scholarly journals Data‐driven prediction of biologic treatment responses in psoriasis: steps towards precision medicine

2021 ◽  
Author(s):  
L. S. Schoot ◽  
J. M. P. A. Reek
Keyword(s):  
Precision Medicine ◽  
Data Driven ◽  
Biologic Treatment ◽  
Treatment Responses

Cancer Biomarker Discovery for Precision Medicine: New Progress

2020 ◽  
Vol 26 (42) ◽  
pp. 7655-7671 ◽  
Author(s):  
Jinfeng Zou ◽  
Edwin Wang
Keyword(s):  
Precision Medicine ◽  
Cancer Patients ◽  
Clinical Application ◽  
Liquid Biopsy ◽  
Biomarker Discovery ◽  
Deep Understanding ◽  
Genetic Mutation ◽  
Data Driven ◽  
Novel Approaches ◽  
Functional Biomarkers

Background: Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued. Objective: This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine. Results: The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients’ survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients’ survival. Conclusion: Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.

Precision Medicine, Data-Driven Diagnosis and Treatment

2017 ◽  
pp. 15-34
Author(s):  
Harry Glorikian ◽  
Malorye Allison Branca
Keyword(s):  
Precision Medicine ◽  
Data Driven ◽  
Diagnosis And Treatment

scholarly journals Precision Medicine

2019 ◽  
Vol 6 (1) ◽  
pp. 263-286 ◽  
Author(s):  
Michael R. Kosorok ◽  
Eric B. Laber
Keyword(s):  
Health Care ◽  
Precision Medicine ◽  
Decision Rules ◽  
Data Driven ◽  
Care Process ◽  
Treatment Regimes ◽  
Specific Diet ◽  
Recommended Action ◽  
Selection Of

Precision medicine seeks to maximize the quality of health care by individualizing the health-care process to the uniquely evolving health status of each patient. This endeavor spans a broad range of scientific areas including drug discovery, genetics/genomics, health communication, and causal inference, all in support of evidence-based, i.e., data-driven, decision making. Precision medicine is formalized as a treatment regime that comprises a sequence of decision rules, one per decision point, which map up-to-date patient information to a recommended action. The potential actions could be the selection of which drug to use, the selection of dose, the timing of administration, the recommendation of a specific diet or exercise, or other aspects of treatment or care. Statistics research in precision medicine is broadly focused on methodological development for estimation of and inference for treatment regimes that maximize some cumulative clinical outcome. In this review, we provide an overview of this vibrant area of research and present important and emerging challenges.

scholarly journals Precision Neurosurgery: A Path Forward

2021 ◽  
Vol 11 (10) ◽  
pp. 1019
Author(s):  
Vianney Gilard ◽  
Stéphane Derrey ◽  
Stéphane Marret ◽  
Soumeya Bekri ◽  
Abdellah Tebani
Keyword(s):  
Precision Medicine ◽  
Data Analytics ◽  
Historical Perspective ◽  
Molecular Profiling ◽  
Data Driven ◽  
Medical Community ◽  
Technological Advances ◽  
Conceptual Shift ◽  
The Impact ◽  
Patient Centric

Since the inception of their profession, neurosurgeons have defined themselves as physicians with a surgical practice. Throughout time, neurosurgery has always taken advantage of technological advances to provide better and safer care for patients. In the ongoing precision medicine surge that drives patient-centric healthcare, neurosurgery strives to effectively embrace the era of data-driven medicine. Neuro-oncology best illustrates this convergence between surgery and precision medicine with the advent of molecular profiling, imaging and data analytics. This convenient convergence paves the way for new preventive, diagnostic, prognostic and targeted therapeutic perspectives. The prominent advances in healthcare and big data forcefully challenge the medical community to deeply rethink current and future medical practice. This work provides a historical perspective on neurosurgery. It also discusses the impact of the conceptual shift of precision medicine on neurosurgery through the lens of neuro-oncology.

scholarly journals A quantum-enhanced precision medicine application to support data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis: development and preliminary validation of precisionKNEE QNN

2021 ◽  
Author(s):  
Stefano Olgiati ◽  
Nima Heidari ◽  
Davide Meloni ◽  
Federico Pirovano ◽  
Ali Noorani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Complexity ◽  
Knee Osteoarthritis ◽  
Precision Medicine ◽  
Real World ◽  
Data Driven ◽  
Personalized Treatment ◽  
Validation Dataset ◽  
Clinical Decisions ◽  
Quantum Machine Learning

