scholarly journals Design and implementation of an intelligent framework for supporting evidence-based treatment recommendations in precision oncology

2020 ◽  
Author(s):  
Frank P.Y. Lin
Keyword(s):  
Decision Making ◽  
Recommendation System ◽  
Unmet Need ◽  
Treatment Recommendations ◽  
Treatment Recommendation ◽  
Evidence Based ◽  
Supporting Evidence ◽  
Precision Oncology ◽  
Sequential Decision ◽  
Evidence Based Treatment

AbstractBACKGROUNDThe advances in genome sequencing technologies have provided new opportunities for delivering targeted therapy to patients with advanced cancer. However, these high-throughput assays have also created a multitude of challenges for oncologists in treatment selection, demanding a new approach to support decision-making in clinics.METHODSTo address this unmet need, this paper describes the design of a symbolic reasoning framework using the method of hierarchical task analysis. Based on this framework, an evidence-based treatment recommendation system was implemented for supporting decision-making based on a patient’s clinicopathologic and biomarker profiles.RESULTSThis intelligent framework captures a six-step sequential decision process: (1) concept expansion by ontology matching, (2) evidence matching, (3) evidence grading and value-based prioritisation, (4) clinical hypothesis generation, (5) recommendation ranking, and (6) recommendation filtering. The importance of balancing evidence-based and hypothesis-driven treatment recommendations is also highlighted. Of note, tracking history of inference has emerged to be a critical step to allow rational prioritisation of recommendations. The concept of inference tracking also enables the derivation of a novel measure — level of matching — that helps to convey whether a treatment recommendation is drawn from incomplete knowledge during the reasoning process.CONCLUSIONSThis framework systematically encapsulates oncologist’s treatment decisionmaking process. Further evaluations in prospective clinical studies are warranted to demonstrate how this computational pipeline can be integrated into oncology practice to improve outcomes.

scholarly journals Conformance to Evidence-Based Treatment Recommendations in Schizophrenia Treatment Services

2012 ◽  
Vol 57 (5) ◽  
pp. 317-323 ◽  
Author(s):  
Donald Addington ◽  
Emily McKenzie ◽  
Harvey Smith ◽  
Henry Chuang ◽  
Stephen Boucher ◽  
...  
Keyword(s):  
Treatment Recommendations ◽  
Evidence Based ◽  
Evidence Based Treatment ◽  
Treatment Services

scholarly journals Authors’ response to “Tranexamic acid and uremic bleeding: evidence-based treatment recommendations”

2007 ◽  
Vol 3 (6) ◽  
pp. E3-E3 ◽  
Author(s):  
Stephanie J Hedges ◽  
Sarah B Dehoney ◽  
Justin S Hooper ◽  
Jamshid Amanzadeh ◽  
Anthony J Busti
Keyword(s):  
Tranexamic Acid ◽  
Treatment Recommendations ◽  
Evidence Based ◽  
Evidence Based Treatment

Abstract P214: Patient and Physician Factors Influence Decision-Making in Hypercholesterolemia

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Michel Krempf ◽  
Ross J Simpson ◽  
Dena R Ramey ◽  
Philippe Brudi ◽  
Hilde Giezek ◽  
...  
Keyword(s):  
Decision Making ◽  
Goal Attainment ◽  
Clinical Decision Making ◽  
Treatment Strategies ◽  
Treatment Decision ◽  
Clinical Decision ◽  
Treatment Recommendations ◽  
Treatment Choice ◽  
Treatment Recommendation ◽  
Double Blind

Objectives: Little is known about how patient factors influence physicians’ treatment decision-making in hypercholesterolemia. We surveyed physicians’ treatment recommendations in high-risk patients with LDL-C not controlled on statin monotherapy. Methods: Physicians completed a questionnaire pre-randomization for each patient in a double-blind trial (NCT01154036) assessing LDL-C goal attainment rates with different treatment strategies. Patients had LDL-C ≥100 mg/dL after 5 weeks’ atorvastatin 10 mg/day and before randomization. Physicians were asked about treatment recommendations for three scenarios: (1) LDL-C near goal (100-105 mg/dL), (2) LDL-C far from goal (120 mg/dL), then (3) known baseline LDL-C of enrolled patients on atorvastatin 10 mg/day. Factors considered in their choice were specified. Physicians had been informed of projected LDL-C reductions for each treatment strategy in the trial. Regression analysis identified prognostic factors associated with each scenario, and projected LDL-C values for physicians’ treatment choices were compared to actual LDL-C values achieved in the trial. Results: Physicians at 296 sites completed questionnaires for 1535 patients. The most common treatment strategies for all three scenarios were: 1) not to change therapy, 2) double atorvastatin dose, 3) add ezetimibe, 4) double atorvastatin dose and add ezetimibe. Doubling atorvastatin dose was the most common treatment recommendation in all scenarios (43-52% of patients). ‘No change in therapy’ was recommended in 6.5% of patients when LDL-C was assumed far from goal. Treatment recommendations were more aggressive if actual LDL-C was known or considered far from goal. When compared with the ‘no change in therapy’ recommendation, CV risk factors and desire to achieve a more aggressive LDL-C goal were generally considered in decision-making for each treatment choice, regardless of LDL-C scenario. Patients randomized to a more aggressive regimen than recommended by physicians had larger reductions in LDL-C: the actual reduction in LDL-C in patients randomized to ‘add ezetimibe’ was -20.8% versus a projected reduction of -10.0% when physicians recommended ‘doubling atorvastatin dose’. Conclusions: This study provides insight into physicians’ perspectives on clinical management of hypercholesterolemia and highlights a gap in knowledge translation from guidelines to clinical practice. Targeting lower LDL-C and CV risk were key drivers in clinical decision-making but, generally, physicians were more conservative in their treatment choice than guidelines recommend, which may result in poorer LDL-C reduction. When compared with actual outcomes, projected LDL-C control was better if physicians used more comprehensive strategies rather than simply doubling the statin dose.

