Breast density follow-up decision support system using deep convolutional models

2019 ◽  
Author(s):  
David Richmond ◽  
Dustin Sargent ◽  
Sun Young Park
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Breast Density ◽  
Support System

Design and evaluation of a wireless decision-support system for heart rate variability study in haemodialysis follow-up procedures

2007 ◽  
Vol 88 (3) ◽  
pp. 273-282 ◽  
Author(s):  
José García ◽  
Jesús D. Trigo ◽  
Álvaro Alesanco ◽  
Pedro Serrano ◽  
Javier Mateo ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Variability Study

scholarly journals Protocol for an experimental study to Design and Evaluate a Personalized Human Centered Dietary Decision Support System for use among diabetics in an Indian Setting. (Preprint)

2019 ◽  
Author(s):  
Dinesh Kumar ◽  
Ashok Bhardwaj ◽  
Shruti Sharma ◽  
Bhavya Malhotra ◽  
Chioma Amadi ◽  
...  
Keyword(s):  
Decision Support ◽  
Data Collection ◽  
Decision Support System ◽  
Support System ◽  
Diabetes Management ◽  
Intervention Group ◽  
Control Group ◽  
Diet Record ◽  
Study Participants

BACKGROUND Human-centered dietary decision support systems are fundamental to diabetes management, and they address limitations of existing diet management systems. OBJECTIVE The objective of the proposed study is to evaluate the use of an interactive telephone-linked Personalized Human Centered Decision Support System for facilitating the delivery of personalized nutrition care for diabetic patients METHODS A Quasi-experimental trial was conducted between the period of June and December, 2018. Study participants were recruited from: Community Health Center, Dharamshala, Kangra (urban population); and Model Rural Health Unit, Haroli Block, Una (rural population). Eligible participants included: adults aged 30 years and above; having both controlled and uncontrolled diabetes; agreeing to participate in the study; available for follow-up interview; and having telephone or computer at home. Diabetic status assessed by physician diagnosis. Individuals with mental or physical challenges affecting their ability to use an electronic diet record, those who were not available for a telephone follow-up, or involved in other protocols related to dietary assessments, were excluded. The study participants were randomized into two groups: Intervention (Telephone-linked Dietary Decision Support System); and Control group (Paper-based diet record). Study participants in the intervention group recorded their daily dietary intake using a telephone-linked Personalized Human Centered Dietary Decision Support System (PHCDDSS), and also received personalized feedback/diet education via SMS. Study participants in the control group were provided with only a paper-based diet record for documenting their daily dietary intake. Follow-up visits were conducted at months 3 and 6 from the baseline, in both groups. Differences in diabetes knowledge, attitudes and practices (KAP) will be measured across groups. RESULTS Baseline data collection is now completed. Follow up data collection for months 3 and 6 is ongoing, and is expected to be completed by October, 2019. CONCLUSIONS We anticipate that the intervention group will show a significant change in nutrition knowledge, attitudes and practices (KAP), satisfaction with care, and overall diabetes management. We also expect to see urban rural-differences across the groups. The uniqueness of our nutrient data capture process is demonstrated through its cultural and contextually relevant features: diet capture in both English and Hindi, diet conversion into its caloric components, sustained diet data collection and participant adherence through telephone-linked care, and auto-generated reminders. CLINICALTRIAL Not registered

scholarly journals A digital decision support system (selfBACK) for improved self-management of low back pain: a pilot study with 6-week follow-up

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Louise Fleng Sandal ◽  
Cecilie K. Øverås ◽  
Anne Lovise Nordstoga ◽  
Karen Wood ◽  
Kerstin Bach ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Decision Support ◽  
Pilot Study ◽  
Decision Support System ◽  
Support System ◽  
Self Management ◽  
Low Back

FRAMEWORK OF A DECISION SUPPORT SYSTEM FOR APPLYING PROGRAM-FOR-RESULTS FUNDING MECHANISM

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Kareem Zahran ◽  
A. Samer Ezeldin
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Decision Makers ◽  
Executive Directors ◽  
Funding Mechanism ◽  
The World ◽  
Financing Mechanism ◽  
The Government

