{
  "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc",
  "docSlug": "cc8183ec02431b7a",
  "documentTitle": "2022 Air Street Capital The State of AI Report 2022",
  "authorId": "AirStreetCapital",
  "authorName": "Air Street Capital",
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  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "vc_research",
  "sourceTypeLabel": "VC research",
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  "orientation": "landscape",
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  "pageNumber": 44,
  "pageCount": 114,
  "prevPage": 43,
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  "slideType": "case_study",
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  "density": "overcrowded",
  "nDataPoints": 20,
  "notes": "The slide uses three bar charts (E, F, G) to demonstrate the efficacy of ML-recommended antibiotics compared to physician-prescribed ones.",
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  "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/44",
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    {
      "bbox": null,
      "kind": "callout",
      "text": "Both UTI (F) and wound infection (G) patients would suffer far fewer reinfections if they'd have been prescribed antibiotics according to the ML system.",
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      "subkind": null,
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      "kind": "chart",
      "text": "Chart E: % of UTIs which gained resistance following treatment by antibiotic type",
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        "h": 0.3,
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        "x": 0.69,
        "y": 0.65
      },
      "kind": "chart",
      "text": "Chart G: Predicted probability of gaining resistance following treatment for wound infections",
      "attrs": null,
      "subkind": "bar-vertical",
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      "componentId": "61c228d1-828c-4f35-bf85-66342f8d5a69",
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      "kind": "chart",
      "text": "Chart F: Predicted probability of gaining resistance following treatment for UTIs",
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      "kind": "list",
      "text": "By comparing the microbiome profiles of >200,000 patients with urinary tract or wound infections who were treated with known antibiotics before and after their infections, ML can be used to predict the risk of treatment-induced gain of resistance on a patient-specific level.\nIndeed, urinary tract infection (UTI) patients treated with antibiotics that the ML system would not have recommended resulted in significantly resistance (E). Both UTI (F) and wound infection (G) patients would suffer far fewer reinfections if they’d have been prescribed antibiotics according to the ML system.",
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      "text": "Probability of gaining resistance",
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      "kind": "paragraph",
      "text": "Resistance to antibacterial agents is common and often arises as a result of a different pathogen already present in a patient’s body. So how should doctors find the right antibiotic that cures the infection but doesn’t render the patient susceptible to a new infection?",
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      "kind": "title",
      "text": "Treating bacterial infections by data-driven personalised selection of antibacterial agents",
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      "beatName": "The Facts (What)",
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