Diagnosis


Algorithm for Identifying Ocular Conditions in Electronic Health Record Data

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Results This study included 122 339 patients, with a mean (SD) age of 52.4 (25.1) years. Of these patients, 69 002 (56.4%) were female and 99 579 (81.4%) were white. The algorithm assigned a less than 10% probability of XFS for 121 085 patients (99.0%) as well as an XFS probability score of more than 75% for 543 patients (0.4%), more than 90% for 353 patients (0.3%), and more than 99% for 83 patients (0.07%). When there was ICD-9 or ICD-10 billing code documentation of XFS, in 86% or 96% of the records, respectively, evidence of XFS was also recorded elsewhere in the EHR. Conversely, when there was clinical examination or free-text evidence of XFS, it was documented with ICD-9 codes only approximately 40% of the time and even less often with ICD-10 codes.


Artificial Intelligence Decision Tree

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In this article we will discuss decision points for selecting right components for Artificial Intelligence (AI) solutions. This is also an update to Machine Learning Decision Tree (v1). Keep in mind here that AI is a broader term compared to Machine Learning.


China has produced another study showing the potential of AI in medical diagnosis

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A new study from China has found that an AI system can best some doctors when it comes to diagnosing common childhood diseases. The study, published in Nature Medicine yesterday (Feb. The study trained a deep-learning system on 101 million data points generated from the electronic records of 1.3 million patient visits to a medical center in Guangzhou. Researchers found that the AI system was able to meet or outperform two groups of junior physicians in accurately diagnosing a range of ailments, from asthma and pneumonia, to sinusitis and mouth-related diseases. The AI was also able to meet or exceed diagnostic performance with some groups of senior physicians, for instance, in the category of upper respiratory issues.


A.I. Shows Promise as a Physician Assistant

AITopics Custom Links

Drawing on the records of nearly 600,000 Chinese patients who had visited a pediatric hospital over an 18-month period, the vast collection of data used to train this new system highlights an advantage for China in the worldwide race toward artificial intelligence. Because its population is so large -- and because its privacy norms put fewer restrictions on the sharing of digital data -- it may be easier for Chinese companies and researchers to build and train the "deep learning" systems that are rapidly changing the trajectory of health care. On Monday, President Trump signed an executive order meant to spur the development of A.I. across government, academia and industry in the United States. As part of this "American A.I. Initiative," the administration will encourage federal agencies and universities to share data that can drive the development of automated systems. Pooling health care data is a particularly difficult endeavor.


Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence

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Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive electronic health record (EHR) data remains challenging. Here, we show that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found. Our model applies an automated natural language processing system using deep learning techniques to extract clinically relevant information from EHRs. In total, 101.6 million data points from 1,362,559 pediatric patient visits presenting to a major referral center were analyzed to train and validate the framework.


Adaptive Exact Learning of Decision Trees from Membership Queries

arXiv.org Machine Learning

In this paper we study the adaptive learnability of decision trees of depth at most $d$ from membership queries. This has many applications in automated scientific discovery such as drugs development and software update problem. Feldman solves the problem in a randomized polynomial time algorithm that asks $\tilde O(2^{2d})\log n$ queries and Kushilevitz-Mansour in a deterministic polynomial time algorithm that asks $ 2^{18d+o(d)}\log n$ queries. We improve the query complexity of both algorithms. We give a randomized polynomial time algorithm that asks $\tilde O(2^{2d}) + 2^{d}\log n$ queries and a deterministic polynomial time algorithm that asks $2^{5.83d}+2^{2d+o(d)}\log n$ queries.



Entropy: How Decision Trees Make Decisions – Towards Data Science

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You've come a long way from writing your first line of Python or R code. You know your way around Scikit-Learn like the back of your hand. You spend more time on Kaggle than Facebook now. You're no stranger to building awesome random forests and other tree based ensemble models that get the job done. You want to dig deeper and understand some of the intricacies and concepts behind popular machine learning models.


Woman links Lyme disease diagnosis to pet cat sleeping on bed

FOX News

Lisa Vallo, an artist, said she would occasionally find ticks in her pet's coat, and that the cat often napped on her bed. A 51-year-old woman who was suffering from symptoms that she says made life intolerable for over a decade has linked her Lyme disease diagnosis three years ago to an incident involving her cat back in 2002. Lisa Vallo, an artist, said she would occasionally find ticks in her pet's coat, and that the cat often napped on her bed. But it took Vallo nine doctors, several misdiagnoses and years of dealing with a racing heart, nausea, arthritis and extreme tiredness before she was diagnosed with Lyme, and then finally made the connection back to her cat, SWNS reported. "I woke up in the morning and noticed a black dot on my tummy," Vallo, of West Yorkshire, told the news agency about the 2002 incident.


Calibration with Bias-Corrected Temperature Scaling Improves Domain Adaptation Under Label Shift in Modern Neural Networks

arXiv.org Machine Learning

Label shift refers to the phenomenon where the marginal probability p(y) of observing a particular class changes between the training and test distributions while the conditional probability p(x|y) stays fixed. This is relevant in settings such as medical diagnosis, where a classifier trained to predict disease based on observed symptoms may need to be adapted to a different distribution where the baseline frequency of the disease is higher. Given calibrated estimates of p(y|x), one can apply an EM algorithm to correct for the shift in class imbalance between the training and test distributions without ever needing to calculate p(x|y). Unfortunately, modern neural networks typically fail to produce well-calibrated probabilities, compromising the effectiveness of this approach. Although Temperature Scaling can greatly reduce miscalibration in these networks, it can leave behind a systematic bias in the probabilities that still poses a problem. To address this, we extend Temperature Scaling with class-specific bias parameters, which largely eliminates systematic bias in the calibrated probabilities and allows for effective domain adaptation under label shift. We term our calibration approach "Bias-Corrected Temperature Scaling". On experiments with CIFAR10, we find that EM with Bias-Corrected Temperature Scaling significantly outperforms both EM with Temperature Scaling and the recently-proposed Black-Box Shift Estimation.