Similarity measuring between patient traces for clinical pathway analysis

Clinical pathways leave traces, described as activity sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further as a basis for providing insights into the pathways, and complementing existing techniques of clinical pathway analysis, which mainly focus on looking at aggregated data seen from an external perspective. In this paper, a probabilistic graphical model, i.e., Latent Dirichlet Allocation, is employed to discover latent treatment behaviors of patient traces for clinical pathways such that similarities of pairwise patient traces can be measured based on their underlying behavioral topical features. The presented method, as a basis for further tasks in clinical pathway analysis, are evaluated via a real-world data-set collected from a Chinese hospital

Similarity measuring between patient traces for clinical pathway analysis

Clinical pathways leave traces, described as activity sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further as a basis for providing insights into the pathways, and complementing existing techniques of clinical pathway analysis, which mainly focus on looking at aggregated data seen from an external perspective. In this paper, a probabilistic graphical model, i.e., Latent Dirichlet Allocation, is employed to discover latent treatment behaviors of patient traces for clinical pathways such that similarities of pairwise patient traces can be measured based on their underlying behavioral topical features. The presented method, as a basis for further tasks in clinical pathway analysis, are evaluated via a real-world data-set collected from a Chinese hospital

Processing of Electronic Health Records using Deep Learning: A review

Availability of large amount of clinical data is opening up new research avenues in a number of fields. An exciting field in this respect is healthcare, where secondary use of healthcare data is beginning to revolutionize healthcare. Except for availability of Big Data, both medical data from healthcare institutions (such as EMR data) and data generated from health and wellbeing devices (such as personal trackers), a significant contribution to this trend is also being made by recent advances on machine learning, specifically deep learning algorithms

Thoughts about disordered thinking: measuring and quantifying the laws of order and disorder

Peer ReviewedPostprint (author's final draft

Predicting Illness and Type of Treatment from Digital Health Records

Kasvavad kulud tervishoius ning samaaegne töötava populatsiooni kahanemine on kriitliine probleem kõikjal arenenud maailmas. Ühest küljest on paratamatu, et uued ravimid ja meetodid on kallid, teisest küljest on võimalik vähendada välditavaid kulutusi parema plaanimise ja ennetustööga. Enamik haiglad salvestavad digitaalselt kõik, mis patsiendiga ravi jooksul toimub ja Eestis esitatakse kõik raviarved ka Eesti Haigekassale (HK) hüvitamiseks. Käesolevas töös kasutatakse HK andmeid ehitamaks mudelit, mille abil on võimalik tuletada erinevad raviprotsessid, mida patsientide ravimisel kasutatakse ning samuti ka ennustada patsientide hulka, kes tulevikus vastavat ravi vajavad. Selline mudel võiks olla kasulik suunamaks otsuseid vahendite jaotamisel ja ennetustöö suunamisel.The rising costs of healthcare and decreasing size of the working population is a dire problem in most of the developed world. While it is inevitable that new methods are costly, it is possible to reduce avoidable expenses by better planning and prevention. Most hospitals keep digital records of everything that happens to a patient during their treatment and in Estonia all medical bills are also presented to the National Health Insurance Fund (NHIF) for reimbursement. In this work the data from NHIF is used to build a model that as the first step uncovers the different clinical pathways followed for the treatment of patients with an illness. As a second step the model is used to predict the number of patients that will be provided the uncovered treatments in the future. The output of such a model could be a valuable asset for planning resource allocation and preventative health care

Construction of Empirical Care Pathways Process Models from Multiple Real-World Datasets

Care pathways (CPWs) are "multidisciplinary care plans that detail essential care steps for patients with specific clinical problems." While CPWs impact on health or cost outcomes is vastly studied, an in-depth analysis of the real-world implementation of the CPWs is an area that still remains underexplored. The present work describes how to apply an existing process mining methodology to construct the empirical CPW process models. These process models are a unique piece of information for health services research: for example to evaluate their conformance against the theoretical CPW described on clinical guidelines or to evaluate the impact of the process in health outcomes. To this purpose, this work relies on the design and implementation of a solution that a) synthesizes the expert knowledge on how health care is delivered within and across providers as an activity log, and b) constructs the CPW process model from that activity log using process mining techniques. Unlike previous research based on ad hoc data captures, current approach is built on the linkage of various heterogeneous real-world data (RWD) sets that share a minimum semantic linkage. RWD, defined as secondary use of routinely collected data as opposite to ad hoc data extractions, is a unique source of information for the CPW analysis due to its coverage of the caregiving activities and its wide availability. The viability of the solution is demonstrated by constructing the CPW process model of Code Stroke (Acute Stroke CPW) in the Aragon region (Spain)

