Editing Physicians' Responses Using GPT-4 for Academic Research.

The integration of Artificial Intelligence (AI) into digital healthcare, particularly in the anonymisation and processing of health information, holds considerable potential. OBJECTIVES: To develop a methodology using Generative Pre-trained Transformer (GPT) models to preserve the essence of medical advice in doctors' responses, while editing them for use in scientific studies. METHODS: German and English responses from EXABO, a rare respiratory disease platform, were processed using iterative refinement and other prompt engineering techniques, with a focus on removing identifiable and irrelevant content. RESULTS: Of 40 responses tested, 31 were accurately modified according to the developed guidelines. Challenges included misclassification and incomplete removal, with incremental prompting proving more accurate than combined prompting. CONCLUSION: GPT-4 models show promise in medical response editing, but face challenges in accuracy and consistency. Precision in prompt engineering is essential in medical contexts to minimise bias and retain relevant information.

Translation of Ontological Concepts from English into German Using Commercial Translation Software and Expert Evaluation.

Medical ontologies are mostly available in English. This presents a language barrier that is a limitation in research and automated processing of patient data. The manual translation of ontologies is complex and time-consuming. However, there are commercial translation tools that have shown promising results in the field of medical terminology translation. The aim of this study is to translate selected terms of the Human Phenotype Ontology (HPO) from English into German using commercial translators. Six medical experts evaluated the translation candidates in an iterative process. The results show commercial translators, with DeepL in the lead, provide translations that are positively evaluated by experts. With a broader study scope and additional optimization techniques, commercial translators could support and facilitate the process of translating medical ontologies.

Initial User-Centred Design of an AI-Based Clinical Decision Support System for Primary Care.

A clinical decision support system based on different methods of artificial intelligence (AI) can support the diagnosis of patients with unclear diseases by providing tentative diagnoses as well as proposals for further steps. In a user-centred-design process, we aim to find out how general practitioners envision the user interface of an AI-based clinical decision support system for primary care. A first user-interface prototype was developed using the task model based on user requirements from preliminary work. Five general practitioners evaluated the prototype in two workshops. The discussion of the prototype resulted in categorized suggestions with key messages for further development of the AI-based clinical decision support system, such as the integration of intelligent parameter requests. The early inclusion of different user feedback facilitated the implementation of a user interface for a user-friendly decision support system.

Enhancing HIV Patient Support Through Telehealth: Exploring Design Solutions.

In recent years, telemedicine has advanced significantly, offering new possibilities for improving healthcare and patient outcomes. This paper presents a telemedicine app for HIV patients, developed using a human-centered design approach. Designed to meet the diverse and specific needs of Pre-Exposure Prophylaxis (PrEP) users and Late Presenters (LP), the app is part of the COMTRAC-HIV Project at the University Hospital Frankfurt. Through interviews with HIV experts and healthcare professionals, initial design solutions were derived. The paper explores the app's design process, core functionalities, and future directions, aiming to provide comprehensive support for individuals living with HIV.

Methods Used in the Development of Common Data Models for Health Data: Scoping Review.

BACKGROUND: Common data models (CDMs) are essential tools for data harmonization, which can lead to significant improvements in the health domain. CDMs unite data from disparate sources and ease collaborations across institutions, resulting in the generation of large standardized data repositories across different entities. An overview of existing CDMs and methods used to develop these data sets may assist in the development process of future models for the health domain, such as for decision support systems. OBJECTIVE: This scoping review investigates methods used in the development of CDMs for health data. We aim to provide a broad overview of approaches and guidelines that are used in the development of CDMs (ie, common data elements or common data sets) for different health domains on an international level. METHODS: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We conducted the literature search in prominent databases, namely, PubMed, Web of Science, Science Direct, and Scopus, starting from January 2000 until March 2022. We identified and screened 1309 articles. The included articles were evaluated based on the type of adopted method, which was used in the conception, users' needs collection, implementation, and evaluation phases of CDMs, and whether stakeholders (such as medical experts, patients' representatives, and IT staff) were involved during the process. Moreover, the models were grouped into iterative or linear types based on the imperativeness of the stages during development. RESULTS: We finally identified 59 articles that fit our eligibility criteria. Of these articles, 45 specifically focused on common medical conditions, 10 focused on rare medical conditions, and the remaining 4 focused on both conditions. The development process usually involved stakeholders but in different ways (eg, working group meetings, Delphi approaches, interviews, and questionnaires). Twenty-two models followed an iterative process. CONCLUSIONS: The included articles showed the diversity of methods used to develop a CDM in different domains of health. We highlight the need for more specialized CDM development methods in the health domain and propose a suggestive development process that might ease the development of CDMs in the health domain in the future.

