Prognosis Prediction
Prognosis Prediction - Related Videos
02:27
Predicting Molecular Geometry
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2020
VSEPR Theory for Determination of Electron Pair Geometries The following procedure uses VSEPR theory to determine the electron pair geometries and the molecular structures: Write the Lewis structure of the molecule or polyatomic ion. Count the number of electron groups (lone pairs and bonds) around the central atom. A single, double, or triple bond counts as one region of electron density. Identify the electron-pair geometry based on the number of electron groups: linear, trigonal planar,...
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
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Cited by 79 •
2011
Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.
08:08
Cell-based Assay Protocol for the Prognostic Prediction of Idiopathic Scoliosis Using Cellular Dielectric Spectroscopy
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Cited by 8 •
2013
A structured protocol is presented for a cell-based assay as a functional test to predict the prognosis of idiopathic scoliosis using cellular dielectric spectroscopy (CDS). The assay can be performed with fresh or frozen peripheral blood mononuclear cells (PBMCs) and the procedure is completed within 4 days.
13:42
RNA Secondary Structure Prediction Using High-throughput SHAPE
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Cited by 16 •
2013
High-throughput selective 2' hydroxyl acylation analyzed by primer extension (SHAPE) utilizes a novel chemical probing technology, reverse transcription, capillary electrophoresis and secondary structure prediction software to determine the structures of RNAs from several hundred to several thousand nucleotides at single nucleotide resolution.
01:03
Prediction Intervals
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2023
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable. However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. The...
FAQs
The useful inputs depend on the clinical question, but diagnosis, disease stage, symptoms, laboratory results, imaging findings, biomarkers, and treatment history are central sources. Their value comes from how they relate to outcomes such as survival, recurrence, complications, recovery, or treatment response. Combining these sources can provide a more clinically informative risk estimate than relying on one factor alone.
A model may be designed to estimate survival, recurrence, complications, recovery, or response to treatment, rather than treating prognosis as a single endpoint. Defining the outcome clarifies which patient information and treatment history are relevant and how performance should be assessed. This focus allows the resulting estimate to address a specific clinical or research question.
Validation tests whether predictions are reliable beyond the data used to develop a model. It helps assess performance, reveal uncertainty, and identify whether estimated risks correspond meaningfully to outcomes such as survival, recurrence, complications, or recovery. Without careful validation, a model may appear accurate while providing less dependable guidance for treatment planning or patient counseling.
Bias can make a model perform unevenly across patient groups, especially when the populations represented in development data do not reflect the people who will receive predictions. Evaluating performance across diverse patient populations helps identify these limitations. This assessment is important because an apparently useful estimate may otherwise produce less reliable risk information for some patients.
A practical workflow begins by assembling available medical information, including diagnosis, stage, symptoms, laboratory and imaging findings, biomarkers, and treatment history. These data are analyzed with a statistical model or machine-learning algorithm, then the resulting predictions are examined for validity, uncertainty, bias, and performance across diverse patient populations. This sequence supports responsible interpretation before clinical use.
A prognosis estimate can help stratify patients according to risk and indicate how likely different outcomes may be. Clinicians can consider that information alongside the patient’s medical details when discussing treatment planning, expected recovery, possible complications, or treatment response. Because predictions include uncertainty, they are most useful as structured support for communication and care decisions.
In research, prognosis prediction can organize patients by estimated risk and help examine outcomes such as survival, recurrence, complications, recovery, or treatment response. In healthcare systems, these estimates can also inform allocation of resources by identifying differing levels of expected clinical need. Reliable validation remains necessary so that these uses do not amplify bias or misrepresent uncertainty.