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Studies in Health Technology and... 2016Knowledge of how diseases progress and transform is crucial for clinical decision making. Frequent pattern mining techniques, such as sequential pattern mining (SPM)...
Knowledge of how diseases progress and transform is crucial for clinical decision making. Frequent pattern mining techniques, such as sequential pattern mining (SPM) algorithms, can automatically extract such knowledge from large collections of electronic medical records (EMR). However, EMR data are usually unorganized and highly noisy. Finding meaningful disease patterns often calls for manual manipulation such as cohort and feature selection on EMR data by medical professionals. In this paper, we propose a topic-model-based SPM approach to find disease progression patterns from diagnostic records. We improve the traditional SPM algorithms by filtering and grouping the diagnosis sequences according to different clinical topics. These topics represent certain clinical conditions with closely related diagnoses, and are detected without prior medical knowledge. The experiment on real-world EMR data shows that our approach is able to find meaningful progression patterns with less noises, and can help quickly identify interesting patterns related to a certain clinical condition with less human effort.
Topics: Algorithms; Decision Making, Computer-Assisted; Disease Progression; Electronic Health Records; Humans
PubMed: 27577403
DOI: No ID Found -
The AAPS Journal Dec 2010This article demonstrates techniques for describing and predicting disease progression in acute stroke by modeling scores measured using clinical assessment scales,...
This article demonstrates techniques for describing and predicting disease progression in acute stroke by modeling scores measured using clinical assessment scales, accommodating dropout as an additional source of information. Scores assessed using the National Institutes of Health Stroke Scale and the Barthel Index in acute stroke patients were used to model the time course of disease progression. Simultaneous continuous and probabilistic models for describing the nature and magnitude of score changes were developed, and used to model the trajectory of disease progression using scale scores. The models described the observed data well, and exhibited good simulation properties. Applications include longitudinal analysis of stroke scale data, clinical trial simulation, and prognostic forecasting. Based upon experience in other areas, it is likely that application of this modeling methodology will enable reductions in the number of patients needed to carry out clinical studies of treatments for acute stroke.
Topics: Acute Disease; Disease Progression; Humans; Models, Theoretical; Stroke
PubMed: 20857252
DOI: 10.1208/s12248-010-9230-0 -
Scientific Reports May 2015Well-trained clinicians may be able to provide diagnosis and prognosis from very short biomarker series using information and experience gained from previous patients....
Well-trained clinicians may be able to provide diagnosis and prognosis from very short biomarker series using information and experience gained from previous patients. Although mathematical methods can potentially help clinicians to predict the progression of diseases, there is no method so far that estimates the patient state from very short time-series of a biomarker for making diagnosis and/or prognosis by employing the information of previous patients. Here, we propose a mathematical framework for integrating other patients' datasets to infer and predict the state of the disease in the current patient based on their short history. We extend a machine-learning framework of "prediction with expert advice" to deal with unstable dynamics. We construct this mathematical framework by combining expert advice with a mathematical model of prostate cancer. Our model predicted well the individual biomarker series of patients with prostate cancer that are used as clinical samples.
Topics: Algorithms; Biomarkers; Disease Progression; Humans; Models, Theoretical
PubMed: 25989741
DOI: 10.1038/srep08953 -
JAMA Aug 2020
Topics: Child; Contact Lenses; Disease Progression; Eyeglasses; Humans; Myopia
PubMed: 32780128
DOI: 10.1001/jama.2020.10953 -
The British Journal of Ophthalmology Sep 1948
Topics: Disease Progression; Humans; von Hippel-Lindau Disease
PubMed: 18170494
DOI: 10.1136/bjo.32.9.575 -
Parkinsonism & Related Disorders Aug 2022
Topics: Biomarkers; Disease Progression; Humans; Parkinson Disease
PubMed: 35922274
DOI: 10.1016/j.parkreldis.2022.07.015 -
BMC Systems Biology Jul 2018Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic...
BACKGROUND
Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time in an individual and through the perturbations of genes. Systematic experiments tracking disease progression at gene level are usually conducted giving a temporal microarray data. There is a need for developing methods to analyze such complex data and extract important proteins which could be involved in temporal progression of the data and hence progression of the disease.
RESULTS
In the present study, we have considered a temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We have used this data along with an available Protein-Protein Interaction network to find a network of interactions between proteins which reproduces the next time point data from previous time point data. We show that the resulting network can be mined to identify critical nodes involved in the temporal progression of perturbations. We further show that published algorithms can be applied on such connected network to mine important proteins and show an overlap between outputs from published and our algorithms. The importance of set of proteins identified was supported by literature as well as was further validated by comparing them with the positive genes dataset from OMIM database which shows significant overlap.
CONCLUSIONS
The critical proteins identified from algorithms can be hypothesized to play important role in temporal progression of the data.
Topics: Algorithms; Computational Biology; Disease Progression; Protein Interaction Maps
PubMed: 30045727
DOI: 10.1186/s12918-018-0600-z -
Nature Medicine Jul 2018
Topics: Disease Progression; Humans; Leukemia, Myeloid, Acute
PubMed: 29988142
DOI: 10.1038/s41591-018-0114-7 -
Journal of Molecular Cell Biology Dec 2022The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration, at...
The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration, at which a drastic and qualitative shift occurs. The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration, which allows the timely implementation of appropriate measures to prevent a catastrophic transition. However, identifying the pre-deterioration stage is a challenging task in clinical medicine, especially when only a single sample is available for most patients, which is responsible for the failure of most statistical methods. In this study, a novel computational method, called single-sample network module biomarkers (sNMB), is presented to predict the pre-deterioration stage or critical point using only a single sample. Specifically, the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples. Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets, including acute lung injury, stomach adenocarcinoma, esophageal carcinoma, and rectum adenocarcinoma. In addition, it provides signaling biomarkers for further practical application, which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.
Topics: Humans; Disease Progression; Biomarkers; Adenocarcinoma; Signal Transduction
PubMed: 36069893
DOI: 10.1093/jmcb/mjac052 -
Nature Cancer Dec 2023
Topics: Humans; Disease Progression; Immunotherapy
PubMed: 38102345
DOI: 10.1038/s43018-023-00666-0