Tyler O'Neal, Staff Editor GOVERNMENT

AI helps categorize, triage primary care patients with respiratory symptoms

Researchers from Iceland trained a machine learning model with artificial intelligence to triage patients with respiratory symptoms before the patients visit a primary care clinic. To train the machine learning model, the researchers used only questions that a patient might be asked before a clinic visit. Information was extracted from 1,500 clinical text notes that included a physician's interpretation of the patient's symptoms and signs, as well as reasons for clinical decisions made during the consultation, such as imaging referrals and prescriptions. Patients were categorized into one of five diagnostic categories based on information in clinical notes. Patients from all primary care clinics in the capital area of Iceland were included. The model scored each patient in two extrinsic datasets and divided patients into 10 risk groups. The researchers then analyzed selected outcomes in each group.

Patients in risk groups 1-5 were younger, had lower rates of lung inflammation, were less likely to be re-evaluated in primary and emergency care, and were less likely to receive antibiotic prescriptions or chest X-ray referrals, as compared to higher risk groups 6-10. The lowest five groups contained no chest X-rays with signs of pneumonia or a pneumonia diagnosis by a physician. Researchers concluded that the model can reduce the number of chest X-ray referrals by eliminating them in risk groups 1-5.

What We Know: Respiratory symptoms are common reasons people visit primary care clinicians. However, many of their symptoms are self-resolving. Researchers argue that triaging patients before physician consultations may reduce unnecessary diagnostic testing; health care costs; and overprescription of antibiotics, which can lead to greater bacterial resistance.

What This Study Adds: Researchers found that a machine learning model can effectively categorize patients among 10 risk groups, allowing clinicians to communicate with lower-risk patients in ways that don’t add to their heavy work schedule and can allow for them to care for higher-risk patients and those with severe respiratory symptoms. The team asserts that the machine learning model could reduce costs for patients, the health care system, and society.

The researcher who carried out the study
The researcher who carried out the study

Spanish researcher Fereres develops simulations of alfalfa in AquaCrop forecasting tool

Tyler O'Neal, Staff Editor GOVERNMENT

The Department of Agronomy at the UCO improves, together with the IAS - CSIC, the AquaCrop model by introducing the option of simulating alfalfa yield with precision

AquaCrop is the crop growth simulation model created by the UN’s Food and Agriculture Organization (FAO). Playing an essential role in its development was Elías Fereres, a Professor Emeritus in the María de Maeztu Unit of Excellence at the University of Cordoba’s Department of Agronomy (DAUCO). This model, which after almost 20 years of life is the second most used in the world in research, allows simulating the response of crop yields according to climate, soil, and irrigation management, something very important in areas where water is a limiting factor in production.

Until now, this model only allowed users the ability to simulate the yield of annual crops (herbaceous crops with annual cycles), but not perennial crops. This has changed thanks to new work by the DAUCO and IAS-CSIC, which includes the simulation of alfalfa in AquaCrop, offering valid crop yield predictions for different climates and zones.

Alfalfa is a perennial forage crop that lasts 3 to 5 years in Mediterranean climates and is cut several times each year, as it resprouts again (4 to 8 cuts per year). To model the life cycle of this crop and to be able to predict harvests "there were two main challenges in the simulation, which were these periodic cuttings and resprouting during the same season, and the fact that alfalfa, as a perennial crop, stores reserves in autumn and uses them in spring to grow, so growth in spring is not only determined by photosynthesis but also by these reserves that the plant stores," explained Professor Fereres.

Therefore, it was necessary to include in the model a routine describing both the transfer of photoassimilates between the aerial part and the underground storage organs and the plant's use of these assimilates for growth.

Yield data collected in Belgium, Turkey, and Canada for different alfalfa cultivars, various years, and different field and irrigation management strategies, was used to calibrate the model. 81 yield data points across different climates, varieties, zones, and irrigation schedules were used to verify this model, which constitutes a robust tool for predicting alfalfa production in different environments.

"The results were very good after this verification. We were able to simulate the performance with very good correlations between the simulated and the real data obtained," Fereresreported, since no systematic overestimation or underestimation was detected by the model.
AquaCrop's future challenges

By introducing the variables of crop, climate, soil, and irrigation management (whether there is water or not and, if there is, how irrigation is distributed) it is possible to simulate the maximum yield that might be obtained in each case. In this way, irrigation can be adapted to optimize management for greater production.

"After 20 years of use it is a very well-optimized application, which has been tested on many crops, and in many environments, and the evidence supports that it works well and is getting better," says Fereres about the application, whose 7th version has just been released, now including the option of modeling alfalfa yield.

In the future the application could be adapted to include woody crops; a challenge, according to Fereres, "since simulating the production of trees is very difficult due to the phenomenon of alternation (trees produce more one year and less the following), and because tree production is determined by the growth and development of previous years."

GPS locations of anonymous cell-phone users (IDs) in the greater Dallas metroplex recorded during February and March 2021: (a) 5 IDs; (b) 50 IDs; (c) 500 IDs; (d) 5000 IDs; The yellow dots are nearby cities mentioned in (a). Credit: Royal Society
GPS locations of anonymous cell-phone users (IDs) in the greater Dallas metroplex recorded during February and March 2021: (a) 5 IDs; (b) 50 IDs; (c) 500 IDs; (d) 5000 IDs; The yellow dots are nearby cities mentioned in (a). Credit: Royal Society

SMU prof Makris measures cell phone data from winter snowstorms that shows Dallas is resilient

Tyler O'Neal, Staff Editor GOVERNMENT

From hurricanes in Houston to winter storms in Dallas, natural disasters can wreak havoc on a city. In any of these situations, policymakers, governing bodies, and aid programs need to know how to measure resilience – the length of time it will take a city to bounce back.

An SMU research team led by engineering professor Nicos Makris measured Dallas’s resilience by looking at anonymous cell phone data among residents in the Dallas metroplex before, during, and after the February 2021 North American winter storm. Their conclusion: Dallas recovered almost immediately after the winter storm ended, indicating Dallas exhibits a great degree of resilience.

“Despite millions of people losing power and water, forcing many to leave their homes immediately after the end of the storm, the city of Dallas reverted back to its pre-event response, showing that the city of Dallas has a great deal of resilience,” Makris said. “Citizens are very resilient individuals. They found ways to revert back.”

Measuring a city’s resilience is important for planning responses to future events and revealing potential vulnerabilities.  The applications for this research extend far beyond Dallas, as United Nations data reveal that more than half of the world’s population currently lives in cities - a number expected to grow to nearly 70 percent by 2050.

Cities serve as global economic and cultural centers, but cities also tend to be in coastal areas and along fault lines, making them prone to acts of nature. This is compounded by climate change, which can enhance the strength or frequency of some of these natural hazards.

The Dallas study was completed by Makris, Addy Family Centennial Professor in Civil Engineering in SMU’s Lyle School of Engineering, along with SMU’s Gholamreza Moghimi, Eric Godat, and Tue Vu. Moghimi is a postdoctoral research fellow at SMU, while Godat is the team leader for research and data science in SMU’s Office of Information Technology (OIT). Vu also works in SMU’s OIT as an AI & ML Research Scientist.

The Dallas results reinforce Makris’ studies of Houston cell phone data after the winter storm as well as data from Hurricanes Harvey (2017) and Irma (2017). Even after the major flooding due to Hurricane Harvey, Houston residents went back to their normal patterns almost immediately after the emergency was over.