Medical information retrieval systems for e-Health care records using fuzzy based machine learning model

2020 ◽  
pp. 103344 ◽  
Author(s):  
Arokia Jesu Prabhu L ◽  
Sudhakar Sengan ◽  
Kamalam G K ◽  
Vellingiri J ◽  
Jagadeesh Gopal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Information Retrieval ◽  
Medical Information ◽  
Learning Model ◽  
Retrieval Systems ◽  
Medical Information Retrieval ◽  
Machine Learning Model ◽  
Information Retrieval Systems ◽  
Health Care Records

Medical E-Reference

2011 ◽  
pp. 116-125
Author(s):  
Terese DeSimio ◽  
Ximena Chrisagis
Keyword(s):  
Medical Information ◽  
Value Added ◽  
Clinical Information ◽  
Retrieval Systems ◽  
Medical Information Retrieval ◽  
Healthcare Environments ◽  
Information Retrieval Systems ◽  
Electronic Access ◽  
User Friendly ◽  
Network Administrators

Electronic medical information retrieval systems and reference sources were some of the first discipline-specific e-resources to be developed, due to physicians’ need to access the most current and relevant clinical information as quickly as possible. Many medical publishers and information aggregators have been incorporating the features their users demand for years. Thus, medical e-reference publishing could serve as a benchmark for e-reference publishing in other fields. Yet medical e-reference is not without its challenges. Today’s physicians and medical students expect immediate and user-friendly electronic access to media rich and value added clinical references, particularly via their mobile devices. Publishers, librarians, and network administrators will need to ensure that mobile information sources users demand are discoverable and easy to access and use, even in healthcare environments where increased data security is necessary.

Searching Health Information in Question-Answering Systems

2013 ◽  
pp. 474-490 ◽  
Author(s):  
María-Dolores Olvera-Lobo ◽  
Juncal Gutiérrez-Artacho
Keyword(s):  
Information Retrieval ◽  
Health Information ◽  
Question Answering ◽  
Medical Information ◽  
Restricted Domain ◽  
Retrieval Systems ◽  
Question Answering Systems ◽  
Information Retrieval Systems

Question-Answering Systems (QA Systems) can be viewed as a new alternative to the more familiar Information Retrieval Systems. These systems try to offer detailed, understandable answers to factual questions, in order to retrieve a collection of documents related to a particular search (Jackson & Schilder, 2005). The authors carry out a study to evaluate the quality and efficiency of open- and restricted-domain QA systems as sources for physicians and users in general through one monolingual evaluation and another multilingual. Their objective led them to use definition-type questions in order to evaluate QA systems and determine if they are useful to retrieve medical information. In addition, they analyze and evaluate the results obtained, and identify the source or sources used by the systems and their procedure (Olvera-Lobo & Gutiérrez-Artacho, 2010, 2011).

scholarly journals Comparison of Use of Health Care Services and Spending for Unauthorized Immigrants vs Authorized Immigrants or US Citizens Using a Machine Learning Model

2020 ◽  
Vol 3 (12) ◽  
pp. e2029230
Author(s):  
Fernando A. Wilson ◽  
Leah Zallman ◽  
José A. Pagán ◽  
Alexander N. Ortega ◽  
Yang Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Services ◽  
Learning Model ◽  
Care Services ◽  
Machine Learning Model ◽  
Use Of Health Care

scholarly journals Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis (Preprint)

2020 ◽  
Author(s):  
Gang Luo ◽  
Claudia L Nau ◽  
William W Crawford ◽  
Michael Schatz ◽  
Robert S Zeiger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Care Management ◽  
Binary Classification ◽  
Health Care Systems ◽  
Characteristic Curve ◽  
Secondary Analysis ◽  
Learning Model ◽  
Emergency Department Visits ◽  
Machine Learning Model

BACKGROUND Asthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown. OBJECTIVE This study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC). METHODS The patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma. RESULTS Our model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391). CONCLUSIONS Our modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management. INTERNATIONAL REGISTERED REPORT RR2-10.2196/resprot.5039

scholarly journals Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis

10.2196/22689 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e22689
Author(s):  
Gang Luo ◽  
Claudia L Nau ◽  
William W Crawford ◽  
Michael Schatz ◽  
Robert S Zeiger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Care Management ◽  
Binary Classification ◽  
Health Care Systems ◽  
Characteristic Curve ◽  
Secondary Analysis ◽  
Learning Model ◽  
Emergency Department Visits ◽  
Machine Learning Model

Background Asthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown. Objective This study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC). Methods The patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma. Results Our model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391). Conclusions Our modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039

scholarly journals Designing a Fused Machine Learning Model for the Provision of Smart Health Care in MANETS

2019 ◽  
Vol 04 (09) ◽  
pp. 363-370
Author(s):  
Kirori Gathuo Mindo ◽  
Simon M. Karume ◽  
Moses M. Thiga
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Learning Model ◽  
Machine Learning Model ◽  
Smart Health
Sign in / Sign up

Export Citation Format

Share Document