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Fracture detection has been a long-standing paradigm on the medical imaging community. Many algorithms and systems have been presented to accurately detect and classify images in terms of the presence and absence of fractures in different parts of the body. While these solutions are capable of obtaining results which even surpass human scores, few efforts have been dedicated to evaluate how these systems can be embedded in the clinicians and radiologists working pipeline. Moreover, the reports that are included with the radiography could also provide key information regarding the nature and the severity of the fracture. In this paper, we present our first findings towards assessing how computer vision, natural language processing and other systems could be correctly embedded in the clinicians&#39; pathway to better aid on the fracture detection task. We present some initial experimental results using publicly available fracture datasets along with a handful of data provided by the National Healthcare System from the United Kingdom in a research initiative call. Results show that there is a high likelihood of applying transfer learning from different existing and pre-trained models to the new records provided in the challenge, and that there are various ways in which these techniques can be embedded along the clinicians&#39; pathway.

ECAI 2020

Assessing the clinicians&#39; pathway to embed artificial intelligence for assisted diagnostics of fracture detection

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Fracture detection has been a long-standing paradigm on the medical imaging community. Many algorithms and systems have been presented to accurately detect and classify images in terms of the presence and absence of fractures in different parts of the body. While these solutions are capable of obtaining results which even surpass human scores, few efforts have been dedicated to evaluate how these systems can be embedded in the clinicians and radiologists working pipeline. Moreover, the reports that are included with the radiography could also provide key information regarding the nature and the severity of the fracture. In this paper, we present our first findings towards assessing how computer vision, natural language processing and other systems could be correctly embedded in the clinicians' pathway to better aid on the fracture detection task. We present some initial experimental results using publicly available fracture datasets along with a handful of data provided by the National Healthcare System from the United Kingdom in a research initiative call. Results show that there is a high likelihood of applying transfer learning from different existing and pre-trained models to the new records provided in the challenge, and that there are various ways in which these techniques can be embedded along the clinicians' pathway.

Assessing the clinicians' pathway to embed artificial intelligence for assisted diagnostics of fracture detection

technical paper

"The management of type 1 diabetes mellitus (T1DM) is a burdensome life-long task. In fact, T1DM individuals are request to perform every day tens of actions to adapt the insulin therapy, aimed at maintaining the blood glucose (BG) concentration as much as possible into a safe range coping with the day-to-day variability of their life style. The recent availability of continuous glucose monitoring (CGM) devices and other low-cost wearable sensors to track important vital and activity signals, is stimulating the development of decision support systems to lower this burden. Modern deep learning models, trained using rich amount of information, are a suitable and effective instrument for such purpose, especially if used to predict future BG values. However, the high accuracy of deep learning approaches is often obtained at the expense of less interpretability.

To surpass this limit, in this work we propose a new deep learning method for BG prediction based on a personalized bidirectional long short-term memory (LSTM) equipped with a tool that enables its interpretability. The OhioT1DM Dataset was used to develop a model targeting future BG at 30 and 60 minute prediction horizons (PH). The accuracy of model predictions was evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and the time gained (TG) to anticipate the actual glucose concentration.

The obtained results show fairly good prediction accuracy (for PH = 30/60 min): RMSE = 20.20/34.19 mg/dl, MAE = 14.74/25.98 mg/dl, and TG = 9.17/18.33 min.
Moreover, we showed, in a representative case, that our algorithm is able to preserve the physiological meaning of the considered inputs. 

