When Will You Die Calculator – This calculator can reveal how long you can live, if you dare to find out.
All you need is your age, gender and zip code. These three factors influence life expectancy in the UK.
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How long will you live? Use the calculator to find out – as life expectancy for men decreases Credit: Alamy
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Men in Warfield Harvest Ride in Berkshire can expect to live more than 20 years longer (90.3) than men in Bloomfield in Blackpool (68.2).
The Office for National Statistics (ONS) calculator is based on older data, but is the most recent version.
Children born between 2018 and 2020 are expected to live between 79.2 and 79 years in 2015-2017.
Office for National Statistics (ONS) estimates for girls remain largely unchanged.
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But both figures are higher than in the early 1980s, when boys were expected to live 70.8 years and girls 76.8 years.
Men are more likely than women to die from the disease, and there were more deaths than usual last year.
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So the numbers don’t necessarily mean that babies born between 2018 and 2020 will have shorter lives.
“These estimates are based on the assumption that current abnormally high mortality levels will continue for life,” said Pamela Cobb, from the ONS Center for Aging and Demography.
“Life expectancy may return to improving trends in the future once the coronavirus pandemic and its impact on future mortality are over.”
He continued: “Britain’s life expectancy has increased over the past 40 years, but the pace has slowed over the past decade.
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“As a result, the latest estimates show that while life expectancy for women has improved little, life expectancy for men has returned to levels reported between 2012 and 2014.
“This is the first time we’ve seen a decline when comparing non-overlapping time periods since the series began in the early 1980s.”
The data suggests that men born in North East England or Yorkshire live four months less.
However, in the West Midlands and Northwest, the decline was only three months, and for men in the South West it increased by one month.
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This is another example of the impact of the coronavirus in 2020, as the South West has lower Covid-19 death rates for men and women than other regions.
In the meantime, you can use this calculator to find out your risk of a heart attack or stroke in the next 10 years.
Email Exclusive@.co.uk or call 02077824104. Click here to load Does AI predict death? Stanford University researchers have developed an AI that can predict when a patient will die with up to 90% accuracy.
Using artificial intelligence to predict when a patient will die sounds like an episode of the dystopian sci-fi TV series Black Mirror. But Stanford University researchers see this use of artificial intelligence as a perfect opportunity to help doctors and patients have much-needed end-of-life conversations sooner.
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Many doctors give optimistic estimates of when their patients will die, often delaying difficult conversations about end-of-life options. Rather than being allowed to die, it can lead to unwanted, expensive and aggressive treatment in hospitals as they die. Use I to help. Physicians screen newly admitted patients who could benefit from a conversation about palliative care options.
Previous polls have shown that about 80% of Americans would prefer to spend their last days at home if possible. In fact, according to a study cited in the Stanford team’s paper, “Deep Learning Improves Palliative Care,” published on the arXiv preprint server, up to 60% of Americans experience an acute phase while receiving active treatment. He died in the hospital.
Palliative care professionals often wait until the medical team assigned to a particular patient requests services. This includes recording the terminal patient’s relief and, in some cases, end-of-life treatment preferences in a living will. But Stephanie Herman, an internist and founder of palliative care services at Stanford Health Care, believes that giving palliative care physicians the ability to be aware of their relationship with their patients and actively communicate with them can help them improve their routines. reverse
Harman pitched the idea to Nigam Shah, an associate professor of medicine and biomedical informatics at Stanford University. Shah talks to Andrew Ng, an adjunct professor at Stanford University and former head of Baidu AI Group, about potential collaborations related to AI in healthcare. They agreed that the idea of palliative care seemed like a good project to explore together.
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The Stanford team’s AI algorithms rely on deep learning. Deep learning is a popular machine learning technique that uses neural networks to filter and learn from large amounts of data. The researchers trained deep learning algorithms on the electronic medical records of nearly two million adult and pediatric patients admitted to Stanford Hospital or Lucille Packard Children’s Hospital to predict mortality in the next three to 12 months for a given patient. (Predicting the patient’s death within three months leaves insufficient time for the necessary preparations for palliative care).
Anand Avati, who is pursuing a PhD in computer science at Stanford University’s Artificial Intelligence Laboratory, said: “The scale of the available data allowed us to develop an all-cause mortality prediction model rather than a specific model of disease or demographics”.
Using a pilot study of the algorithm (approved by the institutional review board) to predict patient mortality proved less difficult than one might think. From an ethical and medical care perspective, the help of deep learning models to help human doctors screen patients for palliative care generally has great advantages and few disadvantages.
“Retaining physicians and thinking of this as ‘doctor machine learning’ is the exact opposite of blindly algorithmically based medical interventions and establishes a strong foundation both ethically and safely. We think,” said Kenneth Jung, a research scientist at Stanford University.
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A potential problem with deep learning algorithms is that even their creators often cannot explain why a deep learning model produces a certain result. The black-box nature of deep learning often means it’s hard to tell how the Stanford team’s models arrive at the conclusion that a given patient is likely to die within a year.
Fortunately, the reasoning behind the deep learning model’s mortality prediction is not particularly important in this case. The palliative care team does not need to know exactly why an algorithm predicts that a particular patient may die within a year, but rather to identify patients who may benefit from care. I am mainly interested in accurately identifying the . Jung explained:
Therefore, in this particular case it is more convenient to use the black box model. Palliative care interventions have nothing to do with the cause of the disease. If this is another hypothetical case of “someone dies and I have to choose a treatment option”, then I’d like to understand cause by treatment. But with this setup it doesn’t matter as long as it’s done right.
However, it can be useful to know why deep learning models made predictions for research purposes. In this case, a team at Stanford University used a common error analysis technique called ablation analysis to provide insight into the decision-making of deep learning models. Their method gradually tunes the model by adjusting individual parameters and learning how those parameters affect the model’s decisions.
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The Stanford University group also emphasized that patients do not have to be close to death’s door to benefit from palliative care. There is evidence, although low, that it is often beneficial for doctors to discuss the end of life with terminally ill patients.
All in all, it’s not a bad thing for deep learning models to focus on predicting death. Mortality is relatively straightforward—that is, when the person dies—compared to the researchers’ primary interest in finding the optimal time for a patient to be visited by the palliative care team, it is a useful indicator of whether
The Stanford team plans to measure the success of the pilot study based on outcomes such as: