Predictive medicine

About: Predictive medicine is a research topic. Over the lifetime, 243 publications have been published within this topic receiving 4528 citations.

Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: systematic evidence review for the U.S. Preventive Services Task Force.

Abstract: This review supports the U.S. Preventive Services Task Force recommendations on genetic counseling and BRCA testing for susceptibility to breast and ovarian cancer.

Predictive medicine: computational techniques in therapeutic decision-making.

Abstract: The current paradigm for surgery planning for the treatment of cardiovascular disease relies exclusively on diagnostic imaging data to define the present state of the patient, empirical data to evaluate the efficacy of prior treatments for similar patients, and the judgement of the surgeon to decide on a preferred treatment. The individual variability and inherent complexity of human biological systems is such that diagnostic imaging and empirical data alone are insufficient to predict the outcome of a given treatment for an individual patient. We propose a new paradigm of predictive medicine in which the physician utilizes computational tools to construct and evaluate a combined anatomic/physiologic model to predict the outcome of alternative treatment plans for an individual patient. The predictive medicine paradigm is implemented in a software system developed for Simulation-Based Medical Planning. This system provides an integrated set of tools to test hypotheses regarding the effect of alternate treatment plans on blood flow in the cardiovascular system of an individual patient. It combines an Internet-based user interface developed using Java and VRML, image segmentation, geometric solid modeling, automatic finite element mesh generation, computational fluid dynamics, and scientific visualization techniques. This system is applied to the evaluation of alternate, patient-specific treatments for a case of lower extremity occlusive cardiovascular disease.

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Abstract: Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.

The use of genomics in clinical trial design.

Abstract: Many cancer treatments benefit only a minority of patients who receive them. This results in an enormous burden on patients and on the health care system. The problem will become even greater with the increasing use of molecularly targeted agents whose benefits are likely to be more selective unless the drug development process is modified to include codevelopment of companion diagnostics. Whole genome biotechnology and decreasing costs of genome sequencing make it increasingly possible to achieve an era of predictive medicine in oncology therapeutics. The challenges are numerous and substantial but are not primarily technological. They involve organizing publicly funded diagnostics of deregulated pathways, adopting new paradigms for drug development, and developing incentives for industry to incur the complexity and expense of codevelopment of drugs and companion diagnostics. This article reviews some designs for phase III clinical trials that may facilitate movement to a more predictive oncology.

Ten years of presymptomatic testing for Huntington's disease: the experience of the UK Huntington's Disease Prediction Consortium

Abstract: Data on all presymptomatic genetic tests for Huntington9s disease (HD) in the UK have been collected over the 10 year period since testing became available as a service. A total of 2937 completed tests have been performed up to the end of 1997, 2502 based on specific mutation testing, feasible since late 1993. A total of 93.1% of these were at 50% prior risk, with a significant excess of females (58.3%); 41.4% of results were abnormal or high risk, including 29.4% in subjects aged 60 or over. The trend in test numbers has currently levelled out at around 500 per year. Almost all presymptomatic tests are carried out in National Health Service genetics centres, with a defined genetic counselling protocol and with availability now in all regions of the UK. The introduction and establishment of HD presymptomatic testing shows that this form of predictive medicine for Mendelian disorders can be successfully incorporated into National Health Service structures. The comprehensive collection of simple data allows trends in demand and outcomes to be monitored and has also been the foundation for more detailed specific studies. A comparable approach to data collection in other genetic disorders will be important as presymptomatic testing becomes more generally feasible.