Interviews with HIV Experts for Development of a Mobile Health Application in HIV Care-A Qualitative Study.

The Communication and Tracing App HIV (COMTRAC-HIV) project aims to develop a mobile health application for integrated care of HIV patients due to the low availability of those apps in Germany. This study addressed organizational conditions and necessary app functionalities, especially for the care of late diagnosed individuals (late presenters) and those using pre-exposure prophylaxis. We followed a human-centered design approach and interviewed HIV experts in Germany to describe the context of use of the app. The interviews were paraphrased and analyzed with a qualitative content analysis. To define the context of use, user group profiles were defined and tasks derived, which will represent the functionalities of the app. A total of eight experts were included in the study. The results show that the app should include a symptom diary for entering symptoms, side effects, and their intensity. It offers chat/video call functionality for communication with an HIV expert, appointment organization, and sharing findings. The app should also provide medication overview and reminders for medications and appointments. This qualitative study is a first step towards the development of an app for HIV individuals in Germany. Further research includes involving patients in the initial app design and test design usability.

Machine translation of standardised medical terminology using natural language processing: A scoping review.

Standardised medical terminologies are used to ensure accurate and consistent communication of information and to facilitate data exchange. Currently, many terminologies are only available in English, which hinders international research and automated processing of medical data. Natural language processing (NLP) and Machine Translation (MT) methods can be used to automatically translate these terms. This scoping review examines the research on automated translation of standardised medical terminology. A search was performed in PubMed and Web of Science and results were screened for eligibility by title and abstract as well as full text screening. In addition to bibliographic data, the following data items were considered: 'terminology considered', 'terms considered', 'source language', 'target language', 'translation type', 'NLP technique', 'NLP system', 'machine translation system', 'data source' and 'translation quality'. The results showed that the most frequently translated terminology is SNOMED CT (39.1%), followed by MeSH (13%), ICD (13%) and UMLS (8.7%). The most common source language is English (55.9%), and the most common target language is German (41.2%). Translation methods are often based on Statistical Machine Translation (SMT) (41.7%) and, more recently, Neural Machine Translation (NMT) (30.6%), but can also be combined with various MT methods. Commercial translators such as Google Translate (36.4%) and automatic validation methods such as BLEU (22.2%) are frequently used tools for translation and subsequent validation.

Diagnosis Support for Rare Diseases Using Phenotypic Profiles.

The common occurrence of characteristic symptoms can be used to infer diagnoses. The aim of this study is to show how syndrome similarity analysis using given phenotypic profiles can help in the diagnosis of rare diseases. HPO was used to map syndromes and phenotypic profiles. The system architecture described is planned to be implemented in a clinical decision support system for unclear diseases.

Conception, Development and Validation of Classification Methods for Coding Support of Rare Diseases Using Artificial Intelligence.

Automated coding of diseases can support hospitals in the billing of inpatient cases with the health insurance funds. This paper describes the implementation and evaluation of classification methods for two selected Rare Diseases. Different classifiers of an off-the-shelf system and an own application are applied in a supervised learning process and comparatively examined for their suitability and reliability. Using Natural Language Processing and Machine Learning, disease entities are recognized from unstructured historical patient records and new billing cases are coded automatically. The results of the performed classifications show that even with small datasets (≤ 200), high correctness (F1 score ∼0.8) can be achieved in predicting new cases.

Challenges of introducing new biomarker products for neuropsychiatric disorders into the market.

There are many challenges associated with the discovery and development of serum-based biomarkers for psychiatric disorders such as schizophrenia. Here, we review these challenges from the point of view of psychiatrists, general practitioners, the regulatory agencies, and biomarker scientists. There is a general opinion in psychiatric medicine that improvements over the current subjective tests are essential. Despite this, there is a reluctance to accept that peripheral molecules can do the job any better. In addition, psychiatrists find it difficult to accept that peripheral molecules, such as those found in blood, can reflect what is happening in the brain. However, the regulatory health authorities now consider biomarkers as important for the future of drug development and have called for efforts to modernize methods, tools, and techniques for the purpose of developing more efficient and safer drugs. We also describe here the development of the first ever molecular blood test for schizophrenia, and its reception in the market place, as a case in point.

