A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain DOI Open Access
Pir Noman Ahmad, Adnan Muhammad Shah, Kang Yoon Lee

et al.

Healthcare, Journal Year: 2023, Volume and Issue: 11(9), P. 1268 - 1268

Published: April 28, 2023

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies entities with special meanings, such as people, places, and organizations, predefined semantic types electronic health records (EHR). bNER essential for discovering novel knowledge using computational methods Information Technology. Early systems were configured manually to include domain-specific features rules. However, these limited handling the complexity of text. Recent advances deep learning (DL) have led development more powerful systems. DL-based can learn patterns text automatically, making them robust efficient than traditional rule-based This paper reviews healthcare domain bNER, DL techniques artificial intelligence clinical records, mining treatment prediction. bNER-based tools are categorized systematically represent distribution input, context, tag (encoder/decoder). Furthermore, create a labeled dataset our machine sentiment analyzer analyze set tweets, we used manual coding approach multi-task method bias training signals inductively. To conclude, discuss challenges facing future directions field.

Language: Английский

Metastasis DOI Creative Commons

Stefanie Gerstberger,

Qingwen Jiang, Karuna Ganesh

et al.

Cell, Journal Year: 2023, Volume and Issue: 186(8), P. 1564 - 1579

Published: April 1, 2023

Language: Английский

Citations

354
Digital twin for healthcare systems DOI Creative Commons
Alexandre Vallée

Frontiers in Digital Health, Journal Year: 2023, Volume and Issue: 5

Published: Sept. 7, 2023

Digital twin technology is revolutionizing healthcare systems by leveraging real-time data integration, advanced analytics, and virtual simulations to enhance patient care, enable predictive optimize clinical operations, facilitate training simulation. With the ability gather analyze a wealth of from various sources, digital twins can offer personalized treatment plans based on individual characteristics, medical history, physiological data. Predictive analytics preventive interventions are made possible machine learning algorithms, allowing for early detection health risks proactive interventions. operations analyzing workflows resource allocation, leading streamlined processes improved care. Moreover, provide safe realistic environment professionals their skills practice complex procedures. The implementation in has potential significantly improve outcomes, safety, drive innovation industry.

Language: Английский

Citations

84
Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare DOI Creative Commons
Lara Marques, Bárbara Costa, Mariana Pereira

et al.

Pharmaceutics, Journal Year: 2024, Volume and Issue: 16(3), P. 332 - 332

Published: Feb. 27, 2024

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in revolutionary era healthcare by individualizing diagnostics and according to each patient’s uniquely evolving health status. This groundbreaking method tailoring disease prevention treatment considers individual variations genes, environments, lifestyles. goal precision target the “five rights”: right patient, drug, time, dose, route. In this pursuit, silico techniques have emerged as an anchor, driving forward making realistic promising avenue for personalized therapies. With advancements high-throughput DNA sequencing technologies, genomic data, including genetic variants their interactions with other environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) pharmacodynamic (PD) mathematical models further contribute drug optimization, behavior prediction, drug–drug interaction identification. Digital health, wearables, computational tools offer continuous monitoring real-time data collection, enabling adjustments. Furthermore, incorporation extensive datasets tools, such electronic records (EHRs) omics also another pathway acquire meaningful information field. Although they are fairly new, machine learning (ML) algorithms artificial intelligence (AI) resources researchers use analyze big develop predictive models. review explores interplay these multiple approaches advancing fostering healthcare. Despite intrinsic challenges, ethical considerations, protection, need more comprehensive research, marks new patient-centered Innovative hold potential reshape future generations come.

Language: Английский

Citations

74
Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation DOI Creative Commons
Tabish Ali, Sarfaraz Ahmed, Muhammad Aslam

et al.

Antibiotics, Journal Year: 2023, Volume and Issue: 12(3), P. 523 - 523

Published: March 6, 2023

Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It very important understand and apply effective strategies counter the impact of AMR its mutation from medical treatment point view. The intersection artificial intelligence (AI), especially deep learning/machine learning, has led new direction in antimicrobial identification. Furthermore, presently, availability huge amounts data multiple sources made it more use these techniques identify interesting insights into genes such genes, mutations, drug identification, conditions favorable spread, so on. Therefore, this paper presents review state-of-the-art challenges opportunities. These include input features posing use, deep-learning/machine-learning models for robustness high accuracy, challenges, prospects practical purposes. concludes with encouragement AI sector intention diagnosis treatment, since presently most studies are at early stages minimal application practice disease.