In conclusion, we built a model able to provide reliable glucose performance ensuring the interpretability of its output. Future work will assess model performance against other competitive strategies. "

A Personalized and Interpretable Deep Learning Based Approach to Predict Blood Glucose Concentration inType 1 Diabetes

A machine learning-based method for blood glucose level prediction thirty and sixty minutes in advance based on highly multiclass classification (as opposed to the more traditional regres- sion approach) is proposed. An advantage of this approach is the possibility of modelling and visualising the uncertainty of a predic- tion across the entire range of blood glucose levels without paramet- ric assumptions such as normality. To demonstrate the approach, a long-short term memory-based neural network classifier is used in conjunction with a blood glucose-specific data preprocessing tech- nique (risk domain transform) to train a set of models and generate predictions for the 2018 and 2020 Blood Glucose Level Prediction Competition datasets. Numeric accuracy results are reported along with examples of the uncertainty visualisation possible using this technique.

Neural Multi-class Classification Approach to Blood Glucose Level Forecasting with Prediction Uncertainty Visualisation

For people with type 1 diabetes (T1D), the beta cells in pancreas are destroyed and can not produce insulin which is the only hormone that response to high glucose concentrations. So, people with T1D must administer exogenous insulin to maintain their BGC within an euglycemic range. Hyperglycemia (high BGC) and hypoglycemia (low BGC) can occur because of poor BGC management. Using recursively identified models to predict the future BGC values opens novel possibilities for improving the BGC regulation performance by adjusting the dose of insulin infusion, taking rescue carbohydrates, or both. In this paper, a latent variable (LV) based multivariable statistical modeling approach is applied to model BGC dynamics and forecast future BGCs. The model is evaluated with the Ohio T1DM dataset that contains BGCs from continuous glucose monitoring (CGM) sensors, basal and bolus insulin information from insulin pump, and additional information from wristbands or reported by the subject. The average RMSE of 6 subjects is 19.37 mg/dL for 30-min-ahead prediction and 32.59 mg/dL for 60-min-ahead prediction. The results indicate that the LV-based model can predict future BGC values with high prediction accuracy.

Prediction of Glucose Levels for People with Type 1 Diabetes using Latent Variable based Model

In this presentation, we show our results for the Blood Glucose Level Prediction Challenge for the Ohio2020 dataset. We have used four variants of genetic programming to build white-box models for predicting 30 minutes and 60 minutes ahead. The results are compared to classical methods including multivariate linear regression, random forests, as well as two types of ARIMA models. Notably, we have included future values of bolus and basal into some of the models because we assume that these values can be controlled. Additionally, we have used a convolution filter to smooth the information in the bolus volume feature. We find that overall tree-based GP per-forms well and better than multi-variate linear regression and random forest, while ARIMA models performed worst on the here analyzed data.

Analysis of the performance of Genetic Programming on the Blood Glucose Level Prediction Challenge 2020

"An essential part of this work is to provide a data-driven
model for predicting blood glucose levels that will help to warn the
patient about a potential hypo- or hyper-glycemic event in an easyto-
manage and discreet way."

A Deep Learning Approach for Blood Glucose Prediction of Type 1 Diabetes

"Real-time forecasting of blood glucose (BG) levels has
the potential to drastically improve management of Type 1 Diabetes,
a widespread chronic disease affecting the metabolic system. Most
notably, if hypo or hyperglycemia episodes (i.e. glycemic excursion
below or above a safe range) could be accurately predicted, then the
patient could be timely warned, thus enabling proactive countermeasures
to avoid these dangerous conditions. In this work, a novel personalized
algorithm for the real-time forecasting of BG is developed
by combining the output of a shallow feed forward neural network
with an error imputation module composed by an ensemble of trees.
Past glucose readings as well as insulin, meals and work/sleep time
information are carefully handled to train and boost the prediction
performance of the algorithm. The root mean square error over the 6
subjects achieves a mean value of 18.63 mg/dL and 32.27 mg/dL for
30- and 60-minute prediction horizon respectively."

