Characterizing environmental and phenotypic associations using information theory and electronic health records

The availability of up-to-date, executable, evidence-based medical knowledge is essential for many clinical applications, such as pharmacovigilance, but executable knowledge is costly to obtain and update. Automated acquisition of environmental and phenotypic associations in biomedical and clinical documents using text mining has showed some success. The usefulness of the association knowledge is limited, however, due to the fact that the specific relationships between clinical entities remain unknown. In particular, some associations are indirect relations due to interdependencies among the data. In this work, we develop methods using mutual information (MI) and its property, the data processing inequality (DPI), to help characterize associations that were generated based on use of natural language processing to encode clinical information in narrative patient records followed by statistical methods. Evaluation based on a random sample consisting of two drugs and two diseases indicates an overall precision of 81%. This preliminary study demonstrates that the proposed method is effective for helping to characterize phenotypic and environmental associations obtained from clinical reports

Privacy in the Genomic Era

Genome sequencing technology has advanced at a rapid pace and it is now possible to generate highly-detailed genotypes inexpensively. The collection and analysis of such data has the potential to support various applications, including personalized medical services. While the benefits of the genomics revolution are trumpeted by the biomedical community, the increased availability of such data has major implications for personal privacy; notably because the genome has certain essential features, which include (but are not limited to) (i) an association with traits and certain diseases, (ii) identification capability (e.g., forensics), and (iii) revelation of family relationships. Moreover, direct-to-consumer DNA testing increases the likelihood that genome data will be made available in less regulated environments, such as the Internet and for-profit companies. The problem of genome data privacy thus resides at the crossroads of computer science, medicine, and public policy. While the computer scientists have addressed data privacy for various data types, there has been less attention dedicated to genomic data. Thus, the goal of this paper is to provide a systematization of knowledge for the computer science community. In doing so, we address some of the (sometimes erroneous) beliefs of this field and we report on a survey we conducted about genome data privacy with biomedical specialists. Then, after characterizing the genome privacy problem, we review the state-of-the-art regarding privacy attacks on genomic data and strategies for mitigating such attacks, as well as contextualizing these attacks from the perspective of medicine and public policy. This paper concludes with an enumeration of the challenges for genome data privacy and presents a framework to systematize the analysis of threats and the design of countermeasures as the field moves forward

A Systematic Review of Natural Language Processing for Knowledge Management in Healthcare

Driven by the visions of Data Science, recent years have seen a paradigm shift in Natural Language Processing (NLP). NLP has set the milestone in text processing and proved to be the preferred choice for researchers in the healthcare domain. The objective of this paper is to identify the potential of NLP, especially, how NLP is used to support the knowledge management process in the healthcare domain, making data a critical and trusted component in improving health outcomes. This paper provides a comprehensive survey of the state-of-the-art NLP research with a particular focus on how knowledge is created, captured, shared, and applied in the healthcare domain. Our findings suggest, first, the techniques of NLP those supporting knowledge management extraction and knowledge capture processes in healthcare. Second, we propose a conceptual model for the knowledge extraction process through NLP. Finally, we discuss a set of issues, challenges, and proposed future research areas

A Systematic Review of Natural Language Processing for Knowledge Management in Healthcare

Driven by the visions of Data Science, recent years have seen a paradigm shift in Natural Language Processing (NLP). NLP has set the milestone in text processing and proved to be the preferred choice for researchers in the healthcare domain. The objective of this paper is to identify the potential of NLP, especially, how NLP is used to support the knowledge management process in the healthcare domain, making data a critical and trusted component in improving the health outcomes. This paper provides a comprehensive survey of the state-of-the-art NLP research with a particular focus on how knowledge is created, captured, shared, and applied in the healthcare domain. Our findings suggest, first, the techniques of NLP those supporting knowledge management extraction and knowledge capture processes in healthcare. Second, we propose a conceptual model for the knowledge extraction process through NLP. Finally, we discuss a set of issues, challenges, and proposed future research areas

The Case for Conscientiousness: Evidence and Implications for a Personality Trait Marker of Health and Longevity

Purpose Recent initiatives by major funding agencies have emphasized translational and personalized approaches (e.g., genetic testing) to health research and health management. While such directives are appropriate, and will likely produce tangible health benefits, we seek to highlight a confluence of several lines of research showing relations between the personality dimension of conscientiousness and a variety of health-related outcomes. Methods Using a modified health process model, we review the compelling evidence linking conscientiousness to health and disease processes, including longevity, diseases, morbidity-related risk factors, health-related psycho-physiological mechanisms, health-related behaviors, and social environmental factors related to health. Conclusion We argue the accumulated evidence supports greater integration of conscientiousness into public health, epidemiological, and medical research, with the ultimate aim of understanding how facilitating more optimal trait standing might foster better health

Selecting information in electronic health records for knowledge acquisition

AbstractKnowledge acquisition of relations between biomedical entities is critical for many automated biomedical applications, including pharmacovigilance and decision support. Automated acquisition of statistical associations from biomedical and clinical documents has shown some promise. However, acquisition of clinically meaningful relations (i.e. specific associations) remains challenging because textual information is noisy and co-occurrence does not typically determine specific relations. In this work, we focus on acquisition of two types of relations from clinical reports: disease-manifestation related symptom (MRS) and drug-adverse drug event (ADE), and explore the use of filtering by sections of the reports to improve performance. Evaluation indicated that applying the filters improved recall (disease-MRS: from 0.85 to 0.90; drug-ADE: from 0.43 to 0.75) and precision (disease-MRS: from 0.82 to 0.92; drug-ADE: from 0.16 to 0.31). This preliminary study demonstrates that selecting information in narrative electronic reports based on the sections improves the detection of disease-MRS and drug-ADE types of relations. Further investigation of complementary methods, such as more sophisticated statistical methods, more complex temporal models and use of information from other knowledge sources, is needed

A genome-wide association study identifies four novel susceptibility loci underlying inguinal hernia.