Following the introduction of the Program-For-Results funding mechanism, in year 2012, The World Bank’s Board of Executive Directors requested a follow-up review of the instrument to be performed two years later. A report titled “Program-For-Results: Two-Year Review” was issued by the operations policy and country services department in the World Bank. One of the main conclusions of the report is the lack of knowledge and experience of the stakeholders involved in the application of the Program-For-Results (P4R) funding mechanism. This leads to the need for further guidance and support for future P4R operations. This research aims to fill the gap between any in-pipeline programs planned to be financed through P4R and previous experiences within the same sector or financing mechanism. It is planned to summarize all P4R experiences to-date and other sectoral experiences in a detailed framework that guides new governments through the life-cycle of P4R. This is achieved by developing the main framework of a Decision Support System (DSS) that guides governments/decision makers throughout the lifetime of the P4R program. It provides guidance for the government in each stage of the P4R starting from the borrowing preparation stage up to the closing stage.

scholarly journals Supporting care for suboptimally controlled type 2 diabetes mellitus in general practice with a clinical decision support system: a mixed methods pilot cluster randomised trial

BMJ Open ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. e032594
Author(s):  
Mark E Murphy ◽  
Jenny McSharry ◽  
Molly Byrne ◽  
Fiona Boland ◽  
Derek Corrigan ◽  
...  
Keyword(s):  
Blood Pressure ◽  
General Practice ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Randomised Trial ◽  
Clinical Decision

ObjectivesWe developed a complex intervention called DECIDE (ComputeriseD dECisIonal support for suboptimally controlleD typE 2 Diabetes mellitus in Irish General Practice) which used a clinical decision support system to address clinical inertia and support general practitioner (GP) intensification of treatment for adults with suboptimally controlled type2 diabetes mellitus (T2DM). The current study explored the feasibility and potential impact of DECIDE.DesignA pilot cluster randomised controlled trial.SettingConducted in 14 practices in Irish General Practice.ParticipantsThe DECIDE intervention was targeted at GPs. They applied DECIDE to patients with suboptimally controlled T2DM, defined as a glycated haemoglobin (HbA1c) ≥70 mmol/mol and/or blood pressure ≥150/95 mmHg.InterventionThe intervention incorporated training and a web-based clinical decision support system which supported; (i) medication intensification actions; and (ii) non-pharmacological actions to support care. Control practices delivered usual care.Primary and secondary outcome measuresFeasibility and acceptability was determined using thematic analysis of semi-structured interviews with GPs, combined with data from the DECIDE website. Clinical outcomes included HbA1c, medication intensification, blood pressure and lipids.ResultsWe recruited 14 practices and 134 patients. At 4-month follow-up, all practices and 114 patients were followed up. GPs reported finding decision support helpful navigating increasingly complex medication algorithms. However, the majority of GPs believed that the target patient group had poor engagement with GP and hospital services for a range of reasons. At follow-up, there was no difference in glycaemic control (−3.6 mmol/mol (95% CI −11.2 to 4.0)) between intervention and control groups or in secondary outcomes including, blood pressure, total cholesterol, medication intensification or utilisation of services. Continuation criteria supported proceeding to a definitive randomised trial with some modifications.ConclusionThe DECIDE study was feasible and acceptable to GPs but wider impacts on glycaemic and blood pressure control need to be considered for this patient population going forward.Trial registration numberISRCTN69498919

scholarly journals A decision support system to follow up and diagnose primary headache patients using semantically enriched data

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Gilles Vandewiele ◽  
Femke De Backere ◽  
Kiani Lannoye ◽  
Maarten Vanden Berghe ◽  
Olivier Janssens ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Primary Headache ◽  
Headache Patients

scholarly journals Determination of Minimum Data Set for Designing a Diagnosis Decision Support System and Medication Follow-Up for Multiple Sclerosis

2020 ◽  
Author(s):  
Sahar Khenarinezhad ◽  
Niloofar Mohammadzadeh ◽  
Marjan GhaziSaeedi ◽  
Abdorreza NaserMoghadasi
Keyword(s):  
Multiple Sclerosis ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Medical Training ◽  
Cross Sectional ◽  
Data Set ◽  
History Of ◽  
Minimum Dataset

