Browsing by Subject "support systems"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Machine learning for early detection of sepsis: an internal and temporal validation study.
    (JAMIA open, 2020-07) Bedoya, Armando D; Futoma, Joseph; Clement, Meredith E; Corey, Kristin; Brajer, Nathan; Lin, Anthony; Simons, Morgan G; Gao, Michael; Nichols, Marshall; Balu, Suresh; Heller, Katherine; Sendak, Mark; O'Brien, Cara

    Objective

    Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice.

    Materials and methods

    We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP-RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed.

    Results

    The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP-RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP-RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP-RNN outperform all 7 clinical risk score and machine learning comparisons.

    Conclusions

    We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.
  • Thumbnail Image
    ItemOpen Access
    Survey of the incidence and effect of major life events on graduate medical education trainees.
    (Med Educ Online, 2015) Grimm, Lars J; Nagler, Alisa; Maxfield, Charles M
    PURPOSE: This study aims to assess the incidence of major life events during graduate medical education (GME) training and to establish any associations with modifiable activities and career planning. METHODS: The authors surveyed graduating GME trainees from their parent institution in June 2013. Demographic information (clinical department, gender, training duration) and major life events (marriage, children, death/illness, home purchase, legal troubles, property loss) were surveyed. Respondents were queried about the relationship between life events and career planning. A multivariable logistic regression model tested for associations. RESULTS: A total of 53.2% (166/312) of graduates responded to the survey. 50% (83/166) of respondents were female. Major life events occurred in 96.4% (160/166) of respondents. Male trainees were more likely (56.1% [46/82] vs. 30.1% [25/83]) to have a child during training (p=0.01). A total of 41.6% (69/166) of responders consciously engaged or avoided activities during GME training, while 31.9% (53/166) of responders reported that life events influenced their career plans. Trainees in lifestyle residencies (p=0.02), those who experienced the death or illness of a close associate (p=0.01), and those with legal troubles (p=0.04) were significantly more likely to consciously control life events. CONCLUSION: Major life events are very common and changed career plans in nearly a third of GME trainees. Furthermore, many trainees consciously avoided activities due to their responsibilities during training. GME training programs should closely assess the institutional support systems available to trainees during this difficult time.