To a busy emergency physician, a split lip or a case of poisoning is just one of those things they deal with. But to a computer mining the patient’s medical history, it could be the last diagnosis needed to decipher a pattern of domestic violence.

Now, a group of researchers at Harvard University has created the first computer model to automatically detect the risk that a patient is being abused at home. The results were published Sept. 29 in the British Medical Journal.

“It’s a great concept,” said Debra Houry, an emergency physician at Emory University, who was not involved in the research. Although around one in four women experience domestic violence at some point in their lives, she says, the problem often goes unnoticed at a doctor’s visit. “It’s one of those hidden epidemics where they don’t come up to you and disclose the issue.”

In fact, patients often try to hide the abuse, says Ben Reis, a Harvard pediatrician and computer scientist who designed the new computer model. “Abused people actually go to different emergency rooms each time, so that [the abuse] is harder to track.”

To get around this problem, Reis and his colleagues tapped into a public U.S. database containing six years of medical history for around half a million people. They fed a large portion of the database into a simple computer model — known as a naïve Bayesian classifier — which then calculated the abuse risks linked to different diagnoses such as burns, sprains or mental disorders.

At present, medical records, even electronic ones, may be hard to interpret in the limited time a physician has to see a patient. “It’s usually a big, long wine list,” Reis says. “We reduced the entire history to one picture.”

That picture is called a risk gel. In essence, it shows the patient’s medical history as a bunch of colored bars representing diagnoses made at various visits. A green bar means the diagnosis is not statistically linked to abuse, while a red bar means it is. When the computer determines that the combined abuse risk based on all diagnoses is high, it sounds the alarm, letting the physician know that a face-to-face meeting is called for. “We see this system as a screening support system,” Reis said.

But screening isn’t the end-all, be-all for victims of domestic violence, says Gene Feder of the University of Bristol. He recently reviewed several trials of screening programs and found that none of them measured whether or not screening led to fewer deaths and injuries among abused women.

“Is [the new computer model] suitable for implementation in in-patient hospital and ER hospital settings without further testing?” he wrote in an e-mail to Wired.com. “Not without suitable training for clinicians in how to ask about abuse of the designated high-risk women and how to manage the women safely.”

Still, researchers agree that domestic violence is severely underdetected by health care providers. But it shows up clearly in murder stats. According to the Harvard group, domestic abuse accounts for more than half of the murders of women in the United States. And without detection, there is no chance of helping the victims.

Using the new system, the researchers were able to predict abuse an average of two years before the doctor made the diagnosis. Presumably, the computer is picking up signs of ongoing maltreatment the patient hasn’t yet revealed.

The researchers also speculate that, in principle, some subtle signal could precede direct abuse. One surprise finding that could be relevant, says Reis, is that infections turned out to be strongly linked to abuse. That might suggest worsening hygiene in the family or increased psychological stress, possible omens of abuse. But at this point, it is anybody’s guess whether true predictions are possible.

Predictions or not, with the current model, fewer than 20 percent of the patients flagged as high-risk cases turned out to have a diagnosis of abuse. Part of the problem may be that the system is only as good as the data it was based on. And as Emory University’s Houry points out, that data isn’t up to speed when it comes to diagnosing abuse.

The Harvard researchers counter that their approach shows all the more promise because it works even when based on poor, real-world data. Working on a new government grant, they are now trying to improve the model and incorporate more data, a task that will get easier as electronic medical records become widespread among health care providers.

Within four years, the group hopes to have a full-fledged system ready, including a user interface optimized for doctors. “The long-term vision is one of predictive medicine, where vast amounts of information are used to improve health care, diagnosis, screening and outcomes,” says Reis.

Yet the question remains how to translate a diagnosis into action that will help the victims of abuse. “Identifying in itself is not enough,” says Houry. “But I believe it helps.”

Images: 1) "Treemap visualizations of abuse risk associated with different diagnostic categories (for women) / Reis et. al., BMJ. 2) Risk gel visualization / Intelligenthistories.org