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For the psychiatric data set, topics were learned using the LDA Python package 27 whereas for the ICU clinical notes, topics were learned using Mallet. Following prior work on enrichment of topics in clinical notes, 13,26 we computed enrichment values for topics for race, gender, and insurance type. We report the area under the receiver operator curve AUC 31 for overall model performance as well as the generalized zero-one loss as a performance metric. All Tukey range test error rate comparisons were performed using the Python package statsmodels.

Psychiatric note topics. White patients had higher topic enrichment values for the anxiety 36 and chronic pain topics, while black, Hispanic, and Asian patients had higher topic enrichment values for the psychosis topic. However, public insurance patients have higher topic enrichment values than private insurance patients for substance abuse 0. ICU note topics. Intensive care unit clinical notes have a different range of topics see Supplementary Appendix Table S3 and more refined topics than psychiatric notes due to the larger data source 25 v patients. As in the psychiatric data set, male patients have higher topic enrichment values for substance use than female patients 0.

Public and private insurance patients vary mainly in the severity of conditions they are being treated for. Those with public insurance often have multiple chronic conditions that require regular care. However, compared with public insurance patients, private insurance patients have higher topic enrichment values for fractures 0. In sum, our results for gender and race reflect known specific clinical findings, whereas our results for insurance type reflect known differences in patterns of ICU usage between public insurance patients and private insurance patients.

After establishing that findings from the clinical notes reflect known disparities in patient population and experience, we evaluated whether predictions made from such notes are fair. There are multiple definitions of algorithmic fairness ; here we compare differences in error rates in ICU mortality and day psychiatric readmission for race, gender, and insurance type. Prediction error in the ICU model. Unstructured clinical notes are a powerful source of information in predicting patient mortality—our models achieve an AUC 31 of 0. Adding demographic information age, race, gender, insurance type , improves AUC slightly, to 0.

As shown in Figures 1 and 2, error rates for gender and insurance type all have nonoverlapping confidence intervals. For gender, female patients have a higher model error rate than male patients; for insurance type, public insurance patients have a much higher model error rate than private insurance patients. Figure 1.

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Figure 2. Prediction in the psychiatric setting. In contrast to ICU mortality, predicting day psychiatric readmission is significantly more challenging, leading to lower model accuracy. Comparison of prediction errors in ICU and psychiatric models.

We compare differences in error rates in day psychiatric readmission and ICU mortality for race, gender, and insurance type. Figure 3 shows differences in error rates in psychiatric readmission between racial groups, which were not statistically significant, with black patients having the highest error rate for psychiatric readmission. Differences in error rates in ICU mortality were also observed between racial groups. Figure 3. We show consistent gender differences across data sets in Figures 1 and 4, with the highest error rates for female patients, although the difference in error rates between genders was only statistically significant for ICU mortality.

Note that because of the smaller size of the psychiatric notes data set, the confidence intervals overlap; however, the heterogeneity in topic enrichment values aligns with the higher error rates for female patients. Figure 4.

Four U.S. CRISPR Trials Editing Human DNA to Research New Treatments

Interestingly, model prediction errors for insurance type were statistically significant for both data sets Figures 2 and 5 , but the group with highest error rate changes. While public insurance patients have the highest error rate for ICU mortality, private insurance patients have the highest error rate for psychiatric readmission. Figure 5. These differences in error rates for insurance type may indicate that insurance type affects patient care in ICU and psychiatric settings differently. We note that public insurance patients have higher baseline hospital mortality rates, whereas private insurance patients have higher baseline day psychiatric readmission see Supplementary Appendix Table S1.

Such variation in baseline rates could be due to the previously noted prevalence of chronic conditions in public insurance patients, 45 making these patients more likely to need the ICU for regular care of multiple chronic conditions.

Public insurance patients are also more likely to have serious mental illness than private insurance patients, 39 indicating that they may not come into a psychiatric hospital unless the situation is dire. In both data sets, predictions are better captured by notes for patients in the group that uses the care setting more regularly ie, public insurance patients in the ICU and private insurance patients in the psychiatric hospital.

