Parallel Distributed Processing and Neural Networks: Origins, Methodology and Cognitive Functions

Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis

Background

To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China.

Methods

Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou.

Results

The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926–67,232) additional hospitalization needs in the first month.

Conclusion

The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.

Prescreening entire mammograms for masses with artificial neural networks: preliminary results

RATIONALE AND OBJECTIVES

The authors evaluated the feasibility of combining wavelet transform and artificial neural network (ANN) technologies to prescreen mammograms for masses.

METHODS AND MATERIALS

Fifty-five mammograms (29 with masses and 26 without) were digitized to 100-mm resolution and processed by using wavelet transformation. These wavelets were subjected to a linear output sequential recursive auto-associative memory ANN and cluster analysis with feature vector formation. These vectors were used in two separate experiments-one with 13 cases and another with seven cases held out in a test set-to train feed-forward ANNs to detect the mammograms with a mass. The experiments were repeated with rerandomization of the data, four and six times, respectively.

RESULTS

There was a statistically significant correlation (P < .01) between the network's prediction of a mass and the presence of a mass. With majority voting, the feed-forward ANNs detected masses with 79% sensitivity and 50% specificity.

CONCLUSION

Although preliminary, the combination of wavelet transform and ANN is promising and may provide a viable method to prescreen mammograms for masses with high sensitivity and reasonable specificity.

Artificial neural networks for screening patients needing emergency cranial computed tomography scans in emergency departments

RATIONALE AND OBJECTIVES

We evaluated the potential for a neural network to screen candidates for emergency cranial computed tomography (CT) scans in an emergency department setting.

METHODS

Data were collected from 1625 patients undergoing emergency cranial CT scanning in two different emergency departments (EDs). Singular value decomposition (SVD) was used to remap input data for network training. Data were randomly divided into six subsets, and one was reserved as a test set to analyze network performance. Five networks were then trained on data from the five remaining sets using fivefold cross-validation. Each trained network was allowed an independent vote on need for CT scanning in each case from the test set. The majority vote was used as the final prediction. A similar analysis was done on data from each individual ED. Results are compared with prior statistical studies of the same data.

RESULTS

The network performed well when predicting clinical variable patterns that consistently produced negative CT scans and on patterns that were ambiguous in terms of the CT scan results. It performed poorly, however, on patterns that consistently predicted positive scans. This last finding appears to have resulted from inadequate training material. The two populations from which data were taken were shown to be distinct, but a network trained on the combined data performed as well as the networks from the individual EDs in predicting patients requiring CT scanning. Variables with the greatest contribution to the networks' prediction were consistent with those in prior statistical studies.

CONCLUSION

Although preliminary in nature, neural networks show promise as a screening device for selecting patients for emergent cranial CT scanning.