Pathways to radiomics-aided clinical decision-making for precision medicine

2019 ◽  
pp. 193-201
Author(s):  
Tianye Niu ◽  
Xiaoli Sun ◽  
Pengfei Yang ◽  
Guohong Cao ◽  
Khin K. Tha ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision

The Johns Hopkins Molecular Tumor Board Precision Oncology elective for Medical Oncology fellows.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 11035-11035
Author(s):  
Kristen Marrone ◽  
Jessica Tao ◽  
Jenna VanLiere Canzoniero ◽  
Paola Ghanem ◽  
Emily Nizialek ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Cancer Genomics ◽  
Medical Oncology ◽  
Clinical Decision ◽  
Tumor Board ◽  
Precision Oncology ◽  
Adaptive Expertise ◽  
Molecular Tumor Board

11035 Background: The accelerated impact of next generation sequencing (NGS) in clinical decision making requires the integration of cancer genomics and precision oncology focused training into medical oncology education. The Johns Hopkins Molecular Tumor Board (JH MTB) is a multi-disciplinary effort focused on integration of NGS findings with critical evidence interpretation to generate personalized recommendations tailored to the genetic footprint of individual patients. Methods: The JH MTB and the Medical Oncology Fellowship Program have developed a 3-month precision oncology elective for fellows in their research years. Commencing fall of 2020, the goals of this elective are to enhance the understanding of NGS platforms and findings, advance the interpretation and characterization of molecular assay outputs by use of mutation annotators and knowledgebases and ultimately master the art of matching NGS findings with available therapies. Fellow integration into the MTB focuses on mentored case-based learning in mutation characterization and ranking by levels of evidence for actionability, with culmination in form of verbal presentations and written summary reports of final MTB recommendations. A mixed methods questionnaire was administered to evaluate progress since elective initiation. Results: Three learners who have participated as of February 2021 were included. Of the two who had completed the MTB elective, each have presented at least 10 cases, with at least 1 scholarly publication planned. All indicated strong agreement that MTB elective had increased their comfort with interpreting clinical NGS reports as well as the use of knowledgebases and variant annotators. Exposure to experts in the field of molecular precision oncology, identification of resources necessary to interpret clinical NGS reports, development of ability to critically assess various NGS platforms, and gained familiarity with computational analyses relevant to clinical decision making were noted as strengths of the MTB elective. Areas of improvement included ongoing initiatives that involve streamlining variant annotation and transcription of information for written reports. Conclusions: A longitudinal elective in the JHU MTB has been found to be preliminarily effective in promoting knowledge mastery and creating academic opportunities related to the clinical application of precision medicine. Future directions will include leveraging of the MTB infrastructure for research projects, learner integration into computational laboratory meetings, and expansion of the MTB curriculum to include different levels of learners from multiple medical education programs. Continued elective participation will be key to understanding how best to facilitate adaptive expertise in assigning clinical relevance to genomic findings, ultimately improving precision medicine delivery in patient care and trial development.

scholarly journals Patient attrition in Molecular Tumour Boards: A Review

2021 ◽  
Author(s):  
Hannah Frost ◽  
Donna M. Graham ◽  
Louise Carter ◽  
Paul O’Regan ◽  
Donal Landers ◽  
...  
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
New Technologies ◽  
Clinical Decision ◽  
Clinical Deterioration ◽  
Treatment Data ◽  
Patient Attrition ◽  
Receive Treatment

AbstractMolecular Tumour Boards (MTBs) were created with the purpose of supporting clinical decision making within precision medicine. Though these meetings are in use globally reporting often focuses on the small percentages of patients that receive treatment via this process and are less likely to report on, and assess, patients who do not receive treatment. A literature review was performed to understand patient attrition within MTBs and barriers to patients receiving treatment. A total of 56 papers were reviewed spanning a 6 year period from 11 different countries. 20% of patients received treatment through the MTB process. Of those that did not receive treatment the main reasons were no mutations identified (26%), no actionable mutations (22%) and clinical deterioration (15%). However, the data was often incomplete due to inconsistent reporting of MTBs with only 54% reporting on patients having no mutations, 48% reporting on presence of actionable mutations and 57% reporting on clinical deterioration. Patient attrition in MTBs is an issue which is very rarely alluded to in reporting, more transparent reporting is needed to understand barriers to treatment and integration of new technologies is required to process increasing omic and treatment data.

scholarly journals Automatic Identification of High Impact Relevant Articles to Support Clinical Decision Making Using Attention-Based Deep Learning

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1364
Author(s):  
Beomjoo Park ◽  
Muhammad Afzal ◽  
Jamil Hussain ◽  
Asim Abbas ◽  
Sungyoung Lee
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
High Impact ◽  
Biomedical Literature ◽  
Classification Model ◽  
Evidence Based ◽  
Proposed Model

