Diseño e implementación de un módulo para la estructuración de notas clínicas

El presente Trabajo Fin de Grado (TFG) surge de la necesidad de disponer de tecnologías que faciliten el Procesamiento de Lenguaje Natural (NLP) en español dentro del sector de la medicina. Centrado concretamente en la extracción de conocimiento de las historias clínicas electrónicas (HCE), que recogen toda la información relacionada con la salud del paciente y en particular, de los documentos recogidos en dichas historias, pretende la obtención de todos los términos relacionados con la medicina. El Procesamiento de Lenguaje Natural permite la obtención de datos estructurados a partir de información no estructurada. Estas técnicas permiten un análisis de texto que genera etiquetas aportando significado semántico a las palabras para la manipulación de información. A partir de la investigación realizada del estado del arte en NLP y de las tecnologías existentes para otras lenguas, se propone como solución un módulo de anotación de términos médicos extraídos de documentos clínicos. Como términos médicos se han considerado síntomas, enfermedades, partes del cuerpo o tratamientos obtenidos de UMLS, una ontología categorizada que agrega distintas fuentes de datos médicos. Se ha realizado el diseño y la implementación del módulo así como el análisis de los resultados obtenidos realizando una evaluación con treinta y dos documentos que contenían 1372 menciones de terminología médica y que han dado un resultado medio de Precisión: 70,4%, Recall: 36,2%, Accuracy: 31,4% y F-Measure: 47,2%.---ABSTRACT---This Final Thesis arises from the need for technologies that facilitate the Natural Language Processing (NLP) in Spanish in the medical sector. Specifically it is focused on extracting knowledge from Electronic Health Records (EHR), which contain all the information related to the patient's health and, in particular, it expects to obtain all the terms related to medicine from the documents contained in these records. Natural Language Processing allows us to obtain structured information from unstructured data. These techniques enable analysis of text generating labels providing semantic meaning to words for handling information. From the investigation of the state of the art in NLP and existing technologies in other languages, an annotation module of medical terms extracted from clinical documents is proposed as a solution. Symptoms, diseases, body parts or treatments are considered part of the medical terms contained in UMLS ontology which is categorized joining different sources of medical data. This project has completed the design and implementation of a module and the analysis of the results have been obtained. Thirty two documents which contain 1372 mentions of medical terminology have been evaluated and the average results obtained are: Precision: 70.4% Recall: 36.2% Accuracy: 31.4% and F-Measure: 47.2%.

Is routine hospital episode data sufficient for identifying individuals with chronic kidney disease? : A comparison study with laboratory data

© The Author(s) 2014. Acknowledgements We thank the Information Services Division, Scotland, who provided the SMR01 data, and NHS Grampian, who provided the biochemistry data. We also thank the University of Aberdeen’s Data Management Team. Funding This work was supported by the Chief Scientists Office for Scotland (grant no. CZH/4/656).

Observations Medical & Chirurgical by John Perkins, 1724-1774 (inclusive)

Volume kept by Dr. John Perkins (1698-1781) from 1724 to 1774 recording observations on various diseases and medical conditions illustrated with cases from Perkins's practice in Boston, Massachusetts. The cases ranged from epileptic fits, various fevers, and rheumatism to melancholy. His treament methods were standard for the era, mainly prescribing vomits, purges, and spirits, and bleeding patients. Also includes a section listing contradictory opinions among prominent medical writers such as Dutch physician Herman Boerhaave and English physician Thomas Sydenham. An index is located at the end of the volume. Perkins likely began compiling the book in 1765. It contains cases dating from 1724 to 1774.

Architecture of a community medical history data base /

"UIUCDCS-R-73-589"

The medical department of the United States Army in the world war.

vol. XII. Pathology of the acute respiratory diseases, and of gas gangrene following war wounds, by G.R. Callender and J.F. Coupal. 1929- vol. XIII. pt. 1. Physical reconstruction and vocational education, by A.G. Crane. pt. 2. The Army nurse corps, by Julia C. Stimson. 1927- vol. XIV. Medical aspects of gas warfare, by W.D. Bancroft, H.C. Bradley [and others] 1926.- vol. XV. Statistics, pt. 1. Army anthropology, based on observations made on draft recruits, 1917-1918, and on veterans at demobilization, 1919, by C.B. Davenport and A.G. Love. 1921. pt. 2. Medical and casualty statistics based on the medical records of the United States Army, April 1, 1917, to December 31, 1919, inclusive, by A.G. Love. 1925.

