Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit Full Text

The predicted probability for death by the ML model was 1% compared to the baseline of 7.8%. The model’s predicted outcome was alive, in line with the final outcome of alive (true negative). The Vgg16 model was then compared with other existing models under the 5-fold cross-validation. For a fair comparison, we ensured that the partition of the data was the same across all methods.

🏆 Experience = Proficiency

A learning curve is typically described with a percentage that identifies the rate of improvement. In the visual representation of a learning curve, a steeper slope indicates initial learning that translates into higher cost savings, and subsequent learnings result in increasingly slower, more difficult cost savings. Because a surgeon is essentially practicing the same skill over and over whenever that procedure is done, the learning curve can be applied to show individual learning and performance over time. The model was widely applied during World War II (WWII) when it was realized that the cost of aircraft decreased with the increase in production performance. It was later taken up by the industrial and business sector for a variety of performance improvement applications. MeasuredThe other application of learning curve is quantitative, where mathematical models are created to represent the rate of proficiency or mastery of a task.

Learning curve models and examples

The cost benefits, therefore, may not lead to increased market share even though industry costs are declining because all participants are learning at approximately the same rate. For one, understanding this concept can help managers forecast the breakeven point and production costs of manufacturing a product. Some of these decisions include how they negotiate payments with vendors, when they can invest in special initiatives, and where they should allocate their resources.

Elaboration Theory

Second-order learning denotes that which is driven by changes in technology or human capital that lead to the learning curve model applies only to goal attainment. Another constraint of the curve is, when applied strictly, it can inhibit innovation. Organizations that optimize their processes to achieve the maximum output from a learning curve can only do so for one product at a time. Adding new products or modifying the processes adds complexity, which then creates costs that cascade through the whole production line.

Collaborative Learning – The Key to Better Performance

M.J and X.P.W performed critical revision of the manuscript for important intellectual content and statistical analysis. This is a 23-year-old male patient with no comorbidities who presented with AP. This patient neither undergone intubation or RRT nor developed an infection during this hospitalization.

• The theory helps us to understand when and why we forget information and what we must do to ensure maximum retention potential.
• A good way to reduce the learning curve when onboarding new hires is to show them your company’s one-page business plan.
• The AUC of the image combined with the gene mutation feature was 0.773, which was improved compared to the prediction result of the pure image (average AUC was 0.743).

Contents

This capability can lead to earlier and more accurate diagnoses, potentially improving patient outcomes through timely interventions. This not only enhances diagnostic confidence but also allows pathologists to allocate more time to complex cases where human expertise is still crucial. In 2022, there are around 590,000 and 160,000 new cases of colorectal cancer (CRC) in China and the United States (US) respectively, making CRC the second and fourth most common cancer respectively in the two countries1.

He identifies the first use of steep learning curve as 1973, and the arduous interpretation as 1978. Often no matter how well a formal employee training program is structured, it does not impart all of the knowledge and information employees need to perform their roles effectively. This is when real-life coaching from an experienced professional – a supervisor, mentor, or veteran employee – can be effective to acquire knowledge in real-time. Set long and short-term measurable outcomes to evaluate employee performance, training effectiveness, and task mastery. Make the purpose of your training program clear by identifying what employees are expected to accomplish by the end of training.

• This showed that images and gene mutations may work together to predict the survival of colorectal cancer patients, and the fusion features improved the prediction performance of the model.
• This may somewhat affect the stability and precision of the prediction, leading to false positives or negatives that might affect patient care.
• Molecular data were modeled separately and compared with the prediction results of H&E-stained images.
• Specifically, our method provides timely survival predictions within this framework, further enhancing the utility of IoT.
• The multimodal compact bilinear pooling method adopted in this paper is an improvement based on the TS method to adapt to the fusion of different modal features.

Wulczyn et al. developed a deep learning system for predicting disease-specific survival in stage II and III CRC, with model five-year disease-specific survival AUCs of 0.70 and 0.69 on two different validation sets, respectively. The limitation of this study is that both validation sets used are from the same dataset and there is no independent validation set29. With the development of sequencing technology, other omics information such as genome and transcriptome can help to obtain information on different dimensions of disease and make up for the lack of single omics information.