Background and Purpose: Diagnosis of multiple sclerosis (MS) is complicated because of the lack of definite factor. Decision support systems are expert systems which help physicians in decision-making process. First step in designing the system is identification of a minimum dataset (MDS). This study aimed to determine minimum dataset required to design diagnosis decision support system.Materials and Methods: This research was a descriptive cross-sectional study. Data were gathered from medical guideline approved by Ministry of Health, Treatment and Medical Training, Multiple Sclerosis diagnosis, international guideline of Royal college of England, and McDonald Diagnostic criteria. Data collection tool was a designed checklist consisting of 100 items provided to 25 neurologists and MS fellowships of medical universities and private clinics in Iran.Results: Out of 100 designed information’s items, 10 items were omitted due to CVR less than 0.49. Employment status items, history of MS in 3rd grade relatives, history of viral diseases, orbital MRI, optical coherence tomography, brain CT-scan, ESR, CRP, visually evoked potentials, delay duration of P100 for each eyes are all examples of information elements that have been omitted.Conclusion: Determining the minimum dataset related to MS is an important step in designing diagnosis decision support system and medication follow-up. Therefore, MDSs can help those responsible for gathering standard information of patients with Multiple Sclerosis (MS), and causes improvement in management of information for this disease.

scholarly journals Decision Support System Determines The Quality Of The House In Pringsewu District Using The WASPAS Method

Tech-E ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 16
Author(s):  
Elisabet Yunaeti Anggraeni ◽  
Riki Renaldo ◽  
Tri Susilowati
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Assessment Method ◽  
Calculation Results ◽  
Product Assessment ◽  
And Behavior ◽  
Housing Facilities

The quality of the Flats is a concern of every citizen and related health offices who need healthy flats. To determine the quality of a flat is declared healthy or unhealthy, three criteria are used, including: housing components, housing facilities, and behavior. In this study, 5 samples were used to determine the quality of the flats, from the calculation results obtained 2 houses that were declared healthy and 3 houses were declared unhealthy. The WASPAS method is applied to determine the quality priority of healthy flats. In this study, researchers used several criteria including area, environment, infrastructure, location, security. The alternatives in this study are, Flat A, Flat B, Flat C, Flat D and Flat E. From these calculations, the results obtained from the analysis determine a healthy apartment using the WASPAS (Weight Aggregated Sum Product Assessment) method. The following: 1. Flat A gets a score of 6.0144, 2. Flat B gets a score of 6.0117, 3. Flat C gets a score of 5.5871, 4. Flat E gets a score of 5.1253. 5. Apartment D received a score of 4,8253.5. From the calculation results, the WASPAS method can be an input for the related health offices to follow up on the priority of healthy flats so that it is easier for people to choose healthy flats.

scholarly journals Penilaian Prestasi Kerja Karyawan PTPN III Pematangsiantar Dengan Metode Simple Additive Weighting (SAW)

2017 ◽  
Vol 2 (1) ◽  
pp. 84 ◽  
Author(s):  
Agus Perdana Windarto
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Performance Appraisal ◽  
Support System ◽  
Employee Performance ◽  
Objective Assessment ◽  
Sample Data ◽  
Simple Additive Weighting

Performance appraisal of employees is absolutely done to know the achievements that have been achieved every employee. With achievement appraisal means subordinates get attention from the topics to encourage their passionate work, with reference to the process of honest and objective assessment and there follow-up. Follow-up appraisals enable employees to be promoted, demotivated, cultivated, and rewarded for service. The purpose of this study is to make a decision support system employee performance appraisal employee used to increase the class. This decision support system uses Simple Additive Weighting (SAW) Method. Where the criteria used are Work Attitude Employees, How Employees Work, Employees Work and Employee Duty Day. The sample data used is 5 with different weights on each criterion. This study took a case study of PTPN III Pematangsiantar with the calculation that 0.90 has the highest value according to SAW calculation. The value is in the name of Mr. Syamsul Arifin, SE. Therefore this method can be used to assess employees by entering actual data in the system created and can solve the selection problem in multi-process decision making.

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