Developing Artificial Neural Network Models to Predict Functioning One Year After Traumatic Spinal Cord Injury

Belliveau, Timothy; Jette, Alan M.; Seetharama, S.; Axt, Jeffrey; Rosenblum, David S.; Larose, Daniel T.; Houlihan, Bethlyn; Slavin, Mary D.; Larose, Chantal D.

doi:10.1016/j.apmr.2016.04.014

Cited by 47 publications

(55 citation statements)

References 28 publications

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“…Because of the variability in the severity of the functional and neurologic deficits that may arise following a traumatic SCI [1,4], a major issue surrounds the inability to predict the precise evolution of the SCI or the extent of recovery. This unresolved barrier has sparked much interest in the research community to develop an ability to better predict long-term ambulation following SCI, with the hope of alleviating patient anxiety surrounding poorly defined recovery trajectories [4][5][6] and to optimize reintegration into the community [3].…”

Section: Introductionmentioning

confidence: 99%

“…Classically, patient demographic characteristics, injury etiology, and baseline neurologic examination collected in the acute setting have been used to predict neurologic and functional outcome [4,[7][8][9][10], with the latter often being the best predictor [4]. More recently, prediction models such as logistic regression (LR) analysis have been introduced to support clinical decision making [5,11,12]. Using data from the European Multicenter Study on Human Spinal Cord Injury (EM-SCI) network, van Middendorp et al [11] devised a clinical model capable of accurately predicting ambulation outcomes following traumatic SCI.…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, Wilson et al [12] developed an effective prognostic model for predicting functional outcomes in patients with SCI using acute clinical and imaging data. In addition, Belliveau et al [5] created a predictive model that was highly accurate in predicting long-term ambulation outcomes in patients with traumatic SCI using acute demographic and neurologic examination data.…”

Section: Introductionmentioning

confidence: 99%

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A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry

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“…8,9 This allows the network to incorporate the intricate associations among variables into algorithms. 10 ANNs have been used in the medical field to perform a variety of difficult predictions: long-term functional recovery after spinal cord injury, 11 the incidence of dangerous viral infections, 12 and the toxicity of thrombolytic nanoparticles. 13 …”

Section: Introductionmentioning

confidence: 99%

Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention

Loftus

Brakenridge

Croft

et al. 2017

Journal of Surgical Research

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Background The prognosis for patients with severe acute lower intestinal bleeding (ALIB) may be assessed by complex artificial neural networks (ANNs) or user-friendly regression-based models. Comparisons between these modalities are limited, and predicting the need for surgical intervention remains elusive. We hypothesized that ANNs would outperform the Strate rule to predict severe bleeding and would also predict the need for surgical intervention. Methods We performed a 4-y retrospective analysis of 147 adult patients who underwent endoscopy, angiography, or surgery for ALIB. Baseline characteristics, Strate risk factors, management parameters, and outcomes were analyzed. The primary outcomes were severe bleeding and surgical intervention. ANNs were created in SPSS. Models were compared by area under the receiver operating characteristic curve (AUROC) with 95% confidence intervals. Results The number of Strate risk factors for each patient correlated significantly with the outcome of severe bleeding (r = 0.29, P < 0.001). However, the Strate model was less accurate than an ANN (AUROC 0.66 [0.57–0.75] versus 0.98 [0.95–1.00], respectively) which incorporated six variables present on admission: hemoglobin, systolic blood pressure, outpatient prescription for Aspirin 325 mg daily, Charlson comorbidity index, base deficit ≥5 mEq/L, and international normalized ratio ≥1.5. A similar ANN including hemoglobin nadir and the occurrence of a 20% decrease in hematocrit was effective in predicting the need for surgery (AUROC 0.95 [0.90–1.00]). Conclusions The Strate prediction rule effectively stratified risk for severe ALIB, but was less accurate than an ANN. A separate ANN accurately predicted the need for surgery by combining risk factors for severe bleeding with parameters quantifying blood loss. Optimal prognostication may be achieved by integrating pragmatic regression-based calculators for quick decisions at the bedside and highly accurate ANNs when time and resources permit.

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“…Machine learning (ML) techniques are increasingly being used to analyze electronic health record data to predict future disease onset or its future course [9][10][11][12][13] . These efforts include prediction of onset and complications of cardiovascular disease [14][15][16][17][18][19][20][21] , onset of T2D [22][23][24][25][26] , onset of kidney disease 27 , as well as prediction of postoperative outcomes [28][29][30][31][32] , birth related outcomes 33,34 , mortality 15,35,36 and hospital readmissions [37][38][39][40][41][42][43] . However, current approaches typically suffer from a number of limitations.…”

Section: Introductionmentioning

confidence: 99%

Nationwide prediction of type 2 diabetes comorbidities

Dworzyński

Aasbrenn

Rostgaard

et al. 2019

Preprint

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Identification of individuals at risk of developing disease comorbidities represents an important task in tackling the growing personal and societal burdens associated with chronic diseases. We employed machine learning techniques to investigate to what extent data from longitudinal, nationwide Danish health registers can be used to predict individuals at high risk of developing type 2 diabetes (T2D) comorbidities. Based on register data spanning hospitalizations, drug prescriptions and contacts with primary care contractors from >200,000 individuals newly diagnosed with T2D, we used logistic regression-, random forest-and gradient boosting models to predict five-year risk of heart failure (HF), myocardial infarction (MI), stroke (ST), cardiovascular disease (CVD) and chronic kidney disease (CKD). For HF, MI, CVD, and CKD, register-based models outperformed a reference model leveraging canonical individual characteristics by achieving area under the receiver operating characteristic curve improvements of 0.06, 0.03, 0.06, and 0.07, respectively. The top 1,000 patients predicted to be at highest risk exhibited observed incidence ratios exceeding 4.99, 3.52, 2.92 and 4.71 respectively. In summary, prediction of T2D comorbidities utilizing Danish registers led to consistent albeit modest performance improvements over reference models, suggesting that register data could be leveraged to systematically identify individuals at risk of developing disease comorbidities. 3/18 5/18 8/18 16/18

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Developing Artificial Neural Network Models to Predict Functioning One Year After Traumatic Spinal Cord Injury

Cited by 47 publications

References 28 publications

A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry

A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry

Neural network prediction of severe lower intestinal bleeding and the need for surgical intervention

Nationwide prediction of type 2 diabetes comorbidities

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