Using Random Forest Models to Identify Correlates of a Diabetic Peripheral Neuropathy Diagnosis from Electronic Health Record Data

Dubrava, Sarah; Mardekian, Jack; Sadosky, Alesia; Bienen, E Jay; Parsons, Bruce; Hopps, Markay; Markman, John D.

doi:10.1093/pm/pnw096

Cited by 44 publications

(25 citation statements)

References 19 publications

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“…An extremely important finding from our study, that is consistent with recent reports in diabetic neuropathy, 79,80 is the association between comorbidity and CIN. The SCQ score assesses not only the number but the impact of comorbidities on an individual.…”

Section: Discussionsupporting

confidence: 93%

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Chemotherapy-Induced Neuropathy in Cancer Survivors

Miaskowski

Mastick

Paul

et al. 2017

Journal of Pain and Symptom Management

100

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Context Evidence suggests that chemotherapy-induced neuropathy (CIN) is a significant problem for cancer survivors. However, a detailed phenotypic characterization of CIN in cancer survivors is not available. Objectives To evaluate between group differences in demographic and clinical characteristics, as well as in measures of sensation, function, and postural control, in a sample of cancer survivors who received a platinum and/or a taxane-based CTX regimen and did (n=426) and did not (n=197) develop CIN. Methods Survivors completed self-report questionnaires and underwent objective testing (i.e., light touch, pain sensation, cold sensation, vibration, muscle strength, grip strength, Purdue Pegboard test, Timed Get Up and Go Test, Fullerton Advanced Balance test). Parametric and non-parametric statistics were used to compare between group differences in study outcomes. Results Of the 426 survivors with CIN, 4.9% had CIN only in their upper extremities, 27.0% only in their lower extremities, and 68.1% in both their upper and lower extremities. Demographic and clinical characteristics associated with CIN included: older age, lower annual income, higher body mass index, a higher level of comorbidity, being born prematurely, receipt of a higher cumulative dose of chemotherapy, and a poorer functional status. Survivors with CIN had worse outcomes for all of the following objective measures: light touch, pain, temperature, vibration, upper and lower extremity function and balance. Conclusions This study is the first to provide a detailed phenotypic characterization of CIN in cancer survivors who received a platinum and/or a taxane compound. These data can serve as a benchmark for future studies of CIN in cancer survivors.

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Section: Discussionsupporting

confidence: 93%

“…In terms of demographic characteristics, consistent with the risk factors for CIN noted above, as well as risk factors associated with painful diabetic neuropathy, 79–82 survivors with CIN were older and had a lower annual household income.…”

Section: Discussionsupporting

confidence: 58%

Chemotherapy-Induced Neuropathy in Cancer Survivors

Miaskowski

Mastick

Paul

et al. 2017

Journal of Pain and Symptom Management

100

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“…It is worth noting that DM complications are far less common and severe in people with well-controlled blood glucose levels. Many of those complications have been studied through machine learning and data mining applications [78], [79], [80], [81], [82], [83], [84], [85], [87], [88], [89], [90], [92], [94], [95], [96], [97].…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

“…Similarly, in the case of neuropathy, DuBrava et al used Random Forest (RF) in order to select specific features targeting prediction of diabetic peripheral neuropathy (DPN) [83]. Based on relevance, the features chosen were Charlson Comorbidity Index score (100%), age (37.1%), number of pre-index procedures and services (29.7%), number of pre-index outpatient prescriptions (24.2%), number of pre-index outpatient visits (18.3%), number of pre-index laboratory visits (16.9%), number of pre-index outpatient office visits (12.1%), number of inpatient prescriptions (5.9%), and number of pain-related medication prescriptions (4.4%).…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,021

530

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The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

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“…It has been shown that random forest performs equally well or better than other methods on a diverse set of problems. It has been widely used in classification problems as diverse as bioinformatics 16 , medicine 17 , transportation safety 18 and customer behavior 19 .…”

Section: Random Forestmentioning

confidence: 99%

Tree-Based Classification of Tabla Strokes

Deolekar¹,

Abraham²

2018

Current Science

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The paper attempts to validate the effectiveness of tree classifiers to classify tabla strokes especially the ones which are overlapping in nature. It uses decision tree, ID3 and random forest as classifiers. A custom made data sets of 650 samples of 13 different tabla strokes were used for experimental purpose. 31 different features with their mean and variances were extracted for classification. Three data sets consisting of 21361, 18802 and 19543 instances respectively were used for the purpose. Validation has been done using measures like ROC curve and accuracy. The experimental results showed that all the classifiers showing excellent results with random forest outperforming the other two. The effectiveness of random forest in classifying strokes which are overlapping in nature is done by comparing the known results of that with multi-layer perceptron.

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Using Random Forest Models to Identify Correlates of a Diabetic Peripheral Neuropathy Diagnosis from Electronic Health Record Data

Abstract: Random forest modeling can determine likelihood of a DPN diagnosis. Further validation of the random forest model may help facilitate earlier diagnosis and enhance management strategies.

Cited by 44 publications

References 19 publications

Chemotherapy-Induced Neuropathy in Cancer Survivors

Chemotherapy-Induced Neuropathy in Cancer Survivors

Machine Learning and Data Mining Methods in Diabetes Research

Tree-Based Classification of Tabla Strokes

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