Accurate assessment of the optic-nerve head, the optic disk, by funduscopy is an important, cost-effective, and noninvasive diagnostic tool for a variety of ocular, neurologic, and inflammatory ...conditions. Unfortunately, reliable funduscopic assessment is challenging for clinicians working with undilated pupils
1
; even after mydriatic dilation, nonophthalmologists have considerably lower accuracy
2
than neuro-ophthalmologists in detecting optic-disk disorders, including papilledema. Could computer programs examining digital funduscopic images perform at the level of neuro-ophthalmologists in classifying disorders of the optic disk? The authors of the study now published in the
Journal
3
were encouraged by previous studies for the detection of diabetic retinopathy
4
to undertake . . .
Big Data and Machine Learning in Health Care Beam, Andrew L; Kohane, Isaac S
JAMA : the journal of the American Medical Association,
04/2018, Volume:
319, Issue:
13
Journal Article
Peer reviewed
The article discusses the impact that big data and machine learning is having on health care. Some of the different ways in which machine learning can be applied to health care are highlighted. The ...use of big data in the health care sector is included.
If genomic studies are to be a clinically relevant and timely reflection of the relationship between genetics and health status--whether for common or rare variants--cost-effective ways must be found ...to measure both the genetic variation and the phenotypic characteristics of large populations, including the comprehensive and up-to-date record of their medical treatment. The adoption of electronic health records, used by clinicians to document clinical care, is becoming widespread and recent studies demonstrate that they can be effectively employed for genetic studies using the informational and biological 'by-products' of health-care delivery while maintaining patient privacy.
Full text
Available for:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Major but surmountable hurdles should be addressed now to hasten the advent of precision medicine.
On 30 January 2015, President Obama announced funding for an Initiative in Precision Medicine (IPM) ...(
1
) less than 3 years after a National Academy of Sciences (NAS) committee report (
2
) made clear just how such an initiative could accelerate progress in medical care and research. The core concept of this initiative is that by harnessing measurements of multiple modalities—not just clinical and genomic evaluations, but environmental exposures, daily activities, and many others, we can develop a much more comprehensive view of the patient's state and its trajectory over time. By understanding precisely, across all these modalities, what the distinguishing features of specific subgroups of patients are, we can better individualize therapies.
Full text
Available for:
BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
AbstractObjectiveTo evaluate on a large scale, across 272 common types of laboratory tests, the impact of healthcare processes on the predictive value of electronic health record (EHR) ...data.DesignRetrospective observational study.SettingTwo large hospitals in Boston, Massachusetts, with inpatient, emergency, and ambulatory care.ParticipantsAll 669 452 patients treated at the two hospitals over one year between 2005 and 2006.Main outcome measuresThe relative predictive accuracy of each laboratory test for three year survival, using the time of the day, day of the week, and ordering frequency of the test, compared to the value of the test result.ResultsThe presence of a laboratory test order, regardless of any other information about the test result, has a significant association (P<0.001) with the odds of survival in 233 of 272 (86%) tests. Data about the timing of when laboratory tests were ordered were more accurate than the test results in predicting survival in 118 of 174 tests (68%).ConclusionsHealthcare processes must be addressed and accounted for in analysis of observational health data. Without careful consideration to context, EHR data are unsuitable for many research questions. However, if explicitly modeled, the same processes that make EHR data complex can be leveraged to gain insight into patients’ state of health.
Full text
Available for:
BFBNIB, CMK, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Many countries are experiencing a resurgence of COVID-19, driven predominantly by the delta (B.1.617.2) variant of SARS-CoV-2. In response, these countries are considering the administration of a ...third dose of mRNA COVID-19 vaccine as a booster dose to address potential waning immunity over time and reduced effectiveness against the delta variant. We aimed to use the data repositories of Israel's largest health-care organisation to evaluate the effectiveness of a third dose of the BNT162b2 mRNA vaccine for preventing severe COVID-19 outcomes.