Telemedical support for Iraq

The Swinfen Charitable Trust (SCT) provided two kinds of telemedical support to Iraq during 2004. Starting in January 2004, the Al-Yarmouk Teaching Hospital in Baghdad was able to refer cases into the well established global e-health network that the SCT has operated for the last five years. (In the first quarter of 2004, the SCT dealt with a total of 57 referrals from 15 hospitals in eight countries.) Two cases were referred from Baghdad in March 2004, both gynaecological, which were dealt with by consultants from the UK and Australia. The SCT administrators visited Basrah during April 2004 and met Iraqi doctors at the Shaibah Hospital as part of the international initiatives to improve health care there. Following this visit, the SCT network expanded to include another four hospitals in Iraq (Table 1). In addition, the SCT provided an electronic health records (EHR) system to support the rebuilding of maternity services, which has been led by the British Royal Colleges. The maternity records system is a Web-based EHR system, running on a secure server, which allows integrated access from antenatal clinics, from hospitals and from postnatal clinics in Iraq. Patients can view their own notes, thus promoting ownership of medical information, and doctors can view the notes of their own patients, from any Internet-connected PC. No special software is required by the user.

Observations in a maternity ward: Usability considerations for EHRs in an interrupt driven environment

With the increasing demand on healthcare systems it is imperative that all care is provided as efficiently and effectively as possible. Technology within the medical domain offers an exciting opportunity to augment work practices in order to meet these needs. This research project explores the implications of the interrupt-driven nature of work in clinical situations on documentation within an environment that increasingly involves electronic health records (EHRs). Midwives in a busy maternity ward were observed and interviewed about the work practices they employed to document information associated with patient care. The results showed that the interrupt-driven nature of the workplace, a feature common to many healthcare settings, led to a tension between the work and the work to document the work. Further, the IT environment in which the information was collected was not designed to cater for frequent interruption of the data entry process. Several recommendations for improving the IT environment are proposed to support health professionals in documenting patient data whilst attending to the interruptions. The recommendations include timeout screens, push technology, use of handheld PDAs, and cues to augment documentation in an interrupted session. Copyright © 2008 RMIT Publishing

Support for paediatric prescribers:what is discussed at hospital board level?

Prescribing support tools range from traditional printed texts to state-of-the-art computerised decision support systems. Comparison between available literature is difficult due to country-specific resources often being the focus of the research. In the UK, it is widely accepted that hospitals take their own individualised approaches to reducing prescribing errors. Objective - This study focused on specialist paediatric hospitals. It aimed to identify the localised approaches taken by paediatric hospitals to reduce prescribing errors. Method - Applied thematic analysis was used to explore the publically published board meeting minutes from the four specialist stand-alone paediatric hospitals in England. Three years of data was collected from each hospital. Codes were collected into groups to identify themes from the data. Results - The main themes identified were clinician involvement in prescribing support is important; credit card-sized reminder tools are used to provide prescribing guidance; electronic prescribing is considered important for reducing prescribing errors; feedback from clinical pharmacists on prescribing errors is widely used; junior doctors require extra support when prescribing; medical records may be incomplete and specific prescribing support (eg, antibiotic prescribing support) is widely in use. Conclusions - There is no single collaborative approach taken to paediatric prescribing support in English paediatric hospitals. Success of electronic prescribing in English paediatric hospitals is considerably behind leaders such as the USA. Use of clinical pharmacists to support prescribers is important as supported by previous studies in Spain and the USA.

