。这将大大提高定性和定量信息学和商业智能可用于稀缺资源风险规划，服务交付和决策的组织。

There is significant need across the public & private sectors for a new, cost effective means by which unstructured data can be presented to professional users. Specifically, to more instantly and easily derive meaning over time from within the ‘Free Text Box’ (FTB) content of Line of Business (LOB) data systems. There is also a critical need for better access to ‘joined-up’ relevant & predictive intelligence across domains such as the medical, social care and education divide (e.g. early warning signs for children at risk, risk evaluation of our ageing population, early onset risk of long term debilitations like dementia & mental health). The problem is that the critical ‘case notes’ information is hidden within the the content of FTBs (unstructured data) rather than in the structured data input to and held within LOB systems. This project will unlock the hidden meaning from within FTB collections and provide predictive analyses and innovative user experiences to better support evidence-based understanding and decisions. So we will make sense and more digestible the information and its relative importance that is currently largely invisible within Clinician Notes in a Hospital PAS, GP Notes in their Practice System, Adult and Child Notes in the Council’s Care Management System, Legal Margin Notes in Case Management Systems, Governance Issues within Complaints Systems, Customer Issues within CRM systems and so on. This will greatly improve the qualitative & quantitative Informatics & Business Intelligence available to an organisation for scarce-resource risk planning, service delivery and decision-making.