Pulmonary embolism (PE) is a leading cause of preventable in-hospital mortality. Advances in diagnosis, risk stratification, and prevention can improve outcomes. There are few large publicly available datasets that contain PE labels for research. Using the MIMIC-IV database, we extracted all available radiology reports of computed tomography pulmonary angiography (CTPA) scans and two physicians manually labeled the results as PE positive (acute PE) or PE negative. We then applied a previously finetuned Bio_ClinicalBERT transformer language model, VTE-BERT, to extract labels automatically. We verified VTE-BERT's reliability by measuring its performance against manual adjudication. We also compared the performance of VTE-BERT to diagnosis codes. We found that VTE-BERT has a sensitivity of 92.4% and positive predictive value (PPV) of 87.8% on all 19,942 patients with CTPA radiology reports from the emergency room and/or hospital admission. In contrast, diagnosis codes have a sensitivity of 95.4% and PPV of 83.8% on the subset of 11,990 hospitalized patients with discharge diagnosis codes. We successfully add nearly 20,000 labels to CTPAs in a publicly available dataset and demonstrate the external validity of a semi-supervised language model in accelerating hematologic research.

The University of California--Irvine (UCI) Machine Learning (ML) Repository (UCIMLR) is consistently cited as one of the most popular dataset repositories, hosting hundreds of high-impact datasets. However, a significant portion, including 28.4% of the top 250, cannot be imported via the $\textit{ucimlrepo}$ package that is provided and recommended by the UCIMLR website. Instead, they are hosted as .zip files, containing nonstandard formats that are difficult to import without additional ad hoc processing. To address this issue, here we present $\textit{lucie}$ -- $\underline{l}oad$ $\underline{U}niversity$ $\underline{C}alifornia$ $\underline{I}rvine$ $\underline{e}xamples$ -- a utility that automatically determines the data format and imports many of these previously non-importable datasets, while preserving as much of a tabular data structure as possible. $\textit{lucie}$ was designed using the top 100 most popular datasets and benchmarked on the next 130, where it resulted in a success rate of 95.4% vs. 73.1% for $\textit{ucimlrepo}$. $\textit{lucie}$ is available as a Python package on PyPI with 98% code coverage.

Objective: Sepsis is a life-threatening condition. Sequential Organ Failure Assessment (SOFA) score is commonly used to assess organ dysfunction and predict ICU mortality, but it is taken as a static measurement and fails to capture dynamic changes. This study aims to investigate the relationship between dynamic changes in SOFA scores over the first 72 hours of ICU admission and patient outcomes. Design, setting, and participants: 3,253 patients in the Medical Information Mart for Intensive Care IV database who met the sepsis-3 criteria and were admitted from the emergency department with at least 72 hours of ICU admission and full-active resuscitation status were analysed. Group-based trajectory modelling with dynamic time warping and k-means clustering identified distinct trajectory patterns in dynamic SOFA scores. They were subsequently compared using Python. Main outcome measures: Outcomes including hospital and ICU mortality, length of stay in hospital and ICU, and readmission during hospital stay, were collected. Discharge time from ICU to wards and cut-offs at 7-day and 14-day were taken. Results: Four clusters were identified: A (consistently low SOFA scores), B (rapid increase followed by a decline in SOFA scores), C (higher baseline scores with gradual improvement), and D (persistently elevated scores). Cluster D had the longest ICU and hospital stays, highest ICU and hospital mortality. Discharge rates from ICU were similar for Clusters A and B, while Cluster C had initially comparable rates but a slower transition to ward. Conclusion: Monitoring dynamic changes in SOFA score is valuable for assessing sepsis severity and treatment responsiveness.

Recently, a team led by clinicians at Beth Israel Deaconess Medical Center and Harvard Medical School demonstrated that an artificial intelligence (AI)-based computer vision system can enhance screening accuracy of colon cancer. Tyler M Berzin, a gastroenterologist from Beth Israel Deaconess Medical Center, discusses how AI-based computer-vision algorithms can assist physicians. Let us examine how this is accomplished. According to Tyler, this would be a real-time application of artificial intelligence, which is also rather unique. In clinical medicine, the majority of examples of AI applications occur after the initial patient engagement, for example, during the subsequent evaluation of the X-ray.