The role of group activity participation in depression among institutionalized elderly

The role of group activity participation in depression among a group of residents (N=65), age 80 and older, in a nursing home was examined using the framework of Roy's Adaptation Theory and Nolen-Hoeksema's Response Style Theory of Depression. Roy views depression as a maladaptation. Nolen-Hoeksema views group activity participation as a therapeutic distraction to break depressed moods and thus allow for positive adaptation. This study utilized data from medical records, group activity attendance, and self-report questionnaires. Demographic distributions were computed and correlational statistics were performed between subjects' participation and their degree of depression, pain experience, functional status, presence of social support, and perception of benefits. Results show a negative correlation between frequency of participation and Geriatric Depression Scale score (GDS). The wide range of measured frequencies among low GDS-scored subjects suggests that less depressed individuals exercise more freedom of choice to participate than those who are more depressed. Significant finding show a positive correlation of group activity participation with functional status in terms of ambulation. Data shows that the experience of pain was not a significant deterrent to participation. The presence of social support from the staff and family did not increase participation. However there is a lesser GDS score among subjects who had recent family/friends visit suggesting a positive role of family in decreasing depression. These results are significant not only for optimizing group therapeutic effects but also for understanding basic human and environmental correlates of depression. Study limitations are pointed out and recommendations are presented.

An investigation of the relationships among self-care behaviors, health beliefs, and metabolic control in the non-insulin-dependent diabetic adult

Self-care and health beliefs have been found to be important concepts in the management of chronic diseases such as diabetes mellitus. Poor metabolic control has been associated with a higher incidence of complications in diabetic patients. This study sought to explore any relationships among perceptions of self-care behaviors, health beliefs and metabolic control. The sample consisted of 52 outpatients with non-insulin-dependent diabetes from a large teaching medical center. Interviews were done to obtain the patients' perceptions of their self-care behaviors, and their health beliefs concerning diabetes. Results of glycosylated hemoglobin and/or serum glucose levels were obtained from the medical records. Data were analyzed using Cochran-Mantel-Haenzel statistics, and Pearson's r. Results indicated no significant relationships among perceptions of self-care behaviors, health beliefs and metabolic control. Ethnicity, education and gender were found to be significantly associated with self-care behaviors and health beliefs.

A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications.

INTRODUCTION: The ability to reproducibly identify clinically equivalent patient populations is critical to the vision of learning health care systems that implement and evaluate evidence-based treatments. The use of common or semantically equivalent phenotype definitions across research and health care use cases will support this aim. Currently, there is no single consolidated repository for computable phenotype definitions, making it difficult to find all definitions that already exist, and also hindering the sharing of definitions between user groups. METHOD: Drawing from our experience in an academic medical center that supports a number of multisite research projects and quality improvement studies, we articulate a framework that will support the sharing of phenotype definitions across research and health care use cases, and highlight gaps and areas that need attention and collaborative solutions. FRAMEWORK: An infrastructure for re-using computable phenotype definitions and sharing experience across health care delivery and clinical research applications includes: access to a collection of existing phenotype definitions, information to evaluate their appropriateness for particular applications, a knowledge base of implementation guidance, supporting tools that are user-friendly and intuitive, and a willingness to use them. NEXT STEPS: We encourage prospective researchers and health administrators to re-use existing EHR-based condition definitions where appropriate and share their results with others to support a national culture of learning health care. There are a number of federally funded resources to support these activities, and research sponsors should encourage their use.