Personalized Machine Learning Algorithm based on Shallow Network and Error Imputation Module for an Improved Blood Glucose Prediction

Accurate blood glucose (BG) prediction is necessary for daily glucose management of diabetes therapy. As glucose dynamics are often affected by various factors, such as diet, physical exercise, and insulin injection, it is difficult to consider all the relevant information and make a balance between the high-dimensional inputs and learning efficiency for a deep learning network. In this work, a novel multivariate predictor with a multi-scale long short-term memory (MS-LSTM) network was developed to automatically characterize the high-dimensional temporal dynamics and extract the features of blood glucose fluctuation and temporal trends sufficiently. Meanwhile, a multi-lag structure is designed for multiple variables, which can extract the dependence between different variables and blood glucose fluctuations more effectively. Furthermore, long-term sparse information was encoded and compressed to improve the learning efficiency of this deep learning network. The predictive capability of the proposed method was illustrated through 30-min and 60-min ahead glucose prediction in the OhioT1DM-2 Dataset. The root means square error (RMSE) values of 30-min and 60-min ahead predictions were 19.048 and 32.029, respectively, and the mean absolute error (MAE) values of 30-min and 60-min ahead predictions were 13.503 and 23.833. The results demonstrate the efficiency and prediction accuracy of the offline deep learning network, especially in the case of high-dimensional variables availability.

Multi-Scale Long Short-Term Memory Network with Multi-Lag Structure for Blood Glucose Prediction

Blood glucose (BG) prediction plays an important role in daily BG control. Accurate prediction of short-term glucose concentration can provide early warning for hyperglycemia and hypoglycemia events. This paper proposed a novel framework that combined an online prediction model with a residual compensation network. The autoregressive moving average (ARMA) model was used for online blood glucose prediction and the neural network was applied for compensation of prediction error. The advantages of this combined framework are: (1) the online ARMA model is efficient and robust to capture time-varying glucose dynamics, (2) the residual compensation network is capable to estimate errors from the online prediction model. The performance of this method was evaluated by the root mean squared error (RMSE) and the mean absolute error (MAE) in the dataset of OhioT1DM.The results were shown in detail that the mean values of the best RMSE of six patients at 30-min and 60-min horizon were 20.03 and 34.89 respectively, and the best MAE at 30-min and 60-min horizon were 14.52 and 24.61. Compared with the ARMA model, the combined predictor with a residual compensation network shows better prediction accuracy. Thus, we concluded that the proposed framework was an available approach for online blood glucose level prediction (BGLP).

Online Blood Glucose Prediction Using Autoregressive Moving Average Model with Residual Compensation Network

In this study we investigate the need for training future blood glucose level prediction models at the individual level (i.e. per patient). Specifically, we train various model classes: linear models, feed-forward neural networks, recurrent neural networks, and recurrent neural networks incorporating attention mechanisms, to predict future blood glucose levels using varying time series history lengths and data sources. We also compare methods of handling missing time series data during training. We found that relatively short history lengths provided the best results: a 30 minute history length proved optimal in our experiments. We observed long short-term memory (LSTM) networks performed better than linear and feed-forward neural networks, and that including an attention mechanism in the LSTM model further improved performance, even when processing sequences with relatively short length. We observed models trained using all of the available data outperformed those trained at the individual level. We also observed models trained using all of the available data, except for the data contributed by a given patient, were as effective at predicting the patient's future blood glucose levels as models trained using all of the available data. These models also significantly outperformed models trained using the patient's data only. Finally, we found that including sequences with missing values during training produced models that were more robust to missing values.

Experiments in non-personalised future blood glucose level prediction

This work suggests two methods—both relying on stacked regression and data fusion of CGM and activity—to predict the blood glucose level of patients with type 1 diabetes. Method 1 uses histories of CGM data appended with the average of activity data in the same histories to train three base regressions: a multilayer perceptron, a long short- term memory, and a partial least squares regression. In Method 2, histories of CGM and activity data are used separately to train the same base regressions. In both methods, the predictions from the base regressions are used as features to create a combined model. This model is then used to make the final predictions. The results obtained show the effectiveness of both methods. Method 1 provides slightly better results.

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