Prognostication Stereotype of Patients Morbidity and Mortality by Extraction of E-Health Records

2018 ◽  
Vol 6 (6) ◽  
pp. 215
Author(s):  
Sunitha .T ◽  
Shyamala .J ◽  
Annie Jesus Suganthi Rani.A
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Health Risks ◽  
Preventive Intervention ◽  
Classification Problem ◽  
Health Examination ◽  
Disease Area ◽  
Health Records ◽  
Mortality And Morbidity ◽  
Main Motive

Data mining suggest an innovative way of prognostication stereotype of Patients health risks. Large amount of Electronic Health Records (EHRs) collected over the years have provided a rich base for risk analysis and prediction. An EHR contains digitally stored healthcare information about an individual, such as observations, laboratory tests, diagnostic reports, medications, procedures, patient identifying information and allergies. A special type of EHR is the Health Examination Records (HER) from annual general health check-ups. Identifying participants at risk based on their current and past HERs is important for early warning and preventive intervention. By “risk”, we mean unwanted outcomes such as mortality and morbidity. This approach is limited due to the classification problem and consequently it is not informative about the specific disease area in which a personal is at risk. Limited amount of data extracted from the health record is not feasible for providing the accurate risk prediction. The main motive of this project is for risk prediction to classify progressively developing situation with the majority of the data unlabeled.

scholarly journals Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine (Preprint)

2018 ◽  
Author(s):  
Xiaofang Wang ◽  
Yan Zhang ◽  
Shiying Hao ◽  
Le Zheng ◽  
Jiayu Liao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
At Risk ◽  
High Risk ◽  
Electronic Health Records ◽  
Prediction Model ◽  
Risk Prediction ◽  
Prospective Cohort ◽  
Retrospective Cohort ◽  
Health Records ◽  
History Of

BACKGROUND Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate. OBJECTIVE The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population. METHODS Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018. RESULTS The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer. CONCLUSIONS We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.

scholarly journals Risk Prediction of Barrett’s Esophagus in a Taiwanese Health Examination Center Based on Regression Models

2021 ◽  
Vol 18 (10) ◽  
pp. 5332
Author(s):  
Po-Hsiang Lin ◽  
Jer-Guang Hsieh ◽  
Hsien-Chung Yu ◽  
Jyh-Horng Jeng ◽  
Chiao-Lin Hsu ◽  
...  
Keyword(s):  
Barrett’S Esophagus ◽  
Risk Prediction ◽  
Barrett's Esophagus ◽  
Gastrointestinal Endoscopy ◽  
Prediction Models ◽  
Upper Gastrointestinal Endoscopy ◽  
Health Examination ◽  
Endoscopic Screening ◽  
Risk Prediction Models ◽  
Upper Gastrointestinal

Determining the target population for the screening of Barrett’s esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.

scholarly journals The Conceptualization of Risk Tolerance and Scale Development for Measuring Publics’ Tolerance of Individual Health Risks

2021 ◽  
Vol 4 ◽  
pp. 29-72
Author(s):  
Hyoyeun Jun ◽  
Yan Jin
Keyword(s):  
At Risk ◽  
Scale Development ◽  
Risk Communication ◽  
Health Risks ◽  
Risky Behaviors ◽  
Risk Tolerance ◽  
Negative Consequences ◽  
Individual Health ◽  
Psychometric Tool ◽  
Depth Interviews

Risk tolerance, identified by scholars over two decades ago as an essential concept in risk communication, has remained understudied without clear conceptual and operational definitions. As the first study developing a multiple-item scale for measuring at-risk publics’ tolerance of different risk types, this study refines the conceptualization of risk tolerance and advances its operationalization in the setting of individual health risks. Qualitative research (in-depth interviews: n = 28; focus group: n = 30) and two survey datasets (sample 1: n = 500; sample 2: n = 500) were employed for scale development and testing. Results identify that two types of individual health risk tolerance exhibited by at-risk publics: (1) Compulsive tendency toward risk taking (CTRT), as evidenced in their unwillingness to refrain from risky behaviors even if they know the negative consequences and (2) inertial resistance to risk prevention (IRRP), as indicated by their indifference toward or intentionally ignoring health messages advocating for behavioral changes. The two-factor 13-item scale’s reliability, factorial structure, and validity are further assessed. This risk tolerance scale provides a valid and reliable psychometric tool for risk communication scholars and practitioners to measure publics’ tolerance of different individual health risks in order to design effective messages to overcome it as a barrier.

scholarly journals Differentiating Military-Connected and Non-Military-Connected Students: Predictors of Graduation and SAT Score

2020 ◽  
Author(s):  
Ryan Shaun Baker ◽  
Andy Berning ◽  
Sujith M. Gowda
Keyword(s):  
At Risk ◽  
Risk Prediction ◽  
Early Warning ◽  
Student Outcomes ◽  
At Risk Students ◽  
Not Given ◽  
Students At Risk ◽  
The Many ◽  
Contemporary Practice ◽  
Core Part

At-risk prediction and early warning initiatives have become a core part of contemporary practice in American high schools, with the goal of identifying students at-risk of poorer outcomes, determining which factors are associated with these risks, and developing interventions to support at-risk students’ individual needs. However, efforts along these lines have typically ignored whether a student is military-connected or not. Given the many differences between military-connected students and other students, we investigate whether models developed for non-military-connected students still function effectively for military-connected students, studying the specific cases of graduation prediction and SAT score prediction. We then identify which variables are highly different in their connections to student outcomes, between populations.

scholarly journals Economics of deforestation: Poverty, inequality and socio-environmental impacts of the consumption of firewood in Ecuador

2020 ◽  
Vol 67 (3) ◽  
pp. 405-431
Author(s):  
María Vallejo ◽  
Maribel Caicedo
Keyword(s):  
At Risk ◽  
Energy Policy ◽  
Environmental Impacts ◽  
Health Risks ◽  
Rural Areas ◽  
Rural Populations

We take the concept of the economics of deforestation to analyse the consumption of firewood in Ecuador during 2018. We identify poor rural populations as being at risk, since the incomplete burning of firewood generates emissions of CO2 that can reach levels that are harmful to their health. We calculate that 95% of the impacts associated with the consumption of firewood are concentrated in rural areas, most of them in poverty conditions: the deforestation of 5,935 hectares, the emission of 1,317.38 Gg of CO2 and 94.58 Gg of CO due to the consumption of 782.08 Gg of firewood. We suggest an energy policy based on solidarity to reduce health risks for these communities, which in turn will enable other impacts to be mitigated. However, it will be necessary to include specific policies for commercial, industrial and productive uses of firewood, where about 65% of firewood consumption and its impacts are concentrated.

scholarly journals The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

2019 ◽  
Vol 53 (10) ◽  
pp. 954-964 ◽  
Author(s):  
Trehani M Fonseka ◽  
Venkat Bhat ◽  
Sidney H Kennedy
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Risk Prediction ◽  
Clinical Management ◽  
Behavior Management ◽  
Suicide Risk ◽  
English Language ◽  
Case Reports ◽  
Mental Healthcare ◽  
Health Concern

Objective: Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within ‘big data’ to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management. Methods: This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance. Results: At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports. Conclusion: Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.

Sign in / Sign up

Export Citation Format

Share Document