Language: Английский

Citations

72
A guide to artificial intelligence for cancer researchers DOI
Raquel Pérez-López, Narmin Ghaffari Laleh, Faisal Mahmood

et al.

Nature reviews. Cancer, Journal Year: 2024, Volume and Issue: 24(6), P. 427 - 441

Published: May 16, 2024

Language: Английский

Citations

64
Less is more: Antibiotics at the beginning of life DOI Creative Commons
Martin Stocker, Claus Klingenberg, Lars Navér

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: April 27, 2023

Abstract Antibiotic exposure at the beginning of life can lead to increased antimicrobial resistance and perturbations developing microbiome. Early-life microbiome disruption increases risks chronic diseases later in life. Fear missing evolving neonatal sepsis is key driver for antibiotic overtreatment early Bias (a systemic deviation towards overtreatment) noise random scatter) affect decision-making process. In this perspective, we advocate a factual approach quantifying burden treatment relation disease balancing stewardship effective management.

Language: Английский

Citations

56
Treatment of pulmonary arterial hypertension: recent progress and a look to the future DOI Creative Commons
Marc Humbert, Olivier Sitbon, Christophe Guignabert

et al.

The Lancet Respiratory Medicine, Journal Year: 2023, Volume and Issue: 11(9), P. 804 - 819

Published: Aug. 14, 2023

Language: Английский

Citations

56
Tribulations and future opportunities for artificial intelligence in precision medicine DOI Creative Commons
Claudio Carini, Attila A. Seyhan

Journal of Translational Medicine, Journal Year: 2024, Volume and Issue: 22(1)

Published: April 30, 2024

Abstract Upon a diagnosis, the clinical team faces two main questions: what treatment, and at dose? Clinical trials' results provide basis for guidance support official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate reported response from relevant trials. The decision complexity increases with combination treatments where drugs administered together can interact each other, which is often case. Additionally, individual's treatment varies changes in condition. In practice, drug dose selection depend significantly on medical protocol team's experience. As such, are inherently varied suboptimal. Big data Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit application. AI rapidly evolving dynamic field potential revolutionize various aspects of human life. has become increasingly crucial discovery development. enhances across different disciplines, such medicinal chemistry, molecular cell biology, pharmacology, pathology, practice. addition these, contributes patient population stratification. need healthcare evident it aids enhancing accuracy ensuring quality care necessary effective treatment. pivotal improving success rates increasing significance discovery, development, trials underscored by many scientific publications. Despite numerous advantages AI, advancing Precision Medicine (PM) remote monitoring, unlocking its full requires addressing fundamental concerns. These concerns include quality, lack well-annotated large datasets, privacy safety issues, biases algorithms, legal ethical challenges, obstacles related cost implementation. Nevertheless, integrating medicine will improve diagnostic outcomes, contribute more efficient delivery, reduce costs, facilitate better experiences, making sustainable. This article reviews applications development sustainable, highlights limitations applying AI.

Language: Английский

Citations

32
Artificial intelligence in neurology: opportunities, challenges, and policy implications DOI

Sebastian Voigtlaender,

Johannes Pawelczyk,

Mario Geiger

et al.

Journal of Neurology, Journal Year: 2024, Volume and Issue: 271(5), P. 2258 - 2273

Published: Feb. 17, 2024

Language: Английский

Citations

28
Privacy Preservation of Electronic Health Records in the Modern Era: A Systematic Survey DOI Open Access
Raza Nowrozy, Khandakar Ahmed, A. S. M. Kayes

et al.

ACM Computing Surveys, Journal Year: 2024, Volume and Issue: 56(8), P. 1 - 37

Published: March 19, 2024

Building a secure and privacy-preserving health data sharing framework is topic of great interest in the healthcare sector, but its success subject to ensuring privacy user data. We clarified definitions privacy, confidentiality security (PCS) because these three terms have been used interchangeably literature. found that researchers developers must address differences when developing electronic record (EHR) solutions. surveyed 130 studies on EHRs, techniques, tools were published between 2012 2022, aiming preserve EHRs. The observations findings summarized with help identified framed along survey questions addressed literature review. Our suggested usage access control, blockchain, cloud-based, cryptography techniques common for EHR sharing. commonly strategies preserving are implemented by various tools. Additionally, we collated comprehensive list similarities PCS. Finally, tabular form all proposed fusion better PCS

Language: Английский

Citations

28