To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impact relevant articles, we propose a novel approach of attention-based deep learning for finding and ranking relevant studies against a topic of interest. For learning the proposed model, we collect data consisting of 240,324 clinical articles from the 2018 Precision Medicine track in Text REtrieval Conference (TREC) to identify and rank relevant documents matched with the user query. We built a BERT (Bidirectional Encoder Representations from Transformers) based classification model to classify high and low impact articles. We contextualized word embedding to create vectors of the documents, and user queries combined with genetic information to find contextual similarity for determining the relevancy score to rank the articles. We compare our proposed model results with existing approaches and obtain a higher accuracy of 95.44% as compared to 94.57% (the next best performer) and get a higher precision by about 14% at P@5 (precision at 5) and about 12% at P@10 (precision at 10). The contextually viable and competitive outcomes of the proposed model confirm the suitability of our proposed model for use in domains like evidence-based precision medicine.

scholarly journals The Need for Precision Medicine to be Applied to Diabetes

2020 ◽  
Vol 14 (6) ◽  
pp. 1122-1128 ◽  
Author(s):  
David C. Klonoff ◽  
Jose C. Florez ◽  
Michael German ◽  
Alexander Fleming
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Gene Sequencing ◽  
Clinical Decision ◽  
Management Decision ◽  
Disease Course ◽  
Economic Costs ◽  
Management Decision Making ◽  
Clinical Management Decision

Precision medicine refers to the tailoring of medical treatment for an individual based on large amounts of biologic and extrinsic data. The fast advancing fields of molecular biology, gene sequencing, machine learning, and other technologies enable precision medicine to utilize this detailed information to enhance clinical management decision-making for an individual in the real time of the disease course. Traditional clinical decision making is based on reacting to a relatively limited number of phenotypes that are determined by history, physical examination, and conventional lab tests. Precision medicine depends on highly detailed profiling of the patient’s genetic, morphologic, and metabolic makeup. The precision medicine approach can be applied to individuals with diabetes to select treatments most likely to offer benefit and least likely to cause side effects, offering prospects of improved clinical outcomes and economic costs savings over current empiric practices. As genetic, metabolomic, immunologic, and other sophisticated testing becomes less expensive and more widespread in the medical record, it is expected that precision medicine will become increasingly applied to diabetes care.

scholarly journals Personalised organs-on-chips: functional testing for precision medicine

Lab on a Chip ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 198-205 ◽  
Author(s):  
Albert van den Berg ◽  
Christine L. Mummery ◽  
Robert Passier ◽  
Andries D. van der Meer
Keyword(s):  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Functional Testing ◽  
Functional Tests

Organs-on-chips can be ‘personalised’ so they can be used as functional tests to inform clinical decision-making for specific patients.

scholarly journals Pathway centered analysis to guide clinical decision-making in precision medicine

2021 ◽  
Author(s):  
Luís B. Carvalho ◽  
J. L. Capelo ◽  
Carlos Lodeiro ◽  
Rajiv Dhir ◽  
Luis Campos Pinheiro ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Care ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Specific Information ◽  
Disease Treatment ◽  
The Individual ◽  
Disease Specific ◽  
Time Point

AbstractChanges in the human proteome caused by disease before, during and after medical care is phenotype-dependent, so the proteome of each individual at any time point is a snapshot of the body’s response to disease and to disease treatment. Here, we introduce a new concept named differential Personal Pathway index (dPPi). This tool extracts and summates comprehensive disease-specific information contained within an individual’s proteome as a holistic way to follow the response to disease and medical care over time. We demonstrate the principle of the dPPi algorithm on proteins found in urine from patients suffering from neoplasia of the bladder. The relevance of the dPPi results to the individual clinical cases is described. The dPPi concept can be extended to other malignant and non-malignant diseases, and to other types of biopsies, such as plasma, serum or saliva. We envision the dPPi as a tool for clinical decision-making in precision medicine.

scholarly journals Emerging Innovations and Professional Skills Needed Within Pharmacy Curricula

2020 ◽  
Vol 7 ◽  
pp. 238212052094359 ◽  
Author(s):  
Amanda A Olsen ◽  
Lana M Minshew ◽  
Kathryn A Morbitzer ◽  
Tina P Brock ◽  
Jacqueline E McLaughlin
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Interprofessional Collaboration ◽  
Digital Health ◽  
Pharmacy Practice ◽  
Clinical Decision ◽  
Professional Skills ◽  
Wide Range

To ensure students are prepared for the rapidly evolving world of health care, curricula must be aligned with emerging innovations, as well as professional skills likely to influence students’ abilities to be successful. At the 2019 annual meeting of PharmAlliance institutions, we asked experts to identify innovations and professional skills necessary for the future of pharmacy practice. Experts identified a wide range of topics, including personalized and precision medicine, digital health, interprofessional collaboration, clinical decision making, and overcoming complexity and ambiguity. While these findings are useful for informing curriculum content, we must also commit to ensuring our pharmacy curricula are emerging, forward thinking, and effective at preparing students for the challenges in health care.

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