Incorporating comorbidities into latent treatment pattern mining for clinical pathways

AbstractIn healthcare organizational settings, the design of a clinical pathway (CP) is challenging since patients following a particular pathway may have not only one single first-diagnosis but also several typical comorbidities, and thus it requires different disciplines involved to put together their partial knowledge about the overall pathway. Although many data mining techniques have been proposed to discover latent treatment information for CP analysis and reconstruction from a large volume of clinical data, they are specific to extract nontrivial information about the therapy and treatment of the first-diagnosis. The influence of comorbidities on adopting essential treatments is crucial for a pathway but has seldom been explored. This study proposes to extract latent treatment patterns that characterize essential treatments for both first-diagnosis and typical comorbidities from the execution data of a pathway. In particular, we propose a generative statistical model to extract underlying treatment patterns, unveil the latent associations between diagnosis labels (including both first-diagnosis and comorbidities) and treatments, and compute the contribution of comorbidities in these patterns. The proposed model extends latent Dirichlet allocation with an additional layer for diagnosis modeling. It first generates a set of latent treatment patterns from diagnosis labels, followed by sampling treatments from each pattern. We verify the effectiveness of the proposed model on a real clinical dataset containing 12,120 patient traces, which pertain to the unstable angina CP. Three treatment patterns are discovered from data, indicating latent correlations between comorbidities and treatments in the pathway. In addition, a possible medical application in terms of treatment recommendation is provided to illustrate the potential of the proposed model. Experimental results indicate that our approach can discover not only meaningful latent treatment patterns exhibiting comorbidity focus, but also implicit changes of treatments of first-diagnosis due to the incorporation of typical comorbidities potentially

Magnetic resonance imaging of the brain and vocal tract:Applications to the study of speech production and language learning

The human vocal system is highly plastic, allowing for the flexible expression of language, mood and intentions. However, this plasticity is not stable throughout the life span, and it is well documented that adult learners encounter greater difficulty than children in acquiring the sounds of foreign languages. Researchers have used magnetic resonance imaging (MRI) to interrogate the neural substrates of vocal imitation and learning, and the correlates of individual differences in phonetic “talent”. In parallel, a growing body of work using MR technology to directly image the vocal tract in real time during speech has offered primarily descriptive accounts of phonetic variation within and across languages. In this paper, we review the contribution of neural MRI to our understanding of vocal learning, and give an overview of vocal tract imaging and its potential to inform the field. We propose methods by which our understanding of speech production and learning could be advanced through the combined measurement of articulation and brain activity using MRI – specifically, we describe a novel paradigm, developed in our laboratory, that uses both MRI techniques to for the first time map directly between neural, articulatory and acoustic data in the investigation of vocalisation. This non-invasive, multimodal imaging method could be used to track central and peripheral correlates of spoken language learning, and speech recovery in clinical settings, as well as provide insights into potential sites for targeted neural interventions

Theories of signs define a novel approach to the analysis of clinical biomarkers of inflammation and oxidative stress

Biomarkers are widely used not only as prognostic or diagnostic indicators, or as surrogate markers of disease in clinical trials, but also to formulate theories of pathogenesis. We identify two problems in the use of biomarkers in mechanistic studies. The first problem arises in the case of multifactorial diseases, where different combinations of multiple causes result in patient heterogeneity. The second problem arises when a pathogenic mediator is difficult to measure. This is the case of the oxidative stress (OS) theory of disease where the causal components are reactive oxygen species (ROS) that have very short half-lives. In this case, it is usual measure the traces left by the reaction of ROS with biological molecules, rather than the ROS themselves. Borrowing from the philosophical theories of signs, we look at the different facets of biomarkers and discuss their different value and meaning in multifactorial diseases and system medicine, to inform their use in patient stratification in personalized medicine