Purpose The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19. Materials and methods This is a retrospective cohort study of patients diagnosed or under investigation for COVID-19. A machine learning algorithm was trained to predict future presence of intubation based on prior vitals, laboratory, and demographic data. Model performance was compared to ROX index, a validated prognostic tool for prediction of mechanical ventilation. Results 4087 patients admitted to five hospitals between February 2020 and April 2020 were included. 11.03% of patients were intubated. The machine learning model outperformed the ROX-index, demonstrating an area under the receiver characteristic curve (AUC) of 0.84 and 0.64, and area under the precision-recall curve (AUPRC) of 0.30 and 0.13, respectively. In the Kaplan-Meier analysis, patients alerted by the model were more likely to require intubation during their admission ( p < 0.0001). Conclusion In patients diagnosed or under investigation for COVID-19, machine learning can be used to predict future risk of intubation based on clinical data which are routinely collected and available in clinical setting. Such an approach may facilitate identification of high-risk patients to assist in clinical care.
Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.
Chronic stress can lead to psychiatric illness characterized by impairments of executive function, implicating the prefrontal cortex as a target of stress-related pathology. Previous studies have shown that various types of chronic stress paradigms reduce dendritic branching, length and spines of medial prefrontal cortex (mPFC) pyramidal neurons. However, these studies largely focused on layer II/III pyramidal neurons in adult male rats with less known about layer V, the site of projection neurons. Because the prefrontal cortex develops throughout adolescence, stress during adolescence may have a greater impact on structure and function than stress occurring during adulthood. Furthermore, females display greater risk of stress-related psychiatric disorders, indicating sex-specific responses to stress. In this study, male and female adolescent (42–48 days old, 4 rats per group) or adult (68–72 days old, 4 rats per group) Sprague-Dawley rats were exposed to 5 days of repeated social stress in the resident-intruder paradigm or control manipulation. We examined dendritic morphology of cells in the mPFC in both layer II/III and Layer V. Repeated social stress resulted in decreased dendritic branching in layer II/III apical dendrites regardless of sex or age. In apical layer V dendrites, stress increased branching in adult males but decreased it in all other groups. Stress resulted in a decrease in dendritic spines in layer V apical dendrites for male adolescents and female adults, and this was mostly due to a decrease in filopodial and mushroom spines for male adolescents, but stubby spines for female adults. In sum, these results suggest that repeated stress reduces complexity and synaptic connectivity in adolescents and female adults in both input and output layers of prelimbic mPFC, but not in male adults. These changes may represent a potential underlying mechanism as to why adolescents and females are more susceptible to the negative cognitive effects of repeated or chronic stress.
Study Design Retrospective Cohort Study. Objectives Using natural language processing (NLP) in combination with machine learning on standard operative notes may allow for efficient billing, maximization of collections, and minimization of coder error. This study was conducted as a pilot study to determine if a machine learning algorithm can accurately identify billing Current Procedural Terminology (CPT) codes on patient operative notes. Methods This was a retrospective analysis of operative notes from patients who underwent elective spine surgery by a single senior surgeon from 9/2015 to 1/2020. Algorithm performance was measured by performing receiver operating characteristic (ROC) analysis, calculating the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC). A deep learning NLP algorithm and a Random Forest algorithm were both trained and tested on operative notes to predict CPT codes. CPT codes generated by the billing department were compared to those generated by our model. Results The random forest machine learning model had an AUC of .94 and an AUPRC of .85. The deep learning model had a final AUC of .72 and an AUPRC of .44. The random forest model had a weighted average, class-by-class accuracy of 87%. The LSTM deep learning model had a weighted average, class-by-class accuracy 0f 59%. Conclusions Combining natural language processing with machine learning is a valid approach for automatic generation of CPT billing codes. The random forest machine learning model outperformed the LSTM deep learning model in this case. These models can be used by orthopedic or neurosurgery departments to allow for efficient billing.
We recently found that non-stressed female rats have higher basal prepro-orexin expression and activation of orexinergic neurons compared to non-stressed males, which leads to impaired habituation to repeated restraint stress at the behavioral, neural, and endocrine level. Here, we extended our study of sex differences in the orexin system by examining spine densities and dendritic morphology in putative orexin neurons in adult male and female rats that were exposed to 5 consecutive days of 30 min restraint. Analysis of spine distribution and density indicated that putative orexinergic neurons in control non-stressed females had significantly more dendritic spines than those in control males, and the majority of these were mushroom spines. This morphological finding may suggest more excitatory input onto orexin neurons in female rats. As orexin neurons are known to promote the HPA response, this morphological change in orexin neurons could underlie the impaired habituation to repeated stress in female rats. Dendritic complexity did not differ between non-stressed males and females, however repeated restraint stress decreased total dendritic length, nodes, and branching primarily in males. Thus, reduced dendritic complexity of putative orexinergic neurons is observed in males but not in females after 5 days of repeated restraint stress. This morphological change might be reflective of decreased orexin system function, which may allow males to habituate more fully to repeated restraint than females.These results extend our understanding of the role of orexin neurons in regulating habituation and demonstrate changes in putative orexin cell morphology and spines that may underlie sex differences in habituation.
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