Although numerous studies have investigated CRC survival through molecular data and imaging approaches, an effective fusion model is still lacking. This study presents a novel deep learning framework developed to predict the 5-year OS in 84 colorectal cancer patients. Initially, we utilized histopathological images to assess their predictive capability regarding CRC survival. Furthermore, we integrated pathological images with molecular data to create advanced fusion models, significantly improving prediction accuracy. These models not only enable cancer patients to better understand their life expectancy but also assist clinicians in making informed decisions that guide appropriate treatment strategies.

This type of observation can be quantified and illustrated as a graph, namely, the learning curve. If the data from the learning curve shows that the current training process is not working, explore alternative employee training methods and implement other modifications to fine-tune your training programs. It might take a few rounds of trial and error to find the right change that improves performance. With project management, teams become more proficient by processing repeated tasks or similar projects. Initially, a project may take longer and involve more resources due to unfamiliarity or unanticipated challenges.

Both forward selection and backward elimination were applied, testing at each step for parameters to be included or excluded. The selection criteria to eliminate the predictors was Akaike Information Criterion (AIC). Figure 1 shows more details of the Vgg16 model, such as the number of layers, and input and output dimensions. Since this study is a dichotomous classification task, the model is changed to a dichotomous classification model by modifying the classification number based on the original VGG16 network. The trained model is saved in this study, and the network structure and parameters before the fully connected layer are saved and used as the image feature extraction model.

Knowledge on the practical application of experience curves and learning curves has increased greatly since 1936. Interest was renewed in the early 1990s with the publication of The Fifth Discipline by Peter Senge. Senge melded theories on mental models, the systems approach, and learning curves in a way that made sense for executives. The learning curve model was expanded by Adler and Clark into a learning process model. First-order learning refers to the classic learning curve model where productivity is an exponential function of experience.

When onboarding and training new employees, L&D teams are tasked with accelerating the time-to-productivity for new hires. Organizations can predict this reduction in per-unit cost by modeling the change with the learning curve. This is done by considering labor costs and employee training and will impact the cost of a good or service. Initially, the product may reach the market at a higher price point because of the high per-unit cost to produce a good. The complex learning curve model looks different for each activity, individual, or group. When learning tasks with complex learning curves, learners often encounter multiple peaks and plateaus.

However, the model predicted the mortality risk to be 9% based on information of the first 24 h after admission. (c) The model performance comparison of H&E and H&E combined with molecular features (top1 of clinic, mRNA, and Mutation), respectively. There’s no single best theory, but models like ADDIE, Gagné’s Nine Events, and Cognitive Load Theory are commonly used to design effective training. The best choice depends on your audience, objectives, and environment—learning theories should be matched to the specific context and goals of the training. Andragogy is a popular theory for adult learners as it emphasizes self-direction, life experience, and readiness to learn.

L&D managers can use Learning Curve Theory to track productivity and determine where employees need the most support and where L&D resources will have the biggest impact. When used in conjunction with a Collaborative Learning platform like 360Learning, these benefits can be even greater. As mentioned above, educators and trainers use this concept not only for effective class management but also to ensure that learners spend their time on the most beneficial activities. This learning curve model indicates that as the quantity of units produced doubles, the average cost per unit decreases at a uniform rate. Another critical assumption of the experience curve, noted by Lieberman, is that learning can be kept within the organization. Where industry-wide dissemination of process technology is rapid, the benefits of organizational learning through the experience curve may be short-lived.

However, as learners complete a task over time, they take less and less time to complete it and experience a rapid improvement in proficiency until the skill is acquired, at which point performance (and the learning curve) flatten. This represents an overall plateau that has been reached in terms of maximum task efficiency or a new challenge has emerged acting as a barrier to improvement. By assessing this rate, instructors can provide additional support to either fill in learning gaps or challenge quicker progress accordingly. The learning curve model supports ongoing development and ensures the implementation of up-to-date methods and tools required for personal growth.