Maternal-Fetal Attachment and Health Behaviors among Women with HIV/AIDS

Background: Mothers with HIV often face personal and environmental risks for poor maternal health behaviors and infant neglect, even when HIV transmission to the infant was prevented. Maternal-fetal attachment (MFA), the pre-birth relationship of a woman with her fetus, may be the precursor to maternal caregiving. Using the strengths perspective in social work, which embeds MFA within a socio-ecological conceptual framework, it is hypothesized that high levels of maternal-fetal attachment may protect mothers and infants against poor maternal health behaviors. Objective: To assess whether MFA together with history of substance use, living marital status, planned pregnancy status, and timing of HIV diagnosis predict three desirable maternal health behaviors (pregnancy care, adherence to prenatal antiretroviral therapy–ART, and infant’s screening clinic care) among pregnant women with HIV/AIDS. Method: Prospective observation and hypothesis-testing multivariate analyses. Over 17 consecutive months, all eligible English- or Spanish-speaking pregnant women with HIV ( n = 110) were approached in the principal obstetric and screening clinics in Miami-Dade County, Florida at 24 weeks’ gestation; 82 agreed to enroll. During three data collection periods from enrollment until 16 weeks after childbirth (range: 16 to 32 weeks), participants reported on socio-demographic and predictor variables, MFA, and pregnancy care. Measures of adherence to ART and infant care were extracted from medical records. Findings: Sociodemographic, pregnancy, and HIV disease characteristics in this sample suggest changes in the makeup of HIV-infected pregnant women parallel to the evolution of the HIV epidemic in the USA over the past two decades. The MFA model predicted maternal health behaviors for pregnancy care (R2 = .37), with MFA, marital living status, and planned pregnancy status independently contributing ( = .50, = .28, = .23, respectively). It did not predict adherence to ART medication or infant care. Relevance: These findings provide the first focused evidence of the protective role of MFA against poor maternal health behaviors among pregnant women with HIV, in the presence of adverse life circumstances. Social desirability biases in some self-report measures may limit the findings. Suggestions are made for orienting future inquiry on maternal health behaviors during childbirth toward relationship and protection.

Federated architecture for biomedical data integration

The last decades have been characterized by a continuous adoption of IT solutions in the healthcare sector, which resulted in the proliferation of tremendous amounts of data over heterogeneous systems. Distinct data types are currently generated, manipulated, and stored, in the several institutions where patients are treated. The data sharing and an integrated access to this information will allow extracting relevant knowledge that can lead to better diagnostics and treatments. This thesis proposes new integration models for gathering information and extracting knowledge from multiple and heterogeneous biomedical sources. The scenario complexity led us to split the integration problem according to the data type and to the usage specificity. The first contribution is a cloud-based architecture for exchanging medical imaging services. It offers a simplified registration mechanism for providers and services, promotes remote data access, and facilitates the integration of distributed data sources. Moreover, it is compliant with international standards, ensuring the platform interoperability with current medical imaging devices. The second proposal is a sensor-based architecture for integration of electronic health records. It follows a federated integration model and aims to provide a scalable solution to search and retrieve data from multiple information systems. The last contribution is an open architecture for gathering patient-level data from disperse and heterogeneous databases. All the proposed solutions were deployed and validated in real world use cases.

Epidemiology and genetic architecture of blood pressure: a family based study of Generation Scotland