Recently, a team led by clinicians at Beth Israel Deaconess Medical Center and Harvard Medical School demonstrated that an artificial intelligence (AI)-based computer vision system can enhance screening accuracy of colon cancer. Tyler M Berzin, a gastroenterologist from Beth Israel Deaconess Medical Center, discusses how AI-based computer-vision algorithms can assist physicians. Let us examine how this is accomplished. According to Tyler, this would be a real-time application of artificial intelligence, which is also rather unique. In clinical medicine, the majority of examples of AI applications occur after the initial patient engagement, for example, during the subsequent evaluation of the X-ray.

Researchers at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Beth Israel Deaconess Medical Center have teamed up to improve electronic health records (EHRs) with artificial intelligence (AI) machine learning and published their findings in a recent study. The automation of patient health records gives hope to benefits to clinicians, patients, and stakeholders such as increase speed of data transfer, lower costs in maintaining paper records, increase efficiency, improve outcomes by avoiding or reducing clinical errors. However, electronic health records have yet to achieve many of these positive benefits and is a leading cause of burnout and stress among physicians according to the researchers. Clinicians are spending time on using the electronic health records instead of talking with patients. The worldwide electronic health records market was USD 26.8 billion in 2020 with North America having the highest revenue share of 45 percent according to Grand View Research.

Electronic health records have been widely adopted with the hope they would save time and improve the quality of patient care. But due to fragmented interfaces and tedious data entry procedures, physicians often spend more time navigating these systems than they do interacting with patients. Researchers at MIT and the Beth Israel Deaconess Medical Center are combining machine learning and human-computer interaction to create a better electronic health record (EHR). They developed MedKnowts, a system that unifies the processes of looking up medical records and documenting patient information into a single, interactive interface. Driven by artificial intelligence, this "smart" EHR automatically displays customized, patient-specific medical records when a clinician needs them.

A team of researchers showed that artificial intelligence (AI) could help predict the type of bacteria that caused the infection in patients with pneumonia. The research is presented at ASM Microbe Online, the annual meeting of the American Society for Microbiology. "This research highlights the potential of AI as a supplementary tool for physicians in identifying causal pathogens of pneumonia, even before sputum culture results are available," said Joowhan Sung, M.D., hospitalist at MedStar Southern Maryland Hospital. "We demonstrated that physicians could be assisted by AI to decide appropriate antibiotics." In the study, investigators showed that AI could use the information available in the emergency room and predict if the patient has MRSA or pseudomonas so that physicians can immediately prescribe specific antibiotics targeting specific bacteria.

Amazon thinks the cloud can make hospitals run better. The company's Amazon Web Services unit is sponsoring a program at Harvard's Beth Israel Deaconess Medical Center (BIDMC) that applies machine learning to improve hospital efficiency. The initiative will focus on clinical care, operations and waste reduction, which Amazon said will ultimately improve patient care. The Amazon grant will supply as much as $2 million to machine learning research, Bloomberg reported. "Every minute spent on cumbersome clerical tasks and management adds up to millions in lost productivity and directly impacts patient care," Dr. John Halamka, executive director of the Health Technology Exploration Center at Beth Israel Deaconess Medical Center, said in a statement.

THE ARTICLE WAS ORIGINALLY PUBLISHED ON THE NEXT WEB. AI, Machine Learning, and Big Data open a vast horizon and many opportunities for the Healthcare Industry. When such tech giants as Google or IBM appear in the field of healthcare, we know it is something worth exploring. AI Researcher Frost & Sullivan said artificial intelligence systems will generate $6.7 billion in global revenue from healthcare by 2021, compared with $811 million in 2015. It is bigger than what we think.