Time-to-Event Predictive Modeling for Chronic Conditions Using Electronic Health Records

2014 ◽  
Vol 29 (3) ◽  
pp. 14-20 ◽  
Author(s):  
Yu-Kai Lin ◽  
Hsinchun Chen ◽  
Randall A. Brown ◽  
Shu-Hsing Li ◽  
Hung-Jen Yang
Keyword(s):  
Electronic Health Records ◽  
Predictive Modeling ◽  
Chronic Conditions ◽  
Time To Event ◽  
Health Records ◽  
Electronic Health

scholarly journals PARAMO: A PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records

2014 ◽  
Vol 48 ◽  
pp. 160-170 ◽  
Author(s):  
Kenney Ng ◽  
Amol Ghoting ◽  
Steven R. Steinhubl ◽  
Walter F. Stewart ◽  
Bradley Malin ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Predictive Modeling ◽  
Health Records ◽  
Electronic Health

scholarly journals Accounting for dependent errors in predictors and time-to-event outcomes using electronic health records, validation samples and multiple imputation

2020 ◽  
Vol 14 (2) ◽  
pp. 1045-1061 ◽  
Author(s):  
Mark J. Giganti ◽  
Pamela A. Shaw ◽  
Guanhua Chen ◽  
Sally S. Bebawy ◽  
Megan M. Turner ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Multiple Imputation ◽  
Time To Event ◽  
Health Records ◽  
Electronic Health

scholarly journals Patient Use and Experience With Online Access to Electronic Health Records in Norway: Results From an Online Survey (Preprint)

2019 ◽  
Author(s):  
Paolo Zanaboni ◽  
Per Egil Kummervold ◽  
Tove Sørensen ◽  
Monika Alise Johansen
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Care Providers ◽  
National Health ◽  
Chronic Conditions ◽  
Online Survey ◽  
Health Condition ◽  
Care Providers ◽  
Health Records ◽  
Electronic Health

BACKGROUND The electronic health record (EHR) has been fully established in all Norwegian hospitals. Patient-accessible electronic health records (PAEHRs) are available to citizens aged 16 years and older through the national health portal Helsenorge. OBJECTIVE This study aimed at understanding how patients use PAEHRs. Three research questions were addressed in order to explore (1) characteristics of users, (2) patients’ use of the service, and (3) patient experience with the service. METHODS We conducted an online survey of users who had accessed their EHR online at least once through the national health portal. Patients from two of the four health regions in Norway were invited to participate. Quantitative data were supplemented by qualitative information. RESULTS A total of 1037 respondents participated in the survey, most of whom used the PAEHR regularly (305/1037, 29.4%) or when necessary (303/1037, 29.2%). Service utilization was associated with self-reported health, age, gender, education, and health care professional background. Patients found the service useful to look up health information (687/778, 88.3%), keep track of their treatment (684/778, 87.9%), prepare for a hospital appointment (498/778, 64.0%), and share documents with their general practitioner (292/778, 37.5%) or family (194/778, 24.9%). Most users found it easy to access their EHR online (965/1037, 93.1%) and did not encounter technical challenges. The vast majority of respondents (643/755, 85.2%) understood the content, despite over half of them acknowledging some difficulties with medical terms or phrases. The overall satisfaction with the service was very high (700/755, 92.7%). Clinical advantages to the patients included enhanced knowledge of their health condition (565/691, 81.8%), easier control over their health status (685/740, 92.6%), better self-care (571/653, 87.4%), greater empowerment (493/674, 73.1%), easier communication with health care providers (493/618, 79.8%), and increased security (655/730, 89.7%). Patients with complex, long-term or chronic conditions seemed to benefit the most. PAEHRs were described as useful, informative, effective, helpful, easy, practical, and safe. CONCLUSIONS PAEHRs in Norway are becoming a mature service and are perceived as useful by patients. Future studies should include experimental designs focused on specific populations or chronic conditions that are more likely to achieve clinically meaningful benefits. Continuous evaluation programs should be conducted to assess implementation and changes of wide-scale routine services over time.

scholarly journals Learning and Visualizing Chronic Latent Representations Using Electronic Health Records

2021 ◽  
Author(s):  
David Chushig-Muzo ◽  
Cristina Soguero-Ruiz ◽  
Pablo de Miguel Bohoyo ◽  
Inmaculada Mora-Jiménez
Keyword(s):  
Health Status ◽  
Electronic Health Records ◽  
Chronic Conditions ◽  
Dimensional Space ◽  
High Dimensional ◽  
Diabetic Patients ◽  
Two Dimensional ◽  
Health Records ◽  
Electronic Health ◽  
Latent Representations

Abstract Background: Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches. Methods: We propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient's health status evolution, which is of paramount importance in the clinical setting. Results: To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients. Conclusions: Our results highlighted the value of ML techniques to extract clinical knowledge, supporting the identification of patients with certain chronic conditions. Furthermore, the patient's health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.

scholarly journals Bi-directional association between depression and HF: An electronic health records-based cohort study

2020 ◽  
Vol 10 ◽  
pp. 2235042X2098405
Author(s):  
William V Bobo ◽  
Euijung Ryu ◽  
Tanya M Petterson ◽  
Kandace Lackore ◽  
Yijing Cheng ◽  
...  
Keyword(s):  
Primary Care ◽  
Cohort Study ◽  
Electronic Health Records ◽  
Chronic Conditions ◽  
Newly Diagnosed ◽  
Health Records ◽  
Care Services ◽  
Primary Care Services ◽  
Increased Risk ◽  
Electronic Health

Objective: To determine whether a bi-directional relationship exists between depression and HF within a single population of individuals receiving primary care services, using longitudinal electronic health records (EHRs). Methods: This retrospective cohort study utilized EHRs for adults who received primary care services within a large healthcare system in 2006. Validated EHR-based algorithms identified 10,649 people with depression (depression cohort) and 5,911 people with HF (HF cohort) between January 1, 2006 and December 31, 2018. Each person with depression or HF was matched 1:1 with an unaffected referent on age, sex, and outpatient service use. Each cohort (with their matched referents) was followed up electronically to identify newly diagnosed HF (in the depression cohort) and depression (in the HF cohort) that occurred after the index diagnosis of depression or HF, respectively. The risks of these outcomes were compared (vs. referents) using marginal Cox proportional hazard models adjusted for 16 comorbid chronic conditions. Results: 2,024 occurrences of newly diagnosed HF were observed in the depression cohort and 944 occurrences of newly diagnosed depression were observed in the HF cohort over approximately 4–6 years of follow-up. People with depression had significantly increased risk for developing newly diagnosed HF (HR 2.08, 95% CI 1.89–2.28) and people with HF had a significantly increased risk of newly diagnosed depression (HR 1.34, 95% CI 1.17–1.54) after adjusting for all 16 comorbid chronic conditions. Conclusion: These results provide evidence of a bi-directional relationship between depression and HF independently of age, sex, and multimorbidity from chronic illnesses.

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