scholarly journals The Juggling Act of Supervision in Community Mental Health: Implications for Supporting Evidence-Based Treatment

2017 ◽  
Vol 44 (6) ◽  
pp. 838-852 ◽  
Author(s):  
Shannon Dorsey ◽  
Michael D. Pullmann ◽  
Suzanne E. U. Kerns ◽  
Nathaniel Jungbluth ◽  
Rosemary Meza ◽  
...  
Keyword(s):  
Mental Health ◽  
Community Mental Health ◽  
Evidence Based ◽  
Supporting Evidence ◽  
Evidence Based Treatment ◽  
Health Implications

Early experience with IBM Watson for Oncology (WFO) cognitive computing system for lung and colorectal cancer treatment.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 8527-8527 ◽  
Author(s):  
S.P. Somashekhar ◽  
Martín-J. Sepúlveda ◽  
Andrew D Norden ◽  
Amit Rauthan ◽  
Kumar Arun ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Rectal Cancer ◽  
Computing System ◽  
Treatment Recommendations ◽  
Evidence Based ◽  
Metastatic Colon Cancer ◽  
Cognitive Computing ◽  
Evidence Based Treatment ◽  
Tumour Board ◽  
Ibm Watson

8527 Background: IBM Watson for Oncology is an artificial intelligence cognitive computing system that provides confidence-ranked, evidence-based treatment recommendations for cancer. In the present study, we examine the level of agreement for lung and colorectal cancer therapy between the multidisciplinary tumour board from Manipal Comprehensive Cancer Centre in Bangalore, India, and Watson for Oncology. Methods: Watson for Oncology is a Memorial Sloan Kettering Cancer Center (New York, USA) trained cognitive computing system that uses natural language processing and machine learning to provide treatment recommendations. It processes structured and unstructured data from medical literature, treatment guidelines, medical records, imaging, lab and pathology reports, and the expertise of Memorial Sloan Kettering experts to formulate therapeutic recommendations. Treatment recommendations are provided in three categories: recommended, for consideration and not recommended. In this report we provide the results of the independent and blinded evaluation by the multidisciplinary tumour board and Watson for Oncology of 362 total cancer cases comprised of 112 lung, 126 colon and 124 rectal cancers seen at the Centre within the last three years. The recommendations of the two agents were compared for agreement and considered concordant when the tumour board recommendation was included in the recommended or for consideration categories of the treatment advisor. Results: Overall, treatment recommendations were concordant in 96.4% of lung, 81.0% of colon and 92.7% of rectal cancer cases. By tumour stage, treatment recommendations were concordant in 88.9% of localized and 97.9% of metastatic lung cancer, 85.5% of localized and 76.6% of metastatic colon cancer, and 96.8% of localized and 80.6% of metastatic rectal cancer. Conclusions: Treatment recommendations made by the Manipal multidisciplinary tumour board and Watson for Oncology were highly concordant in the cancers examined. This cognitive computing technology holds much promise in helping oncologists make information intensive, evidence based treatment decisions.

scholarly journals Diabetic macular oedema: evidence-based treatment recommendations for Asian countries

2017 ◽  
Vol 46 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Gemmy CM Cheung ◽  
Young Hee Yoon ◽  
Lee Jen Chen ◽  
Shih Jen Chen ◽  
Tara M George ◽  
...  
Keyword(s):  
Macular Oedema ◽  
Diabetic Macular Oedema ◽  
Treatment Recommendations ◽  
Evidence Based ◽  
Asian Countries ◽  
Evidence Based Treatment

Mental Health Providers’ Decision-Making Around the Implementation of Evidence-Based Treatment for PTSD

2016 ◽  
Vol 44 (2) ◽  
pp. 213-223 ◽  
Author(s):  
Princess E. Osei-Bonsu ◽  
Rendelle E. Bolton ◽  
Shannon Wiltsey Stirman ◽  
Susan V. Eisen ◽  
Lawrence Herz ◽  
...  
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Evidence Based ◽  
Mental Health Providers ◽  
Health Providers ◽  
Evidence Based Treatment
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