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AI and machine learning may enable faster, more accurate, and more comprehensive health care. We believe a closely cooperative relationship between clinicians and AI—rather than a competitive one 53 —is necessary for illuminating areas of disparate health care impact. Indeed, algorithmic scrutiny is vital to both the short-term and long-term robustness of the health care system. In this paper, we have considered questions related to the disparate impact that AI may have in health care—in particular, on ICU mortality and day psychiatric readmissions.

Based on clinical notes, we demonstrated heterogeneity in the topics emphasized across race, gender, and insurance type, which tracks with known health disparities. We also showed statistically significant differences in error rates in ICU mortality for race, gender, and insurance type and in day psychiatric readmission for insurance type.

In light of known clinical biases, how can AI assist in improving patient care? With increasing involvement of machine learning in health care decisions, it is crucial to assess any algorithmic biases introduced 54 by comparing prediction accuracy between demographic groups.

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The disease, which occurs most frequently in people of African descent, affects a protein called hemoglobin, which plays a critical role in helping red blood cells carry oxygen to different tissues in the body. Sickle cell causes hemoglobin proteins to clump into long fibers that warp disc-shaped red blood cells into sickle shapes.

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Stem cells are collected from the bloodstream and edited with CRISPR so they will pump out high levels of fetal hemoglobin, a protein that typically dwindles to trace levels after infancy. Fetal hemoglobin HbF is encoded by an entirely different gene than beta-globin, the part of hemoglobin that can cause red blood cells to sickle. Adults with sickle cell whose bodies naturally make more HbF often experience less severe symptoms. Doctors will look for the treatment to generate 20 percent or more HbF in the bloodstream for at least three months.

If successful, the therapy would offer another option for a disease with few available treatments. The only current cure for sickle cell disease is a bone marrow transplant, but, according to the National Heart, Blood, and Lung Institute , such transplants work best in children and the likelihood of finding a marrow donor match is low.

Unlike the University of Pennsylvania trial, the study involves editing T cells from donors. A poster from the researchers explains that a prototype treatment in mice with acute leukemia stalled tumor growth for about 60 days. Additionally, lab tests showed that modified human T cells were successfully able to target and kill CDmarked cancer cells. For the clinical trial, which will eventually include a maximum of 95 participants, researchers will track how patients tolerate different doses of the T cell treatment and how many patients see their cancers shrink or disappear entirely.

After the treatment is complete, scientists will keep tabs on patients and their survival and recurrence rates over the course of five years. It will be the first instance of a CRISPR clinical trial that conducts cellular editing within a human body, or in vivo. The trial will include about 18 participants, including patients as young as age 3, with a particular subset of LCA caused by a single genetic mutation that impairs photoreceptors.

These cells in the eye convert light into signals for the brain to process. The treatment comes in the form of an injection into the space behind the retina. Medical researchers aim to affect 10 percent or more of the targeted photoreceptor cells, the threshold that other research suggests is required to make a leap in visual acuity. The EDIT treatment has been tested in non-human primates and also in tiny samples of a donated human retina.

The method of injecting a virus subretinally to treat LCA has been successful before. Early clinical trials are not without risks. In , an year-old participant named Jesse Gelsinger died in a Phase 1 gene therapy trial—a tragedy that still lingers over the field.

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Gelsinger had inherited a metabolic disorder, and like other patients in the trial, received an injection straight to his liver of the ammonia-digesting gene his body lacked. Four days later, multiple organs failed , and Gelsinger was taken off life support.

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After his death, investigations uncovered a tangle of ethical lapses. In the U. Other gene therapies have been successful before, like the cancer treatments Kymriah and Yescarta. But unlike most other gene editing techniques, CRISPR is relatively easy to engineer and use, opening up the floodgates for possible applications. We publish monographs and textbooks in all areas, offering the academic excellence of a traditional press combined with the speed, convenience and accessibility of digital publishing.

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