What is the actual accuracy of clinical prediction models? The case of transition to psychosis

2021 ◽  
Author(s):  
Joaquim Radua
Keyword(s):  
Prediction Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Actual Accuracy

Clinical prediction models

1995 ◽  
Vol 39 ◽  
pp. 134-135
Author(s):  
ALLAN S. DETSKY
Keyword(s):  
Prediction Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models

scholarly journals Towards better clinical prediction models: seven steps for development and an ABCD for validation

2014 ◽  
Vol 35 (29) ◽  
pp. 1925-1931 ◽  
Author(s):  
E. W. Steyerberg ◽  
Y. Vergouwe
Keyword(s):  
Prediction Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models

Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease

2021 ◽  
pp. postgradmedj-2020-139352
Author(s):  
Simon Allan ◽  
Raphael Olaiya ◽  
Rasan Burhan
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Prediction Models ◽  
Bone Fractures ◽  
Statin Treatment ◽  
Computer Algorithms ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Heart Attacks

Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs.

Transferring Clinical Prediction Models Across Hospitals and Electronic Health Record Systems

2020 ◽  
pp. 605-621
Author(s):  
Alicia Curth ◽  
Patrick Thoral ◽  
Wilco van den Wildenberg ◽  
Peter Bijlstra ◽  
Daan de Bruin ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Prediction Models ◽  
Health Record ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Electronic Health

scholarly journals MO1031FROM MARCELLO MALPIGHI  THROUGH  JEAN REDMAN OLIVER AND JOSEP TRUETA: A CONTINUING CONTRIBUTION TO THE “BLACK BOX”  CLINICAL PREDICTION MODELS IN NEPHROLOGY

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Enrico Favaro ◽  
Roberta Lazzarin ◽  
Daniela Cremasco ◽  
Erika Pierobon ◽  
Marta Guizzo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Prediction Models ◽  
Kidney Diseases ◽  
Kidney Injury ◽  
Black Box ◽  
Renal Physiology ◽  
Clinical Prediction ◽  
Renal Circulation ◽  
Clinical Prediction Models

Abstract Background and Aims The modern development of the black box approach in clinical nephrology is inconceivable without a logical theory of renal function and a comprehension of anatomical architecture of the kidney, in health and disease: this is the undisputed contribution offered by Malpighi, Oliver and Trueta starting from the seventeenth century. The machine learning model for the prediction of acute kidney injury, progression of renal failure and tubulointerstitial nephritis is a good example of how different knowledge about kidney are an indispensable tool for the interpretation of model itself. Method Historical data were collected from literature, textbooks, encyclopedias, scientific periodicals and laboratory experimental data concerning these three authors. Results The Italian Marcello Malpighi (1628-1694), born in Crevalcore near Bologna, was Professor of anatomy at Bologna, Pisa and Messina. The historic description of the pulmonary capillaries was made in his second epistle to Borelli published in 1661 and intitled De pulmonibus, by means of the frog as “the microscope of nature” (Fig. 1). It is the first description of capillaries in any circulation. William Harvey in De motu cordis in 1628 (year of publication the same of date of birth of Italian anatomist!) could not see the capillary vessels. This thriumphant discovery will serve for the next reconnaissance of characteristic renal rete mirabile.in the corpuscle of Malpighi, lying within the capsule of Bowman. Jean Redman Oliver (1889-1976), a pathologist born and raised in Northern California, was able to bridge the gap between the nephron and collecting system through meticulous dissections, hand drawn illustrations and experiments which underpin our current understanding of renal anatomy and physiology. In the skillful lecture “When is the kidney not a kidney?” (1949) Oliver summarizes his far-sighted vision on renal physiology and disease in the following sentence: the Kidney in health, if you will, but the Nephrons in disease. Because, the “nephron” like the “kidney” is an abstraction that must be qualified in terms of its various parts, its cellular components and the molecular mechanisms involved in each discrete activity (Fig. 2). The Catalan surgeon Josep Trueta I Raspall (1897-1977) was born in the Poblenou neighborhood of Barcelona. His impact of pioneering and visionary contribution to the changes in renal circulation for the pathogenesis of acute kidney injury was pivotal for history of renal physiology. “The kidney has two potential circulatory circulations. Blood may pass either almost exclusively through one or other of two pathways, or to a varying degree through both”. (Studies of the Renal Circulation, published in 1947). Now this diversion of blood from cortex to the less resistant medullary circulation is known with the eponym Trueta shunt. Conclusion The black box approach to the kidney diseases should be considered by practitioners as a further tool to help to inform model update in many clinical setting. The number of machine learning clinical prediction models being published is rising, as new fields of application are being explored in medicine (Fig. 3). A challenge in the clinical nephrology is to explore the “kidney machine” during each therapeutic diagnostic procedure. Always, the intriguing relationship between the set of nephrological syndromes and kidney diseases cannot disregard the precious notions the specific organization of kidney microcirculation, fruit of many scientific contributions of the work by Malpighi, Oliver and Trueta (Fig. 3).

scholarly journals EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings

2020 ◽  
Author(s):  
Nils Rethmeier ◽  
Necip Oğuz Şerbetci ◽  
Sebastian Möller ◽  
Roland Roller
Keyword(s):  
Language Processing ◽  
Prediction Models ◽  
Prognostic Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Data Resource ◽  
Performance Improvements ◽  
High Data ◽  
Resource Requirements ◽  
Learning Data

Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinical prediction did not improve expectedly. Instead of following this trend of using complex neural models in combination with small, pre-selected feature sets, we propose EffiCare, which focuses on minimizing hospital resource requirements for assistive clinical prediction models. First, by embedding medical events,we eliminate manual domain feature-engineering and increase the amount of learning data. Second, we use small,but data-efficient models, that compute faster and are easier to interpret. We evaluate our approach on four clinical prediction tasks and achieve substantial performance improvements over highly resource-demanding state-of-the-art methods. Finally, to evaluate our model beyond score improvements, we apply explainability and interpretability methods to analyze the decisions of our model and whether it uses data sources and parameters efficiently.

scholarly journals Clinical Prediction Models for Primary Prevention of Cardiovascular Disease: Validity in Independent Cohorts

2021 ◽  
Author(s):  
Gaurav Gulati ◽  
Riley J Brazil ◽  
Jason Nelson ◽  
David van Klaveren ◽  
Christine M. Lundquist ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Decision Making ◽  
Primary Prevention ◽  
Prediction Models ◽  
Model Updating ◽  
Clinical Prediction ◽  
Net Benefit ◽  
Clinical Prediction Models ◽  
C Statistic ◽  
Prevention Of Cardiovascular Disease

AbstractBackgroundClinical prediction models (CPMs) are used to inform treatment decisions for the primary prevention of cardiovascular disease. We aimed to assess the performance of such CPMs in fully independent cohorts.Methods and Results63 models predicting outcomes for patients at risk of cardiovascular disease from the Tufts PACE CPM Registry were selected for external validation on publicly available data from up to 4 broadly inclusive primary prevention clinical trials. For each CPM-trial pair, we assessed model discrimination, calibration, and net benefit. Results were stratified based on the relatedness of derivation and validation cohorts, and net benefit was reassessed after updating model intercept, slope, or complete re-estimation. The median c statistic of the CPMs decreased from 0.77 (IQR 0.72-0.78) in the derivation cohorts to 0.63 (IQR 0.58-0.66) when externally validated. The validation c-statistic was higher when derivation and validation cohorts were considered related than when they were distantly related (0.67 vs 0.60, p < 0.001). The calibration slope was also higher in related cohorts than distantly related cohorts (0.69 vs 0.58, p < 0.001). Net benefit analysis suggested substantial likelihood of harm when models were externally applied, but this likelihood decreased after model updating.ConclusionsDiscrimination and calibration decrease significantly when CPMs for primary prevention of cardiovascular disease are tested in external populations, particularly when the population is only distantly related to the derivation population. Poorly calibrated predictions lead to poor decision making. Model updating can reduce the likelihood of harmful decision making, and is needed to realize the full potential of risk-based decision making in new settings.

scholarly journals Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms

2016 ◽  
Vol 12 (02) ◽  
pp. 161-168 ◽  
Author(s):  
Berk Ustun ◽  
M. Brandon Westover ◽  
Cynthia Rudin ◽  
Matt T. Bianchi
Keyword(s):  
Sleep Apnea ◽  
Medical History ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models

Dynamic clinical prediction models for discrete time‐to‐event data with competing risks—A case study on the OUTCOMEREA database

2018 ◽  
Vol 61 (3) ◽  
pp. 514-534 ◽  
Author(s):  
Rachel Heyard ◽  
Jean‐François Timsit ◽  
Wafa Ibn Essaied ◽  
Leonhard Held ◽  
Keyword(s):  
Competing Risks ◽  
Discrete Time ◽  
Prediction Models ◽  
Event Data ◽  
Clinical Prediction ◽  
Time To Event ◽  
Clinical Prediction Models ◽  
Time To Event Data
Sign in / Sign up

Export Citation Format

Share Document