Using data from Clalit Health Services, which provides mandatory health-care coverage for over half of the Israeli population, individuals receiving a third vaccine dose between July 30, 2020, and Sept 23, 2021, were matched (1:1) to demographically and clinically similar controls who did not receive a third dose. Eligible participants had received the second vaccine dose at least 5 months before the recruitment date, had no previous documented SARS-CoV-2 infection, and had no contact with the health-care system in the 3 days before recruitment. Individuals who are health-care workers, live in long-term care facilities, or are medically confined to their homes were excluded. Primary outcomes were COVID-19-related admission to hospital, severe disease, and COVID-19-related death. The third dose effectiveness for each outcome was estimated as 1 – risk ratio using the Kaplan-Meier estimator.
1 158 269 individuals were eligible to be included in the third dose group. Following matching, the third dose and control groups each included 728 321 individuals. Participants had a median age of 52 years (IQR 37–68) and 51% were female. The median follow-up time was 13 days (IQR 6–21) in both groups. Vaccine effectiveness evaluated at least 7 days after receipt of the third dose, compared with receiving only two doses at least 5 months ago, was estimated to be 93% (231 events for two doses vs 29 events for three doses; 95% CI 88–97) for admission to hospital, 92% (157 vs 17 events; 82–97) for severe disease, and 81% (44 vs seven events; 59–97) for COVID-19-related death.
Our findings suggest that a third dose of the BNT162b2 mRNA vaccine is effective in protecting individuals against severe COVID-19-related outcomes, compared with receiving only two doses at least 5 months ago.
The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The Clinician and Dataset Shift in Artificial Intelligence Finlayson, Samuel G; Subbaswamy, Adarsh; Singh, Karandeep ...
New England journal of medicine/The New England journal of medicine,
07/2021, Volume:
385, Issue:
3
Journal Article
Peer reviewed
Open access
This letter outlines how to identify, and potentially mitigate, common sources of “dataset shift” in machine-learning systems. This occurs when the model “training data” differ from the data used by ...the model to provide diagnostic, prognostic, or treatment advice.
This study uses Centers for Disease Control and Prevention (CDC) data to characterize trends in suicide rates among adolescents and young adults aged 15 to 24 years in the United States and to ...determine if the increase in suicide rates observed in years 2000-2016 is continuing.
Use electronic health records Autism Spectrum Disorder (ASD) to assess the comorbidity burden of ASD in children and young adults.
A retrospective prevalence study was performed using a distributed ...query system across three general hospitals and one pediatric hospital. Over 14,000 individuals under age 35 with ASD were characterized by their co-morbidities and conversely, the prevalence of ASD within these comorbidities was measured. The comorbidity prevalence of the younger (Age<18 years) and older (Age 18-34 years) individuals with ASD was compared.
19.44% of ASD patients had epilepsy as compared to 2.19% in the overall hospital population (95% confidence interval for difference in percentages 13.58-14.69%), 2.43% of ASD with schizophrenia vs. 0.24% in the hospital population (95% CI 1.89-2.39%), inflammatory bowel disease (IBD) 0.83% vs. 0.54% (95% CI 0.13-0.43%), bowel disorders (without IBD) 11.74% vs. 4.5% (95% CI 5.72-6.68%), CNS/cranial anomalies 12.45% vs. 1.19% (95% CI 9.41-10.38%), diabetes mellitus type I (DM1) 0.79% vs. 0.34% (95% CI 0.3-0.6%), muscular dystrophy 0.47% vs 0.05% (95% CI 0.26-0.49%), sleep disorders 1.12% vs. 0.14% (95% CI 0.79-1.14%). Autoimmune disorders (excluding DM1 and IBD) were not significantly different at 0.67% vs. 0.68% (95% CI -0.14-0.13%). Three of the studied comorbidities increased significantly when comparing ages 0-17 vs 18-34 with p<0.001: Schizophrenia (1.43% vs. 8.76%), diabetes mellitus type I (0.67% vs. 2.08%), IBD (0.68% vs. 1.99%) whereas sleeping disorders, bowel disorders (without IBD) and epilepsy did not change significantly.
The comorbidities of ASD encompass disease states that are significantly overrepresented in ASD with respect to even the patient populations of tertiary health centers. This burden of comorbidities goes well beyond those routinely managed in developmental medicine centers and requires broad multidisciplinary management that payors and providers will have to plan for.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
PDF
