Bessen et al., 2014 - Google Patents
A patient-level calibration framework for evaluating surveillance strategies: a case study of mammographic follow-up after early breast cancerBessen et al., 2014
View HTML- Document ID
- 9124892314008046680
- Author
- Bessen T
- Karnon J
- Publication year
- Publication venue
- Value in Health
External Links
Snippet
Objective Currently all women who have completed their primary treatment for early breast cancer are invited to receive routine annual mammography. There is no randomized controlled trial evidence to support this schedule, and model-based analysis is required …
- 206010006187 Breast cancer 0 title abstract description 97
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
- G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/3437—Medical simulation or modelling, e.g. simulating the evolution of medical disorders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/3431—Calculating a health index for the patient, e.g. for risk assessment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
- G06Q50/24—Patient record management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/36—Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Bur et al. | Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma | |
Briggs et al. | Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group–6 | |
Hui et al. | Clinician prediction of survival versus the Palliative Prognostic Score: which approach is more accurate? | |
Henry et al. | The joint effects of census tract poverty and geographic access on late-stage breast cancer diagnosis in 10 US States | |
Fehniger et al. | Perceived versus objective breast cancer risk in diverse women | |
Sathianathen et al. | Cost-effectiveness analysis of active surveillance strategies for men with low-risk prostate cancer | |
Lin et al. | Comparing the benefits of screening for breast cancer and lung cancer using a novel natural history model | |
Virani et al. | Escalating burden of breast cancer in southern Thailand: analysis of 1990–2010 incidence and prediction of future trends | |
Samson et al. | Defining the ideal time interval between planned induction therapy and surgery for stage IIIA non-small cell lung cancer | |
Kendel et al. | Patients' perceptions of mortality risk for localized prostate cancer vary markedly depending on their treatment strategy | |
Armero et al. | Bayesian joint ordinal and survival modeling for breast cancer risk assessment | |
Janes et al. | The 17-gene genomic prostate score test is prognostic for outcomes after primary external beam radiation therapy in men with clinically localized prostate cancer | |
Bessen et al. | A patient-level calibration framework for evaluating surveillance strategies: a case study of mammographic follow-up after early breast cancer | |
Patrikidou et al. | Helping patients make informed decisions. Two-year evaluation of the Gustave Roussy prostate cancer multidisciplinary clinic | |
Tomer et al. | Shared decision making of burdensome surveillance tests using personalized schedules and their burden and benefit | |
Allen Jr et al. | Downstream imaging utilization after emergency department ultrasound interpreted by radiologists versus nonradiologists: a Medicare claims–based study | |
Bessen et al. | Does one size fit all? Cost utility analyses of alternative mammographic follow-up schedules, by risk of recurrence | |
Lee et al. | Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity | |
Stevenson | Statistical models for cancer screening | |
Vale et al. | Optimal surveillance strategies for patients with stage 1 cutaneous melanoma post primary tumour excision: three systematic reviews and an economic model | |
Meys et al. | Economic evaluation of an expert examiner and different ultrasound models in the diagnosis of ovarian cancer | |
Edmonds et al. | A mixed method approach to examine surveillance mammography experiences in Black and White breast cancer survivors | |
Elhefnawy et al. | Predictor naïve temporal baseline hazard of recurrent ischemic stroke | |
Wood et al. | Postpartum uptake of diabetes screening tests in women with gestational diabetes: The PANDORA study | |
Wu et al. | Inference of long-term screening outcomes for individuals with screening histories |