Kraepelin's 'lost biological psychiatry'? Autointoxication, organotherapy and surgery for dementia praecox.

Kraepelin believed that a chronic metabolic autointoxication, perhaps arising from the sex glands, eventually caused chemical damage to the brain and led to the symptoms of dementia praecox. The evolution of Kraepelin's autointoxication theory of dementia praecox is traced through the 5th to 8th (1895 to 1913) editions of his textbook, Psychiatrie. The historical context of autointoxication theory in medicine is explored in depth to enable the understanding of Kraepelin's aetiological assumption and his application of a rational treatment based on it--organotherapy. A brief account of the North American reception of Kraepelin's concept of dementia praecox, its autotoxic basis, and the preferred American style of rational treatment--surgery--concludes the discussion.

Infectious insanities, surgical solutions: Bayard Taylor Holmes, dementia praecox and laboratory science in early 20th-century America. Part 1.

After his 17-year-old son suddenly developed a chronic psychotic illness in 1905, Bayard Taylor Holmes (1852-1924), a Chicago physician and surgeon with no psychiatric training, conducted both library and laboratory research on dementia praecox, as described in Part 1 of this two-part study. By late 1915 he believed he had found support for a focal infection theory of its aetiology--an ergot-like toxaemia caused by faecal stasis in the caecum. Holmes was also the editor of what is believed to be the first medical journal named after a psychiatric disorder: Dementia Praecox Studies. Part 2 will describe Holmes' adoption of a rational therapy (using it first on his son, who died), and his founding of a Psychiatric Research Laboratory.

Infectious insanities, surgical solutions: Bayard Taylor Holmes, dementia praecox and laboratory science in early 20th-century America. Part 2.

Part I of this article on Bayard Taylor Holmes (1852-1924), a Chicago physician and surgeon, detailed his laboratory research on dementia praecox and his presumed discovery in 1915 of evidence in support of a focal infection theory of its aetiology. In May 1916 he began to experiment with a rational therapy based on this theory: abdominal surgery and daily irrigations of the colon. He operated first on his own son, who had been afflicted with dementia praecox since 1905, but he died four days later. Part II deals with Holmes' continued surgical treatment of dementia praecox patients and the outcomes of the operations. It also describes how Holmes set up the short-lived Psychiatric Research Laboratory of the Psychopathic Hospital at Cook County Hospital in 1917, and discusses the dismissal of a key research colleague, H. M. Jones, whose experimental results directly contradicted those of Holmes.

The blood of the insane.

The history of serological investigations of the blood of the insane is traced from the initial such study in 1854 by a solitary Scottish asylum physician, who counted the blood cells of his lunatic patients under a weak microscope, to the January 2005 announcement by an international team of geneticists of the development of a genomic blood test that can differentially diagnose schizophrenia and bipolar disorder. The story of the first claim of the development of a blood test for madness in 1912--the Abderhalden defensive ferments reaction test--is related in detail. Studies of the blood of the insane have followed four general methodological paradigms: the corpuscular richness paradigm (1854); the metabolic paradigm (c. 1895); the immunoserodiagnostic paradigm (1906); and the medical genomics paradigm (2005).

Historical review: Autointoxication and focal infection theories of dementia praecox.

The popularity of theories of autointoxication and focal infection in general medicine and dentistry in the late nineteenth and early twentieth centuries led Emil Kraepelin and others to speculate that dementia praecox was caused by a poisoning of the brain from toxins produced in other parts of the body, notably the sex glands, the intestines and the mouth. Emil Kraepelin's commitment to the autointoxication theory is ignored in the literature on the history of psychiatry due to the focus of historians and clinicians on the major contributions of Kraepelin's methods of clinical psychopathology. Besides heredity, autointoxication and focal infection were the other most dominant theories of the organic aetiology of dementia praecox in the first three decades of its existence as a nosological entity in psychiatry. Rational treatments for dementia praecox that followed logically from these aetio-logical theories were colonic irrigations and major abdominal surgeries such as appendicostomies, colectomies and the removal of presumably infected ovaries, testes and other organs associated with reproduction. Autointoxication and focal infection theories disappeared from psychiatry by the mid-1930s.