Hypertension is a major risk factor for cardiovascular disease and mortality, and a growing global public health concern, with up to one-third of the world’s population affected. Despite the vast amount of evidence for the benefits of blood pressure (BP) lowering accumulated to date, elevated BP is still the leading risk factor for disease and disability worldwide. It is well established that hypertension and BP are common complex traits, where multiple genetic and environmental factors contribute to BP variation. Furthermore, family and twin studies confirmed the genetic component of BP, with a heritability estimate in the range of 30-50%. Contemporary genomic tools enabling the genotyping of millions of genetic variants across the human genome in an efficient, reliable, and cost-effective manner, has transformed hypertension genetics research. This is accompanied by the presence of international consortia that have offered unprecedentedly large sample sizes for genome-wide association studies (GWASs). While GWAS for hypertension and BP have identified more than 60 loci, variants in these loci are associated with modest effects on BP and in aggregate can explain less than 3% of the variance in BP. The aims of this thesis are to study the genetic and environmental factors that influence BP and hypertension traits in the Scottish population, by performing several genetic epidemiological analyses. In the first part of this thesis, it aims to study the burden of hypertension in the Scottish population, along with assessing the familial aggregation and heritialbity of BP and hypertension traits. In the second part, it aims to validate the association of common SNPs reported in the large GWAS and to estimate the variance explained by these variants. In this thesis, comprehensive genetic epidemiology analyses were performed on Generation Scotland: Scottish Family Health Study (GS:SFHS), one of the largest population-based family design studies. The availability of clinical, biological samples, self-reported information, and medical records for study participants has allowed several assessments to be performed to evaluate factors that influence BP variation in the Scottish population. Of the 20,753 subjects genotyped in the study, a total of 18,470 individuals (grouped into 7,025 extended families) passed the stringent quality control (QC) criteria and were available for all subsequent analysis. Based on the BP-lowering treatment exposure sources, subjects were further classified into two groups. First, subjects with both a self-reported medications (SRMs) history and electronic-prescription records (EPRs; n =12,347); second, all the subjects with at least one medication history source (n =18,470). In the first group, the analysis showed a good concordance between SRMs and EPRs (kappa =71%), indicating that SRMs can be used as a surrogate to assess the exposure to BP-lowering medication in GS:SFHS participants. Although both sources suffer from some limitations, SRMs can be considered the best available source to estimate the drug exposure history in those without EPRs. The prevalence of hypertension was 40.8% with higher prevalence in men (46.3%) compared to women (35.8%). The prevalence of awareness, treatment and controlled hypertension as defined by the study definition were 25.3%, 31.2%, and 54.3%, respectively. These findings are lower than similar reported studies in other populations, with the exception of controlled hypertension prevalence, which can be considered better than other populations. Odds of hypertension were higher in men, obese or overweight individuals, people with a parental history of hypertension, and those living in the most deprived area of Scotland. On the other hand, deprivation was associated with higher odds of treatment, awareness and controlled hypertension, suggesting that people living in the most deprived area may have been receiving better quality of care, or have higher comorbidity levels requiring greater engagement with doctors. These findings highlight the need for further work to improve hypertension management in Scotland. The family design of GS:SFHS has allowed family-based analysis to be performed to assess the familial aggregation and heritability of BP and hypertension traits. The familial correlation of BP traits ranged from 0.07 to 0.20, and from 0.18 to 0.34 for parent-offspring pairs and sibling pairs, respectively. A higher correlation of BP traits was observed among first-degree relatives than other types of relative pairs. A variance-component model that was adjusted for sex, body mass index (BMI), age, and age-squared was used to estimate heritability of BP traits, which ranged from 24% to 32% with pulse pressure (PP) having the lowest estimates. The genetic correlation between BP traits showed a high correlation between systolic (SBP), diastolic (DBP) and mean arterial pressure (MAP) (G: 81% to 94%), but lower correlations with PP (G: 22% to 78%). The sibling recurrence risk ratio (λS) for hypertension and treatment were calculated as 1.60 and 2.04 respectively. These findings confirm the genetic components of BP traits in GS:SFHS, and justify further work to investigate genetic determinants of BP. Genetic variants reported in the recent large GWAS of BP traits were selected for genotyping in GS:SFHS using a custom designed TaqMan® OpenArray®. The genotyping plate included 44 single nucleotide polymorphisms (SNPs) that have been previously reported to be associated with BP or hypertension at genome-wide significance level. A linear mixed model that is adjusted for age, age-squared, sex, and BMI was used to test for the association between the genetic variants and BP traits. Of the 43 variants that passed the QC, 11 variants showed statistically significant association with at least one BP trait. The phenotypic variance explained by these variant for the four BP traits were 1.4%, 1.5%, 1.6%, and 0.8% for SBP, DBP, MAP, and PP, respectively. The association of genetic risk score (GRS) that were constructed from selected variants has showed a positive association with BP level and hypertension prevalence, with an average effect of one mmHg increase with each 0.80 unit increases in the GRS across the different BP traits. The impact of BP-lowering medication on the genetic association study for BP traits has been established, with typical practice of adding a fixed value (i.e. 15/10 mmHg) to the measured BP values to adjust for BP treatment. Using the subset of participants with the two treatment exposure sources (i.e. SRMs and EPRs), the influence of using either source to justify the addition of fixed values in SNP association signal was analysed. BP phenotypes derived from EPRs were considered the true phenotypes, and those derived from SRMs were considered less accurate, with some phenotypic noise. Comparing SNPs association signals between the four BP traits in the two model derived from the different adjustments showed that MAP was the least impacted by the phenotypic noise. This was suggested by identifying the same overlapped significant SNPs for the two models in the case of MAP, while other BP traits had some discrepancy between the two sources

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.