Inguinal hernia repair is one of the most commonly performed operations in the world, yet little is known about the genetic mechanisms that predispose individuals to develop inguinal hernias. We perform a genome-wide association analysis of surgically confirmed inguinal hernias in 72,805 subjects (5,295 cases and 67,510 controls) and confirm top associations in an independent cohort of 92,444 subjects with self-reported hernia repair surgeries (9,701 cases and 82,743 controls). We identify four novel inguinal hernia susceptibility loci in the regions of EFEMP1, WT1, EBF2 and ADAMTS6. Moreover, we observe expression of all four genes in mouse connective tissue and network analyses show an important role for two of these genes (EFEMP1 and WT1) in connective tissue maintenance/homoeostasis. Our findings provide insight into the aetiology of hernia development and highlight genetic pathways for studies of hernia development and its treatment

Mikrobioomi väärtus terviseuuringutes

Väitekirja elektrooniline versioon ei sisalda publikatsiooneTehnoloogia areng on andnud inimesele võimaluse uurida ümbritsevat maailma nurkade alt, mille jaoks veel mõned kümnendid tagasi võimalused puudusid. Üks selliseid teadusvaldkondi on inimese mikrobioomi ehk meie kehal ja kehas elavate mikroorganismide nagu näiteks bakterite ja viiruste uurimine. On teada, et mikrobioomil on oluline funktsioon inimese tervisele ning mikrobioomi kooslust omakorda mõjutavad suurel määral meie elustiil, toitumisharjumused, ümbritsev keskkond ning tervislik seisund. Just seosed haigustega on tekitanud huvi mikrobioomi kasutamiseks meditsiinilistes rakendustes. Doktoritöö eesmärk oli uurida, millised faktorid lisaks teadaolevatele on seotud meie soolestiku mikrobioomi kooslusega ning kuidas on mikrobioomi andmeid võimalik kasutada haiguste diagnoosimiseks ning haigusriskide hindamiseks. Esiteks uurisime teist tüüpi diabeeti ning näitasime, et mikrobioom aitab senisest täpsemalt ennustada muutusi veresuhkru regulatsiooni kirjeldavates parameetrites, milleks olid eelkõige insuliini eritamisega seotud näitajaid. Järgmiseks eesmärgiks oli kirjeldada Eesti populatsiooni soolestiku mikrorbioomi profiiili ning tuvasatada mikrobioomi kooslust mõjutavad faktorid. Eesti Geenivaramu terviseandmestikku kasutades tuvastasime, et antibiootikumide pikaajalisel korduval kasutamisel on akkumuleeruv mõju mikrobioomi kooslusele olenemata sellest, kas antibiootikume on kasutatud viimase kuue kuu jooksul. Analüüsides pikaajalise antibiootikumide mõju arvesse võtmine võimaldas omakorda täpsustada haigusspetsiifilisi muutusi mikrobioomis. Lisaks uurisime, kas soolestiku mikrobioomi abil inimeste grupeerimine võimaldaks ka kasutust kliinilistes rakendustes. Selgus, et selliselt mikrobioomi kooslust lihtsustades on võimalik küll anda hinnang inimese üldisele elustiilile, kuid tõendid haiguste diagnoosimisel või haiguste riski hindamiseks pole piisavalt tugevad. Kokkuvõttes on mikrobioomi uurimisel meditsiinis suur potentsiaal, mis võimaldab täiendada olemasolevaid võimalusi haiguste diagnoosimiseks ning riskide hindamiseks, kuid see eeldab täiendavaid teadmisi ja uuringuid.The technological revolution allows us to study the world beyond the limits that were holding us back only a couple of decades ago. One of such fields is the study of the human microbiome. Tiny microorganisms making up the microbiome such as bacteria and viruses have been known to intervene with our health for centuries, but the whole microbial ecosystem has turned out to be more complex than previously thought. The extent of the role of the microbiome to our own functioning and well-being is just starting to unravel. Nevertheless, microbiome has been associated with a large variety of intrinsic and extrinsic factors, including various complex diseases. This evidence is leading a slow but steady progress towards clinical applications such as using microbiome for improving disease diagnostics or estimating the risk of developing a condition. This thesis aimed to expand the understanding of the factors influencing our gut microbiome composition and assess the possibility and challenges in using the microbiome composition for the clinical applications. Firstly, we identified novel microbial biomarkers for identifying the progression of type 2 diabetes (T2D), which can be used to improve the current risk estimation. Secondly, using the comprehensive health data available in the Estonian Biobank, we characterized the profile of the gut microbiome in the Estonian population and identified various factors affecting the microbiome. Our study indicated that the long-term antibiotics usage has an accumulative effect on the gut microbiome composition independent of recent usage. The novelty of this result has a significant impact on the microbiome field and the future analysis need to account for such drug effects. Lastly, we considered dividing the subjects into a few distinct clusters based on their microbiome composition and evaluated the clinical applicability of such representation. We showed that although this approach is desirable in its simplicity, it is not sufficient for clinical applications. In conclusion, the microbiome science is heading towards clinical applications, but exploratory analysis is still needed. Nevertheless, the challenges ahead do not overshadow the enthusiasm.https://www.ester.ee/record=b551831

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

Background: There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been little research into how to make the most of SBDH information from EHRs. Methods: A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results: Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (NLP) technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion: Despite known associations between SBDH and disease, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive ModelComment: 32 pages, 5 figure