Computing disease incidence, prevalence and comorbidity from electronic medical records

Bagley, Steven C.; Altman, Russ B.

doi:10.1016/j.jbi.2016.08.005

Cited by 32 publications

(22 citation statements)

References 51 publications

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“…The results presented in this paper were, exclusively, based on hospitalized patient data and consequently, they should be interpreted accordingly. Possible errors in the diagnostic codes and admission dates, as well as incomplete data and inaccuracies, all inherent to the health dataset used, might cause certain variations in the resulting disease associations, as also discussed by several other authors 43 , 44 . Furthermore, some unexpected –according to the literature- temporal comorbidities identified (e.g., Cataract (366) → Diabetes Mellitus (250) or Cataract (366) → Chronic Bronchitis (491) in Table 1 ), could be possibly explained by the fact that some diseases share important risk factors (such as, ageing, smoking and/or obesity), thereby making difficult to define with accuracy which disease precedes or follows another.…”

Section: Discussionmentioning

confidence: 88%

Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study

Giannoula

Gutiérrez

Bravo

et al. 2018

Sci Rep

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Time is a crucial parameter in the assessment of comorbidities in population-based studies, as it permits to identify more complex disease patterns apart from the pairwise disease associations. So far, it has been, either, completely ignored or only, taken into account by assessing the temporal directionality of identified comorbidity pairs. In this work, a novel time-analysis framework is presented for large-scale comorbidity studies. The disease-history vectors of patients of a regional Spanish health dataset are represented as time sequences of ordered disease diagnoses. Statistically significant pairwise disease associations are identified and their temporal directionality is assessed. Subsequently, an unsupervised clustering algorithm, based on Dynamic Time Warping, is applied on the common disease trajectories in order to group them according to the temporal patterns that they share. The proposed methodology for the temporal assessment of such trajectories could serve as the preliminary basis of a disease prediction system.

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Section: Discussionmentioning

confidence: 88%

Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study

Giannoula

Gutiérrez

Bravo

et al. 2018

Sci Rep

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“…If a patient's blood pressure is not measured, this doesn't mean that the patient does not have hypertension. Similarly, mild pain is often treated using over-the-counter medications leaving no mark in the medical record [30,31].…”

Section: D Data Qualitymentioning

confidence: 99%

Secondary Use and Analysis of Big Data Collected for Patient Care

et al. 2017

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SummaryObjectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on "Data mining and Big Data analytics" focuses on the literature published during the last two years, covering the timeframe since the working group's last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.

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“…Nonetheless, the etiology and outcome of single conditions will very often be related to their temporal context in terms of other conditions 18–20 . A temporal trend is a prerequisite for causality and should systematically be taken into consideration when studying patient-specific co-occurrences of conditions 21,22 .…”

Section: Introductionmentioning

confidence: 99%

Population-wide analysis of differences in disease progression patterns in men and women

et al. 2019

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Sex-stratified medicine is a fundamentally important, yet understudied, facet of modern medical care. A data-driven model for how to systematically analyze population-wide, longitudinal differences in hospital admissions between men and women is needed. Here, we demonstrate a systematic analysis of all diseases and disease co-occurrences in the complete Danish population using the ICD-10 and Global Burden of Disease terminologies. Incidence rates of single diagnoses are different for men and women in most cases. The age at first diagnosis is typically lower for men, compared to women. Men and women share many disease co-occurrences. However, many sex-associated incongruities not linked directly to anatomical or genomic differences are also found. Analysis of multi-step trajectories uncover differences in longitudinal patterns, for example concerning injuries and substance abuse, cancer, and osteoporosis. The results point towards the need for an increased focus on sex-stratified medicine to elucidate the origins of the socio-economic and ethological differences.

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Computing disease incidence, prevalence and comorbidity from electronic medical records

Cited by 32 publications

References 51 publications

Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study

Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study

Secondary Use and Analysis of Big Data Collected for Patient Care

Population-wide analysis of differences in disease progression patterns in men and women

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