Background Quantum computing (QC) and quantum machine learning (QML) are promising experimental technologies which can improve precision medicine applications by reducing the computational complexity of algorithms driven by big, unstructured, real-world data. The clinical problem of knee osteoarthritis is that, although some novel therapies are safe and effective, the response is variable, and defining the characteristics of an individual who will respond remains a challenge. In this paper we tested a quantum neural network (QNN) application to support precision data-driven clinical decisions to select personalized treatments for advanced knee osteoarthritis. Methods Following patients consent and Research Ethics Committee approval, we collected clinico-demographic data before and after the treatment from 170 patients eligible for knee arthroplasty (Kellgren-Lawrence grade ≥ 3, OKS ≤ 27, Age ≥ 64 and idiopathic aetiology of arthritis) treated over a 2 year period with a single injection of microfragmented fat. Gender classes were balanced (76 M, 94 F) to mitigate gender bias. A patient with an improvement ≥ 7 OKS has been considered a Responder. We trained our QNN Classifier on a randomly selected training subset of 113 patients to classify responders from non-responders (73 R, 40 NR) in pain and function at 1 year. Outliers were hidden from the training dataset but not from the validation set. Results We tested our QNN Classifier on a randomly selected test subset of 57 patients (34 R, 23 NR) including outliers. The No Information Rate was equal to 0.59. Our application correctly classified 28 Responders out of 34 and 6 non-Responders out of 23 (Sensitivity = 0.82, Specificity = 0.26, F1 Statistic= 0.71). The Positive (LR+) and Negative (LR-) Likelihood Ratios were respectively 1.11 and 0.68. The Diagnostic Odds Ratio (DOR) was equal to 2. Conclusions Preliminary results on a small validation dataset show that quantum machine learning applied to data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis is a promising technology to reduce computational complexity and improve prognostic performance. Our results need further research validation with larger, real-world unstructured datasets, and clinical validation with an AI Clinical Trial to test model efficacy, safety, clinical significance and relevance at a public health level.

scholarly journals Precision Medicine: Omics-tailored Clinics

2019 ◽  
Vol 4 (02) ◽  
Author(s):  
Saliha Ece Acuner-Ozbabacan
Keyword(s):  
Precision Medicine ◽  
High Accuracy ◽  
Data Driven ◽  
Omics Data

We are now in a data-driven biomedical era in which the aim of precision medicine is to collect, investigate and interpret the omics data effectively and integratively to be able to implement the results in healthcare with high accuracy. Recent promising applications show that molecularly classifying a disease into subpopulations is beneficial for both patients and drug developers.

scholarly journals Data-driven criteria to assess fear remission and phenotypic variability of extinction in rats

2018 ◽  
Vol 373 (1742) ◽  
pp. 20170035 ◽  
Author(s):  
Jason Shumake ◽  
Carolyn Jones ◽  
Allison Auchter ◽  
Marie-Hélène Monfils
Keyword(s):  
Mental Health ◽  
Individual Differences ◽  
Fear Conditioning ◽  
Phenotypic Variability ◽  
Conditioned Fear ◽  
Differential Treatment ◽  
Data Driven ◽  
Output Measure ◽  
Treatment Responses

Fear conditioning is widely employed to examine the mechanisms that underlie dysregulations of the fear system. Various manipulations are often used following fear acquisition to attenuate fear memories. In rodent studies, freezing is often the main output measure to quantify ‘fear’. Here, we developed data-driven criteria for defining a standard benchmark that indicates remission from conditioned fear and for identifying subgroups with differential treatment responses. These analyses will enable a better understanding of individual differences in treatment responding. This article is part of a discussion meeting issue ‘Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists’.

In silico signaling modeling to understand cancer pathways and treatment responses

2019 ◽  
Vol 21 (3) ◽  
pp. 1115-1117 ◽  
Author(s):  
Meik Kunz ◽  
Julian Jeromin ◽  
Maximilian Fuchs ◽  
Jan Christoph ◽  
Giulia Veronesi ◽  
...  
Keyword(s):  
Precision Medicine ◽  
In Silico ◽  
Treatment Strategies ◽  
Therapy Response ◽  
Cancer Pathways ◽  
In Silico Modeling ◽  
Treatment Responses ◽  
The Individual ◽  
In Silico Models ◽  
Pan Cancer

Abstract Precision medicine has changed thinking in cancer therapy, highlighting a better understanding of the individual clinical interventions. But what role do the drivers and pathways identified from pan-cancer genome analysis play in the tumor? In this letter, we will highlight the importance of in silico modeling in precision medicine. In the current era of big data, tumor engines and pathways derived from pan-cancer analysis should be integrated into in silico models to understand the mutational tumor status and individual molecular pathway mechanism at a deeper level. This allows to pre-evaluate the potential therapy response and develop optimal patient-tailored treatment strategies which pave the way to support precision medicine in the clinic of the future.

P1924Population-wide disease trajectories in ischaemic heart disease

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
A D H Haue ◽  
I F R Jorgensen ◽  
A H C Christensen ◽  
C S Simon ◽  
M L H Haakonsen ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Heart Disease ◽  
Precision Medicine ◽  
Ischaemic Heart Disease ◽  
Large Scale ◽  
Anatomical Therapeutic Chemical ◽  
Drug Prescriptions ◽  
Data Driven ◽  
Data Types ◽  
Data Set

Abstract Background Defining cardiovascular disease (CVD) phenotypes from large, longitudinal electronic health records (EHRs) through analysis of patient similarities and dissimilarities is a strategy with new possibilities to practice precision medicine. We carried out a pilot data screen to quantify and characterise selected CVD phenotypes from EHRs comprising medical histories of 6,986,632 individuals spanning 21 years. Purpose The overall aim is to define temporal CVD phenotypes by data-driven characterisation employing bioinformatics approaches. We have defined temporal CVD phenotypes, by data-driven characterisation identifying statistically significant temporal disease trajectories that allow for future integration of lab test results, drug prescriptions and genomic data. Methods Data was assessed by computing temporal disease trajectories made from selecting certain indicator diagnoses. Inclusion criterion was admittance to a Danish hospital during 1995–2016. All data points were indexed by a unique key for each individual in a registry based national infrastructure that is stable over a life time. Encryption was performed pre-analyses to acquire research prone patient IDs (PIDs). Diagnostic codes were annotated from EHRs according to the WHO ICD-10, tests according to the Nomenclature for Properties and Units (NPU), procedures according to the Danish Health Care Classification (SKS) and drug prescriptions according to the Anatomical Therapeutic Chemical Classification System (ATC). Results The largest subsets in the case population were cardiac arrhythmias (I44–49) and chronic ischaemic heart disease (I20-I25) counting 582,180 and 579,619 patients. respectively. Mapping of temporal disease trajectories leading to cardiac arrest (I46) one of four major CVD complications, demonstrated that the majority of cases matching chronic ischaemic heart disease (I25) who present with cardiac arrest (I46) do not have any intermediate diagnosis. This kind of trajectory illustrates the deep phenotypic spectrum of the most common type of I25 patients. Conversely, no direct disease trajectories were observed between patients diagnosed with cardiac arrest (I46) following myocardial infarction (I21) or heart failure (I50) (see figure). Overall, the population-based reference phenomes of the selected CVD diagnoses from the dataset used was verified using detailed EHR from a subset amounting to approximately 2.6 million patients. Ischaemic heart disease trajectories Conclusion Mining of data from patients with chronic ischaemic heart disease by computing distinct disease trajectories leading to cardiac arrest provide a promising framework for establishing computational phenotypes. The multimorbidity trajectory approach allows us to define the longitudinal phenotype in the big data set. We argue that inclusion of additional data types including large-scale genomic analyses for sub-group stratification will elucidate disease mechanisms facilitating implementation of precision medicine. Acknowledgement/Funding NNF14CC0001 and 8114-00031B

Phenomapping of subgroups in hypertensive patients using unsupervised data-driven cluster analysis: An exploratory study of the SPRINT trial

2019 ◽  
Vol 26 (16) ◽  
pp. 1693-1706 ◽  
Author(s):  
Da-ya Yang ◽  
Zhi-qiang Nie ◽  
Li-zhen Liao ◽  
Shao-zhao Zhang ◽  
Hui-min Zhou ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Antihypertensive Treatment ◽  
Cox Regression ◽  
Cardiovascular Outcomes ◽  
Data Driven ◽  
Hypertensive Patients ◽  
Increased Risk ◽  
Data Driven Approach ◽  
Treatment Responses ◽  
Cluster 2

Background Hypertensive patients are highly heterogeneous in cardiovascular prognosis and treatment responses. A better classification system with phenomapping of clinical features would be of greater value to identify patients at higher risk of developing cardiovascular outcomes and direct individual decision-making for antihypertensive treatment. Methods An unsupervised, data-driven cluster analysis was performed for all baseline variables related to cardiovascular outcomes and treatment responses in subjects from the Systolic Blood Pressure Intervention Trial (SPRINT), in order to identify distinct subgroups with maximal within-group similarities and between-group differences. Cox regression was used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) for cardiovascular outcomes and compare the effect of intensive antihypertensive treatment in different clusters. Results Four replicable clusters of patients were identified: cluster 1 (index hypertensives); cluster 2 (chronic kidney disease hypertensives); cluster 3 (obese hypertensives) and cluster 4 (extra risky hypertensives). In terms of prognosis, individuals in cluster 4 had the highest risk of developing primary outcomes. In terms of treatment responses, intensive antihypertensive treatment was shown to be beneficial only in cluster 4 (HR 0.73, 95% CI 0.55–0.98) and cluster 1 (HR 0.54, 95% CI 0.37–0.79) and was associated with an increased risk of severe adverse effects in cluster 2 (HR 1.18, 95% CI 1.05–1.32). Conclusion Using a data-driven approach, SPRINT subjects can be stratified into four phenotypically distinct subgroups with different profiles on cardiovascular prognoses and responses to intensive antihypertensive treatment. Of note, these results should be taken as hypothesis generating that warrant further validation in future prospective studies.

Sign in / Sign up

Export Citation Format

Share Document