KITE: An Analytics Platform for Multimorbidity Management.

Managing multimorbidity entails processing distributed, dynamic and heterogeneous data using diverse analytics tools. We present KITE, a Cloud-based infrastructure allowing the aggregation and processing of health data using a dynamic set of analytical components. We showcase KITE in the context of the ProACT project, aiming at advancing home-based integrated care though IoT, analytics and a behavior change framework. We validate the viability of the infrastructure through an application of Bayesian networks to give a probabilistic representation of older individuals based on a variety of factors.

Bridging Clinical and Social Determinants of Health Using Unstructured Data.

There is a growing interest in identifying, weighing and accounting for the impact of health determinants that lie outside of the traditional healthcare system, yet there is a remarkable paucity of data and sources to sustain these efforts. Decision support systems would greatly benefit from leveraging models which are able to extend and use such cross-domain knowledge. This paper describes an approach to identify and explore related social and clinical terms based on large corpora of unstructured data. Using word embedding techniques on relevant sources of knowledge, we have identified terms that appear close together in the high-dimensional space. In particular, having created a model with cross-domain knowledge on the social determinants of health, we have been able to demonstrate that it is possible to surface terms in this domain when querying for related clinical terms, thereby creating a bridge between the social and clinical determinants of health. This is a promising approach with significant applicability in decision support efforts in healthcare.

An Analytical Method for Multimorbidity Management Using Bayesian Networks.

This paper describes an application of Bayesian Networks to mo-del persons with multimorbidity using measurements of vital signs and lifestyle assessments. The model was developed as part of a project on the use of wearable and home sensors and tablet applications to help persons with multimorbidity and their carers manage their conditions in daily life. The training data was extracted from TILDA, an open dataset collected from a longitudinal health study of the older Irish population. A categorical BN structure was learnt using a score-based approach, with constraints on the ordering of variables. The prediction accuracy of the model is assessed using the Brier score in a cross-validation experiment. Finally, a user inter-face that allows to set some observed levels and query the resulting margi-nal probabilities from the BN is presented.

TIQS: Targeted Iterative Question Selection for Health Interventions.

While healthcare has traditionally existed within the confines of formal clinical environments, the emergence of population health initiatives has given rise to a new and diverse set of community interventions. As the number of interventions continues to grow, the ability to quickly and accurately identify those most relevant to an individual's specific need has become essential in the care process. However, due to the diverse nature of the interventions, the determination need often requires non-clinical social and behavioral information that must be collected from the individuals themselves. Although survey tools have demonstrated success in the collection of this data, time restrictions and diminishing respondent interest have presented barriers to obtaining up-to-date information on a regular basis. In response, researchers have turned to analytical approaches to optimize surveys and quantify the importance of each question. To date, the majority of these works have approached the task from a univariate standpoint, identifying the next most important question to ask. However, such an approach fails to address the interconnected nature of the health conditions inherently captured by the broader set of survey questions. Utilizing data mining and machine learning methodology, this work demonstrates the value of capturing these relations. We present a novel framework that identifies a variable-length subset of survey questions most relevant in determining the need for a particular health intervention for a given individual. We evaluate the framework using a large national longitudinal dataset centered on aging, demonstrating the ability to identify the questions with the highest impact across a variety of interventions.

Medical Text Classification Using Convolutional Neural Networks.

We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information. Through a detailed evaluation, we demonstrate that our method outperforms several approaches widely used in natural language processing tasks by about 15%.

Using Semantic Technologies to Extract Highlights from Care Notes.

We propose a cognitive system for patient-centric care that leverages and combines natural language processing, semantics, and learning from users over time to support care professionals working with large volumes of patient notes. The proposed methods highlight the entities embedded in the unstructured data to provide a holistic semantic view of an individual. A user-based evaluation is presented, showing consensus between the users and the system.

Service Bundle Recommendation for Person-Centered Care Planning in Cities.

Providing appropriate support for the most vulnerable individuals carries enormous societal significance and economic burden. Yet, finding the right balance between costs, estimated effectiveness and the experience of the care recipient is a daunting task that requires considering vast amount of information. We present a system that helps care teams choose the optimal combination of providers for a set of services. We draw from techniques in Open Data processing, semantic processing, faceted exploration, visual analytics, transportation analytics and multi-objective optimization. We present an implementation of the system using data from New York City and illustrate the feasibility these technologies to guide care workers in care planning.

Enabling Person-Centric Care using linked data technologies.

Patient-Centric Care requires comprehensive visibility into the strengths and vulnerabilities of individuals and populations. The systems involved in Patient-Centric Care are numerous and heterogeneous, span medical, behavioral and social domains and must be coordinated across government and NGO stakeholders in Health Care, Social Care and more. We present a system, based on Linked Data technologies, taking first steps in making this cross-domain information accessible and fit-for-use, using minimal structure and open vocabularies